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In recent years, lithium-ion batteries (LIB) have become the de facto energy storage means for electric vehicles (EVs) due to their high energy density. However, LIBs require state of charge (SOC) monitoring to ensure safe operating conditions and for an enhanced lifespan. Since SOC cannot be directly measured, various estimation methods have been proposed in recent literature, most notably the recent rise in popularity of long short-term memory-recurrent neural networks (LSTM-RNN). Current research in the use of LSTM-RNNs typically applies a single strong data-driven model that can produce accurate predictions at the expense of lengthy model training times. As LSTM-RNNs must be retrained as LIBs age to maintain reasonable estimation accuracies, this poses a problem for EV battery management system processors. To address this research gap, this work proposes a homogeneous ensemble learning model based on several LSTM-RNN base models, as a solution to reduce the training time. The LSTM base models are fused by a meta-learner, to overcome the shortcomings of traditional ensemble fusion methods. Data diversification methods for homogeneous ensembles are also reviewed and benchmarked in this paper. The proposed method achieves a low model training time by 2.6–3.5 times while maintaining a similar mean absolute error (MAE) of 1.4% when compared to conventional shallow and deep LSTM-RNN models. The proposed model was also successfully validated with battery discharge data collected from a custom build battery tester. It is anticipated that the proposed LIB SOC estimation approach can contribute to increased feasibility of using artificial intelligence in EVs in general and improve EV battery management. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation. [ABSTRACT FROM AUTHOR]

A method based on strong tracking unscented Kalman filter with multiple suboptimal fading factors (MSTUKF) was proposed to accurately estimate the state of charge (SOC) of power batteries of electric vehicles online. Taking a certain lithium-ion battery as the research object, a second-order RC equivalent circuit model of the battery was established based on its external characteristics and related mechanism. Then the recursive least squares method with forgetting factor was adopted to identify the model parameters, and the MSTUKF nonlinear state space equation of the battery was established according to the equivalent circuit model. Finally, the SOC estimation algorithm was verified by simulation experiments under ECE15 and UDDS conditions. The results show that the error of MSTUKF in SOC estimation of lithium-ion battery is kept within 1.5%, so this method can estimate battery SOC accurately. [ABSTRACT FROM AUTHOR]

Ultrasound spectroscopy up to 6 MHz is carried out on a 12 Ah Lithium‐ion battery pouch‐cell. The analysis revealed that the attenuation behavior can be effectively described as having an absorption component and a resonance component. It was demonstrated that the absorption can be modeled as a second order polynomial. Two distinct resonances were identified at 2.2 MHz and 4.4 MHz. The first of these resonances shows a significant almost linear reduction in magnitude with increasing state of charge. By smartly choosing the ultrasound interrogation frequency to coincide with this resonance, an almost linear increase by a factor of more than five was measured in the signal amplitude between between state of charge levels of 0 and 100. This could provide the basis for a robust and straightforward state of charge determination method. [ABSTRACT FROM AUTHOR]

The core focus of the battery management system (BMS) is accurate state of charge (SOC) estimation of the lithium-ion batteries. To solve the problem of improper selection of the noise covariance matrix in the extended Kalman filter (EKF) algorithm, which in turn affects the actual operating effect and range of electric vehicles, this paper proposes the adaptive sine cosine–Levy flight–quantum particle swarm optimization (ASL-QPSO) algorithm to find the optimal noise covariance matrix. Firstly, this paper proposes the variable forgetting factor recursive least square (VFFRLS) algorithm to identify the parameters of the equivalent circuit model of the power lithium-ion batteries. Then, the obtained parameters are transmitted online by the EKF algorithm, based on which the local attraction factor is updated using the ASL-QPSO, which is used to select the appropriate noise covariance matrix. Finally, the optimized noise covariance matrix is obtained and used to achieve the accurate SOC estimation of the power lithium-ion batteries. Experimental results under different operating conditions and temperatures show that the maximum absolute error (MAX), mean absolute error (MAE), and root mean square error (RMSE) of the algorithm are less than 1.82%, 0.59%, and 0.72%, respectively. This demonstrates that the algorithm has superior convergence tuning and high robustness, presenting a novel optimization strategy for the SOC estimation of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

In order to achieve the safe and reliable operation of grid-connected decommissioned power battery energy storage system, based on long short-term memory ( LSTM), model predictive control ( MPC) and carrier phase-shift modulation, a coupled LSTM-MPC control and carrier phase-shift modulation method was proposed as an optimal control strategy for decommissioned power battery energy storage system by combining LSTM-MPC coupled control and carrier phase-shift modulation method. Firstly, the real time current and voltage were used to continuously predict the state of charge (SOC), voltage, current, and output power of each phase under different switching states at the next moment by using LSTM network and discrete model. Secondly, the cost functions corresponding to all the switching states of the converter were calculated, and the optimal control objective with the smallest cost function was established, so that the converter state with the smallest cost function value was used as the input state at the next moment. The converter state with the smallest cost function value was taken as the input state at the next moment. Thirdly, the ratio of the optimal output voltage to the total single-phase voltage of the system was calculated by using the system discrete model, which is used as the duty cycle of the carrier phase-shift modulation algorithm to generate the submodule switching signals, and the process was continuously iterated in a scheduling cycle to perform the feedback correction, so as to realize the optimal control. Finally, the effectiveness of the proposed control strategy was verified in terms of power tracking, SOC tracking, negative sequence current suppression, and power fluctuation suppression for both balanced and unbalanced grid voltage conditions. The simulation results show that the control strategy proposed can effectively achieve the power tracking and SOC tracking of the grid-connected retired power battery energy storage system. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced recursive least-squares algorithm that applies the adaptive accelerated proximal gradient method to identify Auto-Regressive Exogenous models with output outliers. First, the outlier problem was converted into a robust principal component analysis problem. The adