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An efficient thermal regulation strategy is of great significance in ensuring the safe operation of electric vehicles (EVs). However, the commonly used thermal management systems suffer from the problem of not being able to accurately and uniformly control the temperature distribution of lithium-ion batteries (LIBs), which poses great risks to the thermal safety control of batteries. In this paper, an advanced thermal management system for LIBs based on thermoelectric cooler (TEC) was designed to overcome the above problems. First, a temperature regulation mechanism model for LIB was constructed. Then, a novel temperature distribution modeling method for TEC was developed by using spectral method. This modeling process took into account the unsteady heat transfer characteristics, which can achieve high modeling accuracy. Next, a state space model of temperature control was constructed by combining a differential model of LIB with the proposed cooler model. On this basis, a temperature control strategy for LIB using nonlinear model predictive control (NMPC) method was proposed to optimize the cooling process because of its superior processing ability to constraints and nonlinearity. A various of experiments and verifications demonstrated that the presented thermal regulation strategy was effective and feasible. • An advanced thermal regulation system for lithium-ion battery based on thermoelectric cooler is designed. • A novel temperature distribution modeling method for TEC is proposed. • A regulation strategy is developed to control the uniformity of temperature distribution. • The paper provides a good guidance for the application and further study of advanced thermal management system in electric vehicle. [ABSTRACT FROM AUTHOR]

Battery thermal management systems play a crucial role in maintaining the optimal temperature range of lithium-ion batteries, ensuring their efficient operation and prolonged lifespan. Hybrid cooling (HC) techniques, which involve the utilization of hydrophilic fibers, have shown promise in enhancing the thermal performance of these systems. The present study employs an experimental approach to investigate the impact of selected fiber materials on the thermal performance of a hybrid cooling system designed for battery thermal management. Parameters such as the time required for complete wetting of the fiber and the coolant absorption capacity of the fiber are considered during the fiber selection process. Findings reveal that the tissue paper fiber displayed superior water transfer capabilities, achieving a minimum wetting time of 46 s for a 75 mm fiber length. This characteristic positions it as a promising candidate for the subsequent hybrid cooling experiment. Noteworthy is its water holding capacity per unit volume, which surpassed that of cotton fiber by 40%. HC demonstrates the superior temperature uniformity and average battery pack temperature compared to simple air cooling (SAC) under varying velocities, emphasizing the pivotal role of HC. A remarkable improvement of 41.86% in temperature uniformity is obtained in HC over SAC. [Display omitted] • Hydrophilic fiber-based hybrid cooling significantly enhances battery thermal management. • Tissue paper-based fibers exhibit superior water transport and 40% higher water holding capacity, influencing efficient cooling. • Hybrid cooling's thermal performance under various velocities underscores its adaptability to different operational conditions. • Hybrid cooling surpasses simple air cooling, achieving a notable 41.86% improvement in temperature uniformity. • Cumulative quantity of water evaporated indicates a stable and reliable evaporation rate, which leads to the sustained performance of the hybrid cooling system. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have emerged as an indispensable component in the development of green transportation such as electric vehicles (EVs) and large-scale applications of renewable energy such as smart grid energy storage systems. The detection, judgment, and prediction of various battery states such as State of Charge (SOC) and State of Health (SOH) in the battery management system (BMS) play a critical role in guaranteeing the LIBs work under a safe and reliable situation. After decades of intensive investigation, accompanied by the fast development of big-data techniques (BDT) and artificial intelligence (AI) algorithms, the framework of BMS is moving from the traditional onboard system towards the functional integrated scheme. This paper starts with a comprehensive overview of the underlying degradation mechanism of the battery and algorithm distinction and judgment of the battery states in BMS. Subsequently, the paper has systematically reviewed and discussed the most commonly used approaches and state-of-the-art algorithms for battery state estimation in BMS from the perspective of three different BMS configurations: onboard-BMS, cloud-BMS, and functional integrated-BMS. This review expects to stimulate more new insights and encourage more efforts to develop advanced BMS for intelligent and innovative battery control. • Various battery degradation phenomena and their model development are explained. • Three distinct BMS structures—onboard-BMS, cloud-BMS, and Fi-BMS—are explained. • The latest advancements in battery state estimation algorithms are reviewed. • Emerging technical innovation prospects are highlighted in four areas. [ABSTRACT FROM AUTHOR]

As an important performance indicator of battery management systems, lithium-ion battery state of health (SOH) information is crucial to ensure battery safety and extend battery lifetime. Aiming at the problems of feature extraction difficulty, low accuracy of long-term prediction, and poor parallel computing capability of general data-driven methods, this paper proposes a SOH estimation method for lithium-ion batteries based on incremental capacity analysis (ICA) and Transformer. First, the original incremental capacity (IC) curve of the battery is extracted based on the ICA method, and the original IC curve is processed using the dual filtering method of moving average smoothing filter plus Gaussian smoothing filter, which in turn extracts the peak features of the curve. Then, the Transformer network model based on the multi-head attention mechanism is built. Finally, the extracted peak features of the IC curve are used as model inputs, and the Transformer model is utilized to realize the SOH estimation of lithium-ion batteries. In this paper, experiments based on different input features, prediction starting points, and ambient temperatures are conducted using experimental data of lithium-ion batteries from three sources and analyzed in comparison with commonly used machine learning methods. The experimental results show that the SOH estimation method proposed in this paper has higher long-term prediction accuracy and better temperature adaptability than commonly used machine learning methods. • The dual filtering method of MASF plus GSF is used to smooth the IC curve. • Transformer model is proposed to estimate the SOH of lithium-ion batteries. • The peak features of the IC curves are used as model inputs. • The advantages of the proposed method were verified by several experiments. [ABSTRACT FROM AUTHOR]

Recently, lithium-ion battery storage system has become increasingly popular due to its enormous potential and capacity in renewable energy integration and e-mobility applications leading to achieve global carbon neutrality by 2050. However, the performance of lithium-ion batteries has been affected due to the material degradation as well as varying environmental conditions. Thus, this paper presents analytical evaluation, aiming to investigate the advancement on the state-of-the-art of lithium-ion battery material potential that has been mapped from the hot papers. Accordingly, 73 hot papers (top 0.1% highly cited) have been found using the keyword search on lithium-ion batteries from the Web of Science database published in last 2 years between 2019 and 2021. These hot papers are evaluated using various key factors, including state-of-the-art of lithium-ion battery materials followed by analytical evaluation. The analytical assessment comprises numerous crucial aspects including keyword co-occurrence network analysis, distribution of articles over countries, journals, research types, and subject categories. Moreover, the review explores the concerns, issues and challenges to identify the existing research gaps. Finally, the review uncovers promising research opportunities and directions that will support the researchers towards energy efficiency, environmental sustainability of advanced lithium-ion battery management system. [Display omitted] • Lithium-ion battery material potential is analytically evaluated towards future research directions. • Hot paper earns a significant number of citations which is top 0.1 percent on the field. • The analytical evaluation is comprehensively done based on keyword co-occurrence network analysis. • Existing research gaps, issues and challenges on LIB materials are highlighted for future development. • This study will assist researchers and industries to develop an energy efficient advanced LIB. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing demand for electrical vehicles, which are more energy efficient and environmentally friendly than their traditional counterpart. One of the crucial aspects of electrical systems is energy storage. A promising option for energy storage is Lithium-ion batteries (LIBs) due to their high energy density, longer life cycles, and faster charging when compared to other batteries. The disadvantage of employing LIBs is that they are more difficult to maintain because they are more prone to failure, and they are greatly dependent on the system's thermal behavior, which has led to an emphasis in research on battery thermal management systems (BTMSs). The BTMS aims to address this issue by limiting the temperature fluctuation of the battery cells to maintain the average temperature within recommended limits throughout the charging and discharging processes. There has been a continuous interest in nanofluids and phase change materials (PCMs) because of their unique properties and their potential thermal application as a cooling medium for high-density batteries. Recent studies have aimed to use different active and passive methods with varying materials and cooling system geometries to achieve the highest possible thermal performance. The current review aims to outline the recent studies on thermal management systems for LIBs. Three main sections have been presented in this paper. The first section reviewed studies on nanofluids in different BTMSs, the other one highlighted techniques adopted to overcome the low thermal conductivity drawback in PCMs for BTMs, and the last one focuses on the effects of combining both cooling approaches. We believe that the present review paper provides useful information that could be a significant step toward developing high-performance BTMS. • A summary of the most recent research on BTMS of LIBs. • Nanofluids and PCM-based BTMS attract significant interest in LIBs. • PCM with carbon additives enhance LIB pack temperature uniformity. • Incorporation of fins has demonstrated effectiveness in improving BTMS performance. • Metal foams with PCMs enhance BTMS performance via improved thermal conductivity. [ABSTRACT FROM AUTHOR]

Lithium-ion cells (LIC) are emerging as the critical energy source in space applications for spacecraft. The analysis of the failure of small satellites reveals that 41.3% have either suffered partial or total failures in the space environment around Earth, resulting in the accumulation of debris. The defects in batteries caused more failures than thrusters. The present paper details a framework for ensuring the durability of LIC systems in space applications. It is discussed in three verticals viz. a) Durability through Design' that gives prominence to best practices in cell chemistry selection, processing and, design through parametric modelling; b) 'Durability through quality control and testing by the manufacturer and by identifying relevant reliability models; c)'On-orbit management' through the State of Health (SOH) assessment by Random Forest algorithm and a well-equipped intelligent Battery Management System (BMS). Lacuna in standards for cell development process is brought out. Importance of quantitative approach for design, reliability and asset management during space application is highlighted. Use of agile, contemporary techniques of artificial intelligence in this quantitative approach is also discussed. The framework tries to bring out a systematized protocol to be followed during the development and operational life cycle of LIC for ensuring highly reliable systems for space application. [ABSTRACT FROM AUTHOR]

• This research paper deals with design and development of PV emulator for testing of MPPT algorithm and Battery Charger controller using GA optimized PI controlled DC-DC Buck converter. • Genetic Algorithm has been used to optimize the parameter of the PI controller to emulate the exact behavior of the reference PV model. • The proposed GA optimized PI controlled DC-DC Buck converter based PV emulator provides the successful operation in three region i.e., constant voltage region, variable voltage and current region and constant current region. • The proposed PV emulator has been tested with perturb and observe maximum power point tracking algorithm and constant voltage battery charging control. • The PV emulator was also tested with MPPT algorithm and solar MPPT battery charging control algorithm and it has maximum power point extraction ratio of 97.45% and charging efficiency of 93.9%. • The proposed PV emulator has been successfully realized in real time with low cost microcontroller i.e., control logic for the PV emulator was implemented using Arduino ATmega board. • The developed model was tested and IV and PV characteristics are compared with PV reference model and observed that PV emulator has an emulation efficiency of 98.2%. In this paper, PV emulator is developed using double loop PI controlled DC-DC buck converter. Ambient environmental conditions such as irradiance, temperature and wind speed are considered for creating PV reference model in the PV emulator. The simulation model of the proposed PV emulator is created and tested using MATLAB Simulink package. To validate the effectiveness of the proposed PV emulator, it is tested with different operating conditions such as varying irradiance, temperature and wind speed. The current–voltage and power-voltage characteristic of the PV emulator is compared with PV reference model. The suitability of the PV emulator is tested with MPPT algorithm and battery charging controller in addition to number of objectives. Real time testing of the proposed PV emulator is done experimentally and also corresponding results are discussed. The proposed Solar PV emulator has the following properties: (1) higher bandwidth DC-DC buck converter, (2) extremely reliable performance with lower response time, and (3) lower output voltage and current ripple. [ABSTRACT FROM AUTHOR]

The promotion of electric vehicles can help reduce carbon emissions and mitigate the greenhouse effect. Lithium-ion batteries are one of the main power sources for electric vehicles, whose state of charge is a key parameter of the battery management system to ensure the safe operation of electric vehicles. The complex structure of the battery makes the state of charge unable to be directly measured, so this paper establishes a mathematical model to estimate the state of charge based on four parameters (voltage, current, wavelength and light intensity), aiming to provide a high-precision model for state of charge measurement and establish a mathematical model which can help guide actual production. These four parameters can be measured by voltmeters, ammeters, and optical fibers. In this paper, the least squares method is used to perform global linear regression and global quadratic regression of experimental data, and a data pre-treatment is proposed to improve the behavior of the linear equation system. Also, the ridge regression method is used to achieve an effective solution of the ill-conditioned linear equation system. Then, the piecewise linear regression is introduced to optimize the global regression model, and effectively reduces the regression error by capturing the local features of the data. In order to meet the actual engineering requirements, constraints satisfying segmental continuity are added in the piecewise linear regression model. Finally, the regression results are compared with the results obtained by the Machine Learning method. Besides, the proposed methods outperform the Machine Learning method with a sufficient number of segments. The state of charge estimation model established in this paper has high approximation accuracy, strong interpretability, low operational complexity, and has certain engineering application value. • An efficient model is built to estimate the state of charge of lithium-ion battery. • The global linear and quadratic regressions are performed. • A data pre-treatment is proposed to improve the linear equation system. • The ridge regression method is used for the ill-conditioned linear equation system. • The piecewise linear regression is used to optimize the global regression model. • To meet the actual requirements, constraints of segmental continuity are added. • The proposed methods outperform the Machine Learning method. [ABSTRACT FROM AUTHOR]

Battery degradation, caused by multiple coupled degradation mechanisms, severely affects the safety and sustainability of a battery management system (BMS). The battery state of health (SOH) is a commonly-adopted metric to evaluate a battery's degradation condition, which should be carefully modeled to facilitate the safety and reliability of a BMS. Recently, owing to the rapid progress of data science-related techniques, data-driven models for battery SOH estimation have attracted great attentions from both academia and industry communities. This paper aims to provide the scientists and engineers with a general overview of data-driven battery SOH estimation technology for BMSs. State-of-the-art models published during 2018–2022 are reviewed with care, including a) feature extraction and selection methods; b) benchmarks, variants and extensions of data-driven SOH estimation models; and c) publicly-available battery SOH datasets. Afterwards, experiments are conducted and analyzed on the Toyota & Stanford-MIT battery SOH datasets for benchmark study. Finally, existing challenges and feature trends are summarized. [ABSTRACT FROM AUTHOR]

A rapid expansion in the technology of solar PV energy in recent years has paved the way for PV market to grow resulting in a cost reduction in material. Hence, technological improvements are eminent to maintain the stable working of the system. In order to create a system many techniques and modules are used like, MPPT algorithms to track the power which increases real-time efficiency, and a battery management system to efficiently manage the stored energy from the battery. This paper aims to design a forecasting model to predict the weather and load of the standalone system to prepare the battery for future use. The forecasting is done using Machine learning and Deep learning tools like RNN, LSTM, and GRU and the results are fed into the system in MATLAB Simulink simulation platform. [ABSTRACT FROM AUTHOR]

Accurate state-of-charge (SOC) estimation is crucial for ensuring the safe and reliable operation of battery management systems (BMS). Among the various algorithms used for SOC estimation in real-vehicle BMS, the extended Kalman filter (EKF) algorithm holds significance due to its adherence to optimal estimation principles and its property of insensitivity to initial values. By studying the relationship between error sources and SOC estimation errors, it becomes possible to develop targeted measures for enhancing the accuracy of SOC estimation based on the EKF. From a probabilistic perspective, this paper derives theoretical equations that establish the connection between SOC estimation errors and various error sources, including measured voltage, measured current, open circuit voltage curve, capacity, ohmic internal resistance, and polarization resistance. Furthermore, the paper analyzes the relationship among multiple error sources in generating SOC estimation errors. Building upon the outcomes of this theoretical analysis, a joint SOC estimation method that combines the EKF with Ampere-hour counting (AHC) is employed to identify errors. In scenarios where simultaneous faults occur in the current and voltage sensors, they are identified based on the slope and Euclidean distance of the two SOC trajectories, respectively. Subsequently, sensor faults correction and SOC compensation are implemented by leveraging simplified equations involving capacity and SOC increments and measured voltage and SOC estimation errors. In addition to addressing sensor faults, the paper also considers battery model parameter errors. By incorporating customized current pulse profile and theoretical equations relating to error sources and SOC estimation errors, a comprehensive error estimation of model parameters is achieved, capable of handling single and multiple errors. The derived simplification bridges the gap between error sources and SOC estimation errors, offering a novel approach for parameter sensitivity analysis and a theoretical foundation for quantifying these errors. The experimental results demonstrate the effectiveness and rapidity of the proposed method for identifying and correcting sensor faults and model parameter errors. • Derive error equations of signal error sources and SOC errors. • Analysis the relationship of multiple error sources in generating SOC errors. • Achieve the identification-correction of current and voltage sensor faults. • A model parameter errors estimation method is demonstrated. • A new method for parameter sensitivity analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time state of health (SOH) estimation model based on a deep learning (DL) framework. The proposed model is a combination of two different architectures; a one-dimensional convolutional neural network (1D-CNN) and a bidirectional gated recurrent unit (BiGRU). The hybrid CNN-BiGRU uses the 1D CNN layers to extract pertinent features from input data and then relies on the BiGRU layers for sequence learning in both directions. To account for all SOH indicators, the proposed approach uses the current, voltage, and temperature measurements, which are readily obtainable from the electric vehicle's battery management system (BMS). This prevents the complex and time-consuming feature extraction used in most related papers. Since the hyperparameters have a significant impact on the performance of neural network models, a Bayesian Optimization (BO) technique based on the Gaussian Process (GP) was considered to tune the CNN-BiGRU model hyperparameters. Accordingly, the objective function was able to converge to a low Mean Squared Error (MSE) of 1.2 × 10 − 5 in just 19 iterations. Afterward, to verify the accuracy of the optimized model, a Lithium-ion battery dataset with several discharge profiles provided by the National Aeronautics and Space Administration (NASA) was used. The obtained results demonstrated the accuracy and robustness of the proposed method compared to other commonly used models. The CNN-BiGRU model yielded a Mean Absolute Error (MAE) of 2.080% and a root-mean-square error (RMSE) of 2.516% in the case of the battery set #C, referring to a set with 70 cycles already used at 24 °C. Additionally, the End of life (EOL) indicator error of zero cycles for the same data. [ABSTRACT FROM AUTHOR]

The performance degradation of lithium-ion batteries (LiB) at low temperatures, as well as variability among batteries after battery grouping, limit the application range of electric vehicles (EVs). A low-temperature preheating method for power battery packs with an integrated dissipative balancing function is proposed in this research. The system builds its topology structure and sets the placement of heating units in the battery pack, as well as using heating units as preheating elements and balancing resistors. Furthermore, this paper presents an online balancing control technique to completely utilize the minimal capacity single battery module and formulates the balancing control threshold for different SOC new states. The results showed that the temperature rise and temperature difference caused by the battery pack balancing process are within the range of 7 °C under the full operation conditions of balancing resistance at an ambient temperature of 20 °C. Under a −20 °C ambient temperature, the preheating system can raise the average temperature of the battery pack to 16 °C within 40 min and keep the temperature inside the battery pack above 0 °C for the following 4 h. This paper provides a new idea for coupling the research of efficient balancing technology and low-temperature preheating technology for power battery packs. • A novel preheating system with a dissipative balancing function was dnsproposed. • Heating plates acted as preheating elements and balancing resistors. • A target heating temperature calculation method was developed. • The new system's equalization current is 10 times the traditional one's. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries are the workhorse of energy storage systems in electric vehicles (EVs) due to their high energy density and desirable characteristics. To obtain an optimized and safe operation, a battery management system (BMS) should be implemented to provide the main safety features based on the estimation of different battery states, which entails an accurate, fast, and adaptive mathematical model. In this paper, a data-driven linear model using dynamic mode decomposition is proposed. The proposed modeling procedure bridges the gap between abstracted models (e.g., circuit-based models) and empirical models (e.g., data-driven models) of Li-ion batteries. Unlike the abstracted models, the proposed model does not impose any assumptions on what the model should be, and also considers the battery as a black box similar to the empirical techniques, and yet gives an interpretable linear model in state space form. In order to generate this model, only one discharge cycle that contains high dynamic content is required, and no other specialized tests are required. Two models are proposed for the Li-ion battery, namely, a model-based estimator based on Kalman filter for BMS purposes and a piecewise model for offline simulation. The results are verified experimentally via a lab scale prototype. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications. • Innovating an Adaptive learning kernel function for union Poly to construct a joint kernel function. • Using joint kernel function for extreme learning machine. • Optimizing the dung beetle optimization algorithm under multiple constraints. • Constructed LOWDBO-PAKELM model for SOH estimation with a high adaptation range. [ABSTRACT FROM AUTHOR]

Cell inconsistency is an inevitable challenge that large-scale battery packs must confront, directly impacting their safety and performance. Accurate cell inconsistency evaluation is essential for efficient state management and second-life utilisation. This paper presents an inconsistency evaluation method based on cell voltage only and simultaneously identifies abnormal cells that perform relatively poorly compared to others. Firstly, the discrete wavelet transform (DWT) is utilised to generate features representing each cell's state of health (SOH). Then, the effectiveness of the DWT-based features in quantitatively representing SOH is validated using the NASA dataset. Leveraging charging data extracted from real-world electric vehicles (EVs), the DWT-based feature is further applied to detect the abnormal cells of the battery system. Additionally, a model-based approach, focusing on the internal resistance (IR) difference, is introduced for comparison with the proposed DWT-based method. The results show that the proposed method accurately identifies the abnormal cells of real-world EVs within a mere 10 s, markedly outperforming the traditional model-based approach, which consumes 15.6 h. Finally, a robust indicator is proposed to characterise the evolution trend of cell inconsistency in complex operating conditions. • Identifying abnormal cells in electric vehicles using only cell voltage data. • Precisely retrace the evolution path and quantify the relative differences among cells. • Reliably assess the inconsistency of in-service vehicles under changeable temperatures. [ABSTRACT FROM AUTHOR]

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages for feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable capacity estimation approach utilising short-duration random charging voltages during the constant-current charging stage and leveraging convolutional neural networks (CNNs). Based on the user-friendly " V s t a r t − t e n d " strategy, two health features including charging voltage and its increment are firstly extracted from random charging segments. Secondly, a feature evolution pattern analysis over the battery's lifespan is proposed to divide the charging voltage range for robust model development. An optimal combination of both the sampling interval and data length is determined for the feature extraction. Then, a two-dimensional CNN model is developed to effectively learn ageing-related knowledge from various random charging segments in a specific charging voltage range. The effectiveness of the proposed approach is ultimately verified using two distinct types of batteries across three operational temperatures. The results demonstrate that the proposed approach show much potential as a promising capacity estimation technique utilising a 600 s random charging segment sampled at a 20 s interval. • Adaptable battery capacity estimation is achieved using random short-duration charging voltages. • Feature evolution analysis is proposed to guide feature extraction and model development. • 2D-CNN model is developed for diverse random inputs and accurate capacity estimation. • The model's effectiveness is validated on two types of batteries at three working conditions. [ABSTRACT FROM AUTHOR]

The accurate estimation of battery state of charge (SOC) and state of energy (SOE) are the key technologies in researching battery management systems. Due to the uncertainty of prior noise in practical engineering, traditional cubature Kalman filters (CKF) have lower filtering accuracy. This paper proposes an improved limited memory-Sage Husa-cubature Kalman (LM–SH–CKF) algorithm to estimate the SOC and SOE of lithium-ion batteries. During the filtering process, the Sage-Husa estimator is introduced to estimate the noise, correcting the statistical characteristics of the noise in real-time, thereby improving the estimation accuracy. Meanwhile, establish a filtering divergence criterion to determine whether the filtering is abnormal and ensure the real-time performance of the algorithm. To improve the accuracy of parameter identification, the Hysteresis Effect-Dual Polarization (HE-DP) model is established and the limited memory recursive least square (LMRLS) algorithm is proposed. The results show that the LM–SH–CKF has the smallest mean absolute error (MAE) and root mean square error (RMSE). Under the dynamic stress test (DST) at 15 °C, the estimated MAE and RMSE of SOC and SOE are both less than 1 %. The LM–SH–CKF has high accuracy and robustness, and short calculation time, providing a reference for battery status monitoring. • The HE-DP model considers the internal hysteresis effect of the battery to characterize its characteristics. • The LMRLS only uses data within a limited memory cycle to update the system and obtain real-time battery parameters. • The Sage-Husa estimator estimates and corrects noise, improving the accuracy and robustness of SOC and SOE co-estimation. [ABSTRACT FROM AUTHOR]

State of Health (SOH) is a crucial metric for battery management systems, and accurate estimation of battery SOH is essential for the underlying management and maintenance of batteries. Traditional data-driven approaches lack in-depth analysis of health features and fail to exploit the deep-level health information among these features. Therefore, this paper extracts six health features from both measurement and calculation perspectives based on actual measurable charging curve data, and conducts aging analysis of health features from a mechanistic perspective and Spearman correlation analysis from a quantitative perspective. Additionally, a strategy combining deep learning with ensemble learning is proposed. This strategy utilizes one-dimensional convolutional neural networks to deeply integrate multiple original features, extracting fusion features with deep-level health information. Subsequently, XGBoost is employed to learn the fusion features for estimating battery SOH. Finally, the effectiveness and superiority of the proposed method are verified on battery aging datasets from the University of Maryland and our laboratory. Experimental results demonstrate that the RMSE and MAE of the proposed method are both below 1 % and outperform other methods. • A novel multi-feature deep fusion and XGBoost method is proposed for SOH estimation. • Health features are analyzed in depth from a mechanistic perspective. • The limitations of the Pearson correlation analysis are analyzed and identified. • Fusion features with deep health information are extracted and validated. • The proposed method is validated on both public and laboratory datasets. [ABSTRACT FROM AUTHOR]

Physics-based lithium-ion battery model is rarely used in real-time applications due to its complexity, restricting the development of next-generation refined battery management systems. Fidelity and computation efficiency are still challenging to coordinate for existing Model Order Reduction (MOR) methods, especially in solving either electrochemical reaction rate distribution or coupled multi-physics fields. This paper proposes a novel reduced-order electrochemical model called the Triple Region Transmission-Line Model (TRTLM), aiming at a win-win situation for both fidelity and real-time applicability. The TRTLM stands on a simple triple region transmission-line circuit topology, concisely depicting the inhomogeneous distribution of the electric field. Current and potential distribution are obtained considering the effect of the reaction distribution and concentration field. Two different linearized MIMO state-space models are then established to solve liquid and solid phase concentration fields continuously based on Padé Approximation techniques, achieving less distortion with limited discretization of the electric field. Convenient parameterized governing equations and extreme computation efficiency are both realized. Coupling of electric and concentration field is implemented by solving them alternatively and iteratively. A comparison study of the TRTLM with a Full-Order Model (FOM) and other representative MOR is conducted. The result shows less than 10 mV maximum distortion is reached by the TRTLM, even in extreme conditions, mainly through increasing the fidelity of reaction overpotential and liquid phase polarization. Moreover, hardware validation of the TRTLM is executed under different computation periods (down to 0.01s) based on the dSPACE platform, intuitively proving the real-time applicability of the model. [Display omitted] • A reduced order electrochemical model is proposed with high fidelity and efficiency. • A new topology is constructed to characterize electrical inhomogeneity concisely. • Compared with other reduced order methods in fidelity and computation speed. • A hardware validation is executed under multiple computation periods. [ABSTRACT FROM AUTHOR]

• Effects of plate structure and channel on temperature distributions are assessed. • Applying sidewall cooling decreases the battery pack's average temperature by 4.48 ℃. • The temperature difference is within 3.84 ℃ with serpentine/parallel flow channels at 3C discharge rate. • The safe operating ranges of the battery pack are determined. Refrigerant direct cooling is currently being considered as an efficient thermal management technology in power battery systems. In this paper, four types of liquid cooling plates for power battery modules are designed and the computational model is constructed. With the model being validated, it is applied to analyze the effects of the cooling plate structure and cooling channel on the cooling and heat dissipation performances. The results reveal that for the conventional cooling method, i.e., when the cooling plate is placed on the bottom of the battery pack, a significant temperature gradient in the vertical direction is observed. On the contrary, adopting a serpentine cooling plate structure in the main wall or parallel cooling channels in the narrow side wall could significantly reduce the temperature difference of the battery pack, which can keep the temperature difference within 5 °C even under extreme conditions. Further, a detailed analysis of the heat dissipation performance based on the optimal cooling plate structure is performed to determine the safe operating range of the battery pack. It is shown that as the humidity is less than 0.73 and the evaporation temperature is below 15.43 °C, the battery pack can operate safely. However, beyond this condition range the heat dissipation characteristics of the battery pack cannot satisfy the operating requirements. This work sheds light upon the potential of refrigerant direct cooling strategy in power battery thermal management systems by properly arranging the cooling plate. [ABSTRACT FROM AUTHOR]

The state of energy (SOE) is one of the most critical state indicators in battery management systems. However, its nonlinear characteristics present significant challenges in obtaining accurate SOE. Especially when applying different magnetic field strengths to perform battery charging and discharging tests, the change in battery energy becomes more complex due to the influence of the magnetization effect. In this paper, a deep learning network, combining an improved Informer and long short-term memory network (LSTM), was developed to estimate the SOE of lithium-ion batteries under different magnetic fields. First, we improve the decoder structure by adding a convolutional module using residual connections with trainable weight parameters to capture hidden states with more details.The improved decoder does not require label history information for decoding, which improves the generalization ability of the model. Finally, the output of the Informer network is a higher-dimensional hidden feature that is input into the LSTM network layer to output the SOE prediction value, which improves the original Informer network's ability to integrate sequences. Experiments with magnetic field and public datasets show the improved Informer-LSTM network achieves 0.31 % MAE, 0.42 % RMSE, and 1.79 % maximum error in SOE estimation, outperforming others in short sequence predictions. • Proposed improved Informer-LSTM network to estimate SOE. • The estimation of SOE under various magnetic field conditions is comprehensively considered. • It also has excellent performance in the case of short sequence data. • The verification was carried out under different magnetic field strengths and charge and discharge rates. [ABSTRACT FROM AUTHOR]

Accurate estimation of state of charge (SOC) forms the foundation of battery management systems. Although commonly used for SOC estimation, equivalent circuit models (ECMs) inadequately capture battery nonlinear dynamics and rely on SOC-open circuit voltage curves. To overcome these limitations, this paper introduces state-dependent mechanisms into ECMs, proposing a state-dependent quasi-linear parameter-varying model (SD-QLPVM). This model incorporates a quasi-linear model derived from ECMs. Crucially, it eschews traditional approaches of parameter determination through offline experiments or online adaptive methods, which are limited by their linear nature. Conversely, the parameters of the quasi-linear model are treated as time-varying and state-dependent functional parameters, calculated using radial basis function neural networks (RBF-NNs). Subsequently, state variables, such as terminal voltage, current, SOC, and temperature, are used to characterize the operation point of LIBs. By considering state variables as the inputs to the RBF-NNs, the proposed parameter determination approach enables the quasi-linear model to dynamically adjust its parameters in response to evolving battery operation points, representing battery dynamics accurately and responsively. Finally, an online SOC estimation method is developed based on the SD-QLPVM and a particle filter. The effectiveness of the proposed model and SOC estimation method is verified across various drive cycles and temperature conditions. • Introduce state-dependent mechanism into ECMs, proposing the SD-QLPVM. • Enhance modeling capacity and eliminate the dependency on SOC-OCV curves. • A close-loop and real-time approach for SOC estimation is designed based on the PF. • Verified across various temperature levels and drive cycles. [ABSTRACT FROM AUTHOR]

Electric autonomous underwater vehicles (AUVs) are facing more urgent battery thermal management (BTM) demands than electric cars, as their battery compartment design is constrained by limited volume, airtightness and impact loads. In this paper, a novel hybrid battery thermal management strategy of metal foam/paraffin composite combined with thermal bridges is proposed for the AUVs' BTM. A three-dimensional model considering the non-thermal equilibrium effect for AUVs' battery module was developed. Gradient PCMs and gradient foam porosity were introduced to improve the temperature and melting behaviors in various regions of AUVs' battery pack. The trade-off between performance and weight of metal foam/PCM composite on AUVs' battery thermal management was also quantitatively analyzed. The results showed that the temperature in the top region of the battery pack was higher than that in the bottom region due to the natural convection of the liquid PCM. The positive gradient porosity of metal foam has the advantages of large porosity increasing the heat storage and small porosity improving the thermal conductivity, both benefit to reduce the maximum temperature of the battery module. The positive gradient latent heat and melting point hybrid strategy of PCM demonstrates the best thermal management performance when compared with constant parameter cases, and the maximum temperature of battery modules is more sensitive to the melting point of PCM. When the metal foam/PCM composite was introduced, the weight of the battery module increased by 27.1%, while the maximum temperature and temperature difference of the battery module decreased by 44.27 K and 11.1 K, respectively. The safe working time of the battery module was extended by 105.7%, and the mass-energy density that can be utilized was increased by 61.9%. The findings can help AUVs to achieve the goals of high speed and long range. • The metal foam/paraffin composite was innovatively used for AUVs' BTM. • A 3D model was established considering non-thermal-equilibrium effect. • The thermal and melting behaviors of battery module were discussed in depth. • The performance of metal foam/paraffin with gradient parameters was compared. • The trade-off between performance and weight of metal foam/paraffin was evaluated. [ABSTRACT FROM AUTHOR]

Big data platforms for electric vehicles (EVs) provide data and network advantages for the online battery management system (BMS) algorithms. However, storage costs lead to platform degradation of data quality, which increases the error of online BMS algorithms. Voltage is the raw data for many BMS algorithms, and improving its accuracy and prediction for use in EVs big data platforms has application prospects. In this paper, a high-precision voltage prediction method for the whole life cycle of batteries is proposed. The self-attention network can accurately predict voltage of a sparse dataset by pre-extracting features through transfer learning. A new loss function is proposed to address the problem of overfitting and distortion caused by using sparse data to train the model. Moreover, a training dataset update strategy is proposed to adjust the model according to internal and external factors of battery. The superiority, stability, and robustness of the proposed method are verified by experimental datasets and real-world datasets. The mean-absolute-percentage-error (MAPE) of predicted voltage is less than 0.5%, and it is 1–5% lower than traditional training methods. The proposed method has the advantage of high accuracy, low data requirements, and low computational amount. This study provides the foundation for implementing whole life cycle BMS algorithms. • Sparse voltage data from the new energy vehicle big data platform increases algorithm errors. • A high-precision voltage prediction method for the whole life cycle of batteries is proposed. • An online transfer learning strategy that considers the condition and environment of the battery. • The effectiveness of the proposed method is verified with experimental and real-world datasets. • A fundamental basis for online management algorithms of lithium-ion batteries is provided. [ABSTRACT FROM AUTHOR]

Energy storage systems, especially lithium-ion batteries have gained significant attention and interest due to their potential in storing electrical energy and environmental sustainability. They play a crucial role in electric vehicles and significantly impact their performance, particularly in terms of electric driving range and quick acceleration. Despite their advantages, lithium-ion batteries also have limitations. These include the potential for thermal runaway, which can lead to safety hazards if not properly managed, such as outgassing, fire, and explosion that in turn cause significant property damage and fatalities. Published studies on road vehicles have not adequately considered the safety assurance of rechargeable energy storage systems in accordance with ISO 26262 standard. Accordingly in this paper, we focus on the safety assurance of a battery management system (BMS) that prevents thermal runaway and keeps lithium-ion batteries safe in electric vehicles. To this end, the safety life cycle process is performed. At first, the potential hazards that lead to thermal runaway impacting the functions of electric vehicles have been identified and safety goals related to means for preventing and controlling hazards are formulated. Next, the functional safety requirements are derived from each safety goal, and subsequently technical safety requirements are derived. To demonstrate the acceptable safety of electric vehicles using the BMS strategy, the safety cases are developed from the functional safety activities. The safety contracts are derived from battery specifications and chemistry and are associated with safety cases that provide the means for performing necessary adaptations at the operational phase. We leveraged a simulation for performing the verification and validation as well as finetuning of the BMS strategy. Simulation data is gathered, and the critical parameters are monitored to determine safety violations, control actions are triggered to resolve them, and safety cases are updated to reflect the current system safety. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) become more commonplace, the development and deployment of advanced battery thermal management (BTM) technologies are vital for increasing the sturdiness of EV batteries, ultimately contributing to the sustainable and massive adoption of electric mobility. This study comprehensively evaluates new advancements in BTM systems for EVs, supplemented with a comparative evaluation of various BTM technologies, including active and passive cooling strategies, structure design, and advanced control algorithms, including deep learning methods. The study also scrutinizes the software's capabilities employed for designing BTM systems. The cross-relevant papers related to BTM systems from 2019 to early 2024, which rely on Scopus and Web of Science databases, are considered. The comparative evaluation explores the strengths and obstacles of different BTM processes, shedding light on their efficacy under varying operational conditions. Additionally, this study discusses the impact of BTM on overall EV efficiency from the perspective of thermal considerations. Insights into current research trends, innovations, and emerging trends in the field are also presented. Ultimately, this state-of-the-art study aims to thoroughly understand the latest BTM for EVs. The findings offer insightful information for scientists, engineers, and professionals pursuing sustainable transportation development and the continuous enhancement of EV technology. [ABSTRACT FROM AUTHOR]

Accurate online estimation of state of charge (SOC) and state of health (SOH) is crucial for safe operation and reasonable planning of battery management system (BMS). However, battery internal states are unmeasurable directly and coupled with each other, which results in serious difficulties on multi-states accurate estimation. Considering SOC estimation is deeply influenced by SOH, this paper proposes a collaborative estimation method combined machine learning algorithm with simple physical rule. Integrated the merits of the above methods, it can not only improve the estimating accuracy but also low computational burden to a large extent. Firstly, SOH acts as input of SOC in collaboration, which means SOH should have been already known before SOC estimation operation. Thus, SOH is creatively predicted based on gate recurrent unit network in ahead of SOC estimation. Subsequently, the well-trained SOC data-driven model is combined with the ampere hour (Ah) integration, which act as observation and state equation respectively in the particle filtering algorithm to realize the final estimation of SOC. This way, the sole data-driven model is well improved under physics restriction without any other computational procedure, such as online parameter identification. Simultaneously, the predicted SOH is used to fill the Ah integration expression to realize the collaboration between SOC and SOH. What's more, the SOH is further corrected by the incremental capacity peak at the checkpoint, thereby further reducing the estimation deviation. The experimental result on battery shows that the SOH estimated error is below 0.3Ah (the rated capacity is 32.5Ah) and SOC error is less than 1.5 %. What's more, the generalization capacity of the proposed method is verified on different battery types based on public datasets. Obviously, the proposed method has fairly satisfactory property and performance and can totally support its promotion into state online evaluation of battery energy storage. • Clarify the coupling relationship between SOC and SOH. • Based on the coupling relation, SOH estimation takes single-step prediction and checkpoints correction with GRU network. • SOC estimation takes data and physics fusion method via acting as observation and state equation in PF respectively. • The co-estimation framework is verified both on laboratory measured data and public dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a new diagnostic indicator derived from the distribution of relaxation times (DRT) analysis of electrochemical impedance spectroscopy (EIS) data for lithium-ion battery state estimation. The indicator is the area of the peak occurring within the highest frequency region of the DRT spectrum, exhibiting correlation with battery internal temperature, state of charge (SOC), and state of health (SOH). By focusing EIS measurements on a narrow high-frequency range and preprocessing data before DRT conversion, the overall time for impedance measurement and DRT calculation is significantly reduced, enabling practical onboard implementation in battery management systems (BMSs). Experimental analysis validates the proposed indicator's effectiveness and trends under varying temperature, SOC, and SOH conditions. A case study compares the proposed DRT-based method with an existing intercept frequency-based approach for internal temperature estimation, demonstrating the DRT method's superior robustness in the presence of noise. This suggests the potential for accurate battery state monitoring in noisy operating environments like electric vehicles. The proposed methodology paves the way for integrating advanced EIS-based diagnostic tools into real-time BMSs for enhanced battery performance and safety. [Display omitted] • Highest frequency DRT peak via EIS: New diagnostic indicator for Li-ion batteries. • Experiments reveal the indicator reflects battery temperature, SOC, and SOH trends. • Optimizing EIS frequency range minimizes measurement time and computational load. • Case study shows more robust temperature estimation than others in noisy conditions. [ABSTRACT FROM AUTHOR]

Lithium-ion battery charge dynamics is consisted of several discipline theories. This is particularly challenging for putting all kinds of discipline theories into a dynamics model, for which the difference between the electricity and the chemistry is very significant. In this paper, a voltage dynamics model of lithium-ion battery is formulated by fusing current-integral principle and voltage charge curve into equivalent circuit model. For low computing-power and practical engineering-application, an extended sliding mode observer is designed to estimate the state-of-charge, and five parameters of the voltage dynamics model are also observed online in real-time. Then, the extended sliding mode observer algorithm is translated into a battery management system, which is validated in a battery electric vehicle. Among extended sliding mode observer, current integral method, and extended Kalman filter, some experiments are tested in a same driving cycle. Based on experimental results and statistical analyses, it can be concluded that the extended sliding mode observer is a good candidate for state-of-charge estimation by the voltage dynamics model. • A voltage dynamics model of lithium-ion battery is developed by fusing current-integral principle, voltage charge curve, and equivalent circuit model. • An extended sliding mode observer algorithm is designed to estimate the state-of-charge based on the voltage dynamics model in real-world vehicle. • The parameters of the voltage dynamics model are estimated online in real-time. • The extended sliding mode observer based on the voltage dynamics model is validated in a battery electric vehicle. [ABSTRACT FROM AUTHOR]

Dynamic modeling and state of charge (SOC) estimation of lithium-ion batteries (LIBs) is a critical technology in the battery management system. Aiming at the essential nonlinear characteristics of LIBs and the time-varying operating state caused by the influence of the external environment including load changes and other factors, this paper proposes to adopt the state-dependent autoregressive model with exogenous inputs (SD-ARX) to describe its nonlinear dynamic characteristics. This type of model first constructs state signal quantities that can characterize the dynamic properties of LIBs and uses them as inputs to the radial basis function neural network to approximate the functional-type coefficients of the SD-ARX model, which is used to guide the model to represent the nonlinear dynamic properties of LIBs under different operating states. Benefiting from the model can be represented as a linear combination of nonlinear functions, the model parameters identified offline by a structured nonlinear parameter optimization method. Finally, the SOC is estimated online using an adaptive extended Kalman filter based on the established model. The results show that the method can reliably estimate the SOC of a battery under different working conditions and a relatively wide temperature range and achieves a balance between accuracy and real-time performance. • A state-dependent autoregressive model with exogenous inputs for battery modeling. • Optimization of parameters by structured nonlinear parameter optimization method. • Comparison with results from existing literature. • The model has good modeling accuracy and SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method. • The SOH estimation accuracy is improved in an optimization manner. • The optimal multiple voltage ranges are automatically selected by NSGA-II and grid search. • Various solutions at different battery charging stages are provided for SOH estimation. • Three NMC-based batteries are aged for 360 days to validate the proposed method. [ABSTRACT FROM AUTHOR]

Automotive electrification is a main source of demand for lithium ion batteries. Performances of battery charging directly affect consumers' recognition and acceptability of electric vehicles. Study on optimized charging methods is vital for future development of a smarter battery management system and an intelligent electric vehicle. This paper starts from introducing the working principles and existing problems of simple charging methods and then elaborating various optimized charging methods along with their characteristics and applications. It demonstrates that the optimized charging methods can reduce charging time, improve charging performance and extend battery life cycle comparing with conventional charging methods. At the end, this paper also provides a four-step pathway towards the design of an optimal charging method of Li-ion batteries: determine optimization objectives, establish optimization scheme, develop matching design and implement and promote the optimal charging method. • Optimized charging methods are reviewed. • A new viewpoint on optimized charging methods is presented. • A four-step pathway towards the design of an optimal charging method is provided. • The prospects and development direction of charging methods are expounded. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of charge (SOC) of a lithium-ion battery is important to ensure the safe operation of the battery management system. Adaptive extended Kalman filter (AEKF) is used to estimate SOC, which approximates nonlinear function to linear function by first-order Taylor expansion with large truncation error. Therefore, the second-order AEKF is proposed to reduce the truncation error and improve the accuracy of SOC estimation. Since the estimation accuracy of second-order AEKF is also affected by the sliding window length (SWL), correspondence analysis is proposed in this paper to verify the correlation between SWL and algorithm errors and obtain a reasonable SWL parameter value, which helps to ensure that the algorithm has higher accuracy in SOC estimation under the condition that the value of SWL is not changed when the working condition changes. To substantiate the efficacy of the algorithm outlined in this paper, data sets collected from different sources and at various temperatures are employed. The experimental results obtained through meticulous analysis demonstrate that the second-order AEKF proposed in this study excels in terms of estimation accuracy and robustness. • Second order Taylor expansion is used to approximate nonlinear functions. • The second-order adaptive extended Kalman filter (SOAEKF) algorithm is proposed. • Correspondence table is used to select the value of sliding window length (SWL). • Verify the correlation between SWL and SOAEKF error with correspondence analysis. [ABSTRACT FROM AUTHOR]

The performance of electric vehicles (EVs) heavily relies on the durability and longevity of lithium-ion cells, which are strongly affected by operating temperatures. Various electrochemical models have been developed to investigate the impact of operating temperature on the dynamic heat generation at cell level. Among these, the Lumped Single Particle (LSP) model stands out due to its lower computational cost and higher accuracy under > 1 C-rates. However, key electrochemical parameters (i.e. the ohmic overpotential, exchange current, and diffusion time constant) in the LSP model are temperature-sensitive, requiring Arrhenius dependencies. Conventional activation energy measuring approaches, relying on testing under discrete fixed temperatures to obtain activation energies, are time-consuming. To address this, this paper proposes a FAED approach that fits the temperature-sensitive parameters and corresponding activation energies to experimental data with a single discharge process, reducing the testing time to a minimum of one hour. This approach was experimentally validated on a Tesla battery module (74P6S), demonstrating a good agreement between simulation and experimental results up to 1.5 C-rates, ranging from 16 ℃ to 46 ℃. Furthermore, this approach is suitable for cells with different lithium chemistries, formats, and properties, making it an effective method for thermal analysis of EV packs. • A novel Fast Activation Energy Derivation (FAED) approach is proposed in this paper. • Reduce activation energy measurement time to 1 h. • Suitable for module level test for EVs. • Experimental verification on a TESLA Model S battery module. [ABSTRACT FROM AUTHOR]

Internal short circuit (ISC) is a serious safety hazard for lithium-ion battery packs. How to comprehensively detect and evaluate ISC in battery packs remains a challenging problem. Motivated by this, this paper proposes an ISC detection method based on the transformation matrix and an ISC resistance calculation method based on an improved state-space model. Specifically, benefiting from the descriptive ability of the transformation matrix for curve changes, the shear element in the transformation matrix is used to capture the skewed and downward voltage pattern of the ISC cell in the battery pack. An online detection flow is designed based on the opposite variation relationship between the shear elements calculated from adjacent cells. Moreover, an improved state-space model is developed to directly estimate the ISC current, which ensures that the weight of the ISC current in the model is unrestricted. A dual extended Kalman filter with separated time scales is deployed to obtain accurate state estimations. Experimental results validate the effectiveness and advantages of the ISC detection method, as well as the high accuracy of the ISC resistance calculation method. • Both ISC detection and ISC evaluation are implemented in this paper. • Transformation matrix and shear element are introduced to ISC detection. • An improved state-space model is developed to estimate ISC current and resistance. • Comparative studies demonstrate the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. • The paper proposes Evolving Quantum Fuzzy Neural Network. • Number of rules are automatically adjusted and evolved, in quantum fuzzy set. • The decoupled extended Kalman filter (DEKF) is used for parameter estimation. • The method estimates cell capacity, over 600 charging-discharging cycles. • The results are simulated and validated by experimental verification. [ABSTRACT FROM AUTHOR]

A viable way to reduce carbon emissions and achieve sustainable development goals (SDGs) is through reliable and sustainable transportation, specifically through the utilization of electric vehicles (EVs) and integrated energy storage systems (ESSs). The operation and performance efficiency of EVs are based on accurate prediction of the remaining useful life (RUL), which improves the reliability, robustness, efficiency, and longevity of different ESSs like lithium-ion battery (LIB), supercapacitor (SC), and fuel cell (FC) in EV applications. Nonetheless, the RUL prediction of the LIB, SC and FC is considered difficult due to factors associated with capacity degradation and variable environmental conditions. In recent times, enormous attention to conduct the RUL prediction has been attained by expert deep learning (DL) techniques due to their supremacy with high volumes of data, fast computational performance, and large storage capacity. However, the DL techniques implemented for RUL prediction with ESS in EV applications are inadequate. Therefore, a novel review paper is presented which comprehensively reviews DL-based RUL prediction techniques for LIB, SC, and FC, encompassing DL models, their strength, weakness, accuracy, and research gaps. The objective of the presented review work is to demonstrate the superiority of the DL techniques for RUL prediction. Firstly, the review work examines different DL methods applied to conduct the RUL prediction of the LIB, SC and FC. Furthermore, the RUL prediction results with different factors like model implementation, critical hyperparameters, and data features/samples are analyzed. The review also outlines various execution features related to experiments, data, model features, and computational performance. Additionally, the current issues with the DL techniques for RUL prediction are identified. Lastly, future trends and outlooks are listed that would pave the way toward developing an accurate RUL prediction framework for ESSs technologies in reliable EV technology. [ABSTRACT FROM AUTHOR]

With the rapid progress of the new power automotive industry, the requirements for vehicle range and charging ratio have gradually increased. With the high-speed cycling of batteries, the heat content increases rapidly, and the thermal problem has become the main factor restricting its development. One of the key technologies to maintain the performance, longevity, and safety of lithium-ion batteries (LIBs) is the battery thermal management system (BTMS). Owing to its excellent conduction and high temperature stability, liquid cold plate (LCP) cooling technology is an effective BTMS solution. Currently, the maximum surface temperature (T max), the pressure drop loss of the LCP, and the maximum temperature variance (T max-v) of the battery are often applied to evaluate the cooling capacity of LCP cooling BTMS. These parameters are also used as design indicators to guide the optimization of new liquid cooling BTMS. However, objective factors such as system safety, cost, system simplification, and heating or cooling efficiency should also be considered in the design and evaluation process of BTMS. Therefore, it is necessary to explore a multi-objective optimization system to design liquid plate BTMS and use a unified evaluation system to assess the capability of LCP cooling BTMS to comprehensively analyze and achieve the whole system performance improvement. Four common BTMS cooling technologies are described in this paper, including their working principle, advantages, and disadvantages. Direct liquid cooling and indirect liquid cooling BTMS are compared and analyzed. The BTMS optimization technology of LCP is reviewed and discussed from the aspects of structure design, type of working liquid, space arrangement, and system. Finally, the challenges affecting the development of liquid-cooled BTMS are outlined and suggestions for future research are made. • A review on liquid-based cooling of battery thermal management system (BTMS) is presented. • The influence of optimization techniques of the cold plate-based BTMS is discussed. • A general framework for a multi-objective optimization method for liquid cold plates is established. [ABSTRACT FROM AUTHOR]

With the emerging development of electrical vehicle, the efficient thermal management of the lithium-ion battery is becoming a critical point for its wide application and normal operating. Hence, based on the advantages of liquid cooling and PCM, a novel hybrid cooling system using PCM coupled with wavy microchannel cooling plate (PCM-WMCP) was proposed in this paper, and a numerical study was carried out to investigate its performance. It's found the hybrid cooling system with PCM and microchannel cooling plate showed much lower battery temperature than individual PCM and air cooling systems, moreover, among the hybrid cooling systems, the novel PCM-WMCP exhibited 2 K lower maximum battery temperature than PCM-SMCP (straight microchannel cooling plate) type at 1C, and increased potential in utilizing PCM for heat dissipation. The PCM was further modified to composite PCM (CPCM) by adding expanded graphite, which was found to realize a further lower maximum battery temperature and more uniform battery temperature field, with graphite mass fraction of 10 %. Besides, facing thermal runaway, the CPCM-WMCP cooling system showed a better temperature control ability compared to CPCM-SMCP, with temperature suppressed to below 318 K, and the maximum temperature difference at about 1.6 K. [ABSTRACT FROM AUTHOR]

With lithium-ion batteries being utilized in all aspects of life, accurately estimating the state of charge (SOC) of a battery has become a key issue in battery management systems. In this paper, an improved hybrid model based on adaptive extended Kalman filter (AEKF) and improved long short-term memory (ILSTM) neural network is proposed. The proposed model is based on a second-order RC equivalent circuit model, and the dynamic forgetting factor recursive least squares (DFFRLS) and AEKF algorithms are utilized to obtain the initial SOC estimates. And the estimation error in the AEKF algorithm due to neglecting the higher order terms of the Taylor expansion equations is compensated by the improvement of the LSTM network. The results under different working conditions indicate that the SOC estimation of the hybrid model has good convergence and high system robustness. The maximum error (MAX) of this algorithm is less than 2.3 %, especially the root mean square error (RMSE) and mean absolute error (MAE) are less than 0.84 % and 0.65 %, respectively. • A model and data-driven fusion method for battery SOC estimation is proposed. • It is based on the battery model and supplemented by the data-driven model. • Introduced the dynamic forgetting factor recursive least squares method. • The ILSTM algorithm is proposed to compensate for the high-order error of AEKF. • The proposed method can accurate SOC estimation at various operating conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • A systematic overview is given on safety hazards in Li-ion batteries. • Diverse demerits in Li-ion batteries are addressed and scrutinized. • Summarizes active and passive cooling based on PCM. Li-ion batteries are a key component of today's electric cars. However, those pushing for more electric vehicles are concerned about battery performance. Because temperature determines the battery's durability, regulating the heat and maintaining the temperature and temperature changes in the battery packs within an acceptable range is critical. A battery cooling system can prevent early degradation of battery life. The first section of this study looks at the impact of PCM and its composite on battery performance in three different cooling conditions: low temperature, high temperature, and ambient temperature in environmental conditions. The battery cooling system is then thoroughly explored, focusing on battery modeling approaches and thermal management techniques. This article summarizes active and passive cooling based on PCM and the influence of high and low-temperature environments on lithium-ion battery technology and their aging effects. In addition, this paper examines some real-world accidents involving electric vehicles under Indian climate conditions. The southern regions typically have hot weather for most of the year. Most accidents occur in the southern region, where the temperature is high. [ABSTRACT FROM AUTHOR]

• The honeycomb fins-modified PCM exhibits prominent advantages in cooling performance. • The honeycomb fins effectively improve the heat dissipation of the battery at high rates. • Fin thickness is the dominant influential factor in the performance of BTMS. • Energy density and PCM volume are considered in the BTMS design. In this paper, a battery thermal management system (BTMS) based on honeycomb-structured fins-modified phase change material (PCM) is constructed to address the thermal safety issues of electric vehicles under high discharge rates. The effect of the structural parameters (fin spacing, fin width, fin thickness) of honeycomb fins on the heat dissipation performance of BTMS is discussed in detail. Moreover, the influence of the honeycomb structural design of fins on the energy density and the PCM volume is further investigated. The cooling performance and mechanism of BTMS with honeycomb fin-modified PCM under high-rate working conditions are presented. Compared to the BTMS with finless PCM, the BTMS with honeycomb fins-modified PCM shows a reduction of 26.52% in the battery temperature and an increase of 47.6% in the utilization efficiency of PCM. The honeycomb fins modified PCM based-BTMS exhibits prominent advantages in lowering battery temperature, promoting temperature uniformity, and increasing PCM utilization efficiency at a high discharge rate of 8C, making it suitable for high power operation requirements. The research findings offer theoretical and data support for the design of BTMS in electric vehicles. [ABSTRACT FROM AUTHOR]

Liquid cooling is a promising approach for battery thermal management due to its compact construction and high heat transfer coefficient. However, the conventional liquid cooling plates are generally designed by trial-and-error approaches regarding the battery as a uniform heat generating body during charge and discharge processes, limiting the cooling performance of the battery thermal management systems. This paper introduces a deep learning framework that considers non-uniform heat generation to design a liquid cooling plate with a bionic leaf structure. The framework links the structural features of the cooling plate to performance using deep learning and optimizes these features with a non-dominated sorting genetic algorithm II. The results show that the maximum temperature difference of the battery can be up to 11°C in natural cooling condition at 4C. The average value of of the three outputs of the established ANN neural network model is 0.98, and the model is capable of predicting results with high accuracy. More significantly, the temperature difference and pressure drop are 2.26°C and 2562.62 Pa compared to traditional structure, which are reduced by 60.17% and 25.06%, respectively. The proposed deep learning-based multi-objective optimization framework can guide the design of liquid cooling plates for battery thermal management systems. • A model considering the non-uniform heat generation characteristics is developed. • A novel bionic leaf structure liquid cooling plate is designed and optimized. • Deep learning is used to predict the performance of liquid cooling plates. • A multi-objective optimization framework combining deep learning and NSGA-II is developed. [ABSTRACT FROM AUTHOR]

High coulombic efficiency (CE) usually indicates a long battery cycle life. However, the relationship between long-term CE evolution and battery degradation is not fully understood yet. This paper explores the behavior of long-term CE and clarifies its relationship with capacity degradation. Cycle life tests are conducted on two types of mainstream commercial lithium-ion batteries. An incremental capacity (IC) analysis is then employed to identify battery aging mechanisms. Experimental observations along with in-depth discussions are presented regarding battery degradation, aging mechanisms, and CE evolution. From the experimental results, two typical degradation patterns are recognized. From the IC analysis, we observed that, in addition to a loss of lithium inventory, a loss of active material accelerates battery degradation and brings down CE values. From an electrochemical perspective, this paper establishes the relationship between CE evolution and capacity degradation. This relationship can help develop battery degradation models, estimate battery health states, and provide early failure warnings for a battery management system. [ABSTRACT FROM AUTHOR]

Physics-based model has been regarded as a promising alternative to equivalent circuit model due to its ability to describe internal electrochemical states of battery. However, the rigorous physics-based model, namely pseudo-two-dimensional (P2D) model, is too complicated for online application in embedded battery management system. In this paper, to simplify the P2D model, a series of polynomial functions are employed to approximate the electrolyte phase concentration profile, solid phase concentration profile, and non-uniform reaction flux profile, respectively. Especially, the accuracy of 2nd-order and 3rd-order polynomial approximations for reaction flux is compared, and the higher-order is validated with more strength. Benefit from the acquisition of above variables, the electrolyte potential is derived directly according to the conservation of charge at electrolyte phase; the accuracy of activation overpotential is also improved by using the non-uniform reaction flux rather than assuming the uniform current density in single particle (SP) model. Finally, the developed model is simulated by different constant current rates, hybrid pulse and driving cycles, and its outputs are compared with P2D model and original SP model. The results demonstrate that the model proposed in this paper could capture the battery characteristics efficiently, and also significantly reduce the computation complexity. [ABSTRACT FROM AUTHOR]

Thermal runaway in lithium-ion batteries (LIBs) cannot be completely avoided and poses a risk of fire and explosion incidents. Existing battery management systems (BMSs) lack the timeliness and accuracy in warning of the thermal runaway, thus it is difficult to prevent such accidents. This paper presents a review of the research progress in LIB thermal runaway, focusing on the evaluation of different indicative signals and detection methods including temperature, electrical signal, and gas monitoring. The limitations of temperature and electrical signal monitoring, such as lacking timeliness and insufficient sensitivity, contribute to the inadequate reliability of current BMSs. To overcome these limitations, a gas monitoring solution is proposed for the ultra-early warning of the thermal runaway. By analysing the release of gases during the thermal runaway process, certain gases like CO 2 , volatile organic compounds (VOCs), particularly hydrocarbons (C x H y), and CO are identified as suitable indicators for the ultra-early detection of the thermal runaway. The introduction of gas sensors capable of detecting these gases is discussed. Given the low power consumption and high safety requirements, low/room temperature gas sensors, such as Non-Dispersive Infra-Red (NDIR) and light-excited/assisted gas sensors, have great potential for the ultra-early warning of the thermal runaway of LIBs. • Gas sensors have great potential for the ultra-early warning of the thermal runaway in LIBs. • CO 2 , VOCs, CxHy, and CO are identified as suitable indicators for the thermal runaway. • Low power consumption and high safety are key requirements for integrating gas sensors into Battery Management Systems. [ABSTRACT FROM AUTHOR]

• Pulsating heat pipes with good thermal performance were designed for battery module heating management at low–temperatures. • The low–temperatures thermal management system for the battery module with pulsating heat pipes was proposed. • The thermal and electrical performance of the battery module with low–temperatures thermal management system was investigated. To effectively solve the problem that the capacity of lithium power batteries decreases significantly and is prone to irreversible damage under low–temperature charge and discharge scenarios, which seriously affects the driving range, cycle life and service safety of electric vehicles (EVs). In this paper, a low–temperatures thermal management system of pulsating heat pipes with ethanol as the base fluids and TiO 2 nanofluids as the working fluids (TiO 2 –CLPHP TMS) is constructed. On this basis, the thermal performance analysis of TiO 2 nanofluids pulsating heat pipes (TiO 2 –CLPHP) is carried out under different volume concentrations (0 %, 0.5 %, 1.0 %, 2.0 %, 4.0 %, and 8.0 %) and different fluid filling rates (30 %, 50 %, 70 %, and 90 %) in a low–temperature environment. Aiming at the low–temperatures charging scenario of the battery module, the performance test of the TiO 2 –CLPHP TMS for the battery module heating management is carried out under different low–temperature environments (−5 °C, −15 °C, and −25 °C) and different charging rates (0.3C, 0.5C, 0.7C, and 1.0C). The experimental results show that TiO 2 –CLPHP with fluids filling rate of 50.00 % and TiO 2 nanofluids concentration of 2.00 % has good performance in start–up and heat transfer, and can optimize the thermal performance of TiO 2 –CLPHP. Meanwhile, TiO 2 –CLPHP TMS can ensure that the battery module has superior thermal performance. the battery module can be charged within the allowed temperature range (greater than 0 °C). under different low temperature environments (−5 °C, −15 °C, and −25 °C), the temperature of the battery module is basically maintained at 3.30 °C, 8.00 °C and 16.00 °C. The temperature difference between different areas of the battery module is within the allowable temperature difference range of 5 °C, which reduces the adverse effects of the battery aging caused by low–temperature charging and uneven temperature. Moreover, the battery module also has good electrical performance, the battery module voltage is in a low–level range in the early and middle stages of charging. During the complete charging process, the low–temperature charging capacity of the battery module has almost no attenuation, and the charging capacity ratio is as high as 95.92 %. Compared with the pure battery module, the charging capacity and charging efficiency of the battery module with the TiO 2 -CLPHP TMS has been improved to different degrees. Especially at −25 °C, the maximum charging capacity and charging efficiency can be increased to 24.51 %, and the minimum can be increased to 13.17 %. which ensures the efficient charging of the battery module. This proves that the designed TiO 2 –CLPHP TMS is feasible and effective for battery module heating management. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state-of-charge (SOC) and state-of-energy (SOE) of lithium-ion batteries (LIBs) is fundamental for the battery management system. This paper proposes a method based on the combination of domain adaptation (DA) and unscented Kalman filter (UKF) (DA-UKF) to achieve joint estimation of SOC and SOE at distinct temperatures. A data-driven network consisting of source domain (SD) and target domain (TD) parts is adopted. A gated recurrent unit network and linear layer are used to extract features of the SD and TD datasets, while maximum mean difference and adversarial DA are adopted to align the features. The linear layer outputs SOC and SOE joint estimation results, and the UKF smooths the outputs to obtain accurate and stable joint estimation. Experimental results show that, regardless of whether performing in supervised or unsupervised mode, the DA-UKF can achieve highly robust and accurate joint estimation of SOC and SOE at various temperatures. Compared with other advanced methods, the root mean square error and the mean absolute error of the DA-UKF, at different temperatures, reduce, on average, between 49.760% and 84.150%, and 53.579% and 84.787%, respectively. Moreover, the DA-UKF does not require complex adjustments to the hyperparameters of the network. • New method for joint estimating SOC and SOE of lithium-ion battery (LIB). • Data-driven method domain adaptation (DA) and unscented Kalman filter combined. • DA used to achieve cross-temperature state estimation. • MMD and adversarial DA used to perform domain feature alignment. • New scheme is compared with existing alternatives for estimating SOC and SOE. [ABSTRACT FROM AUTHOR]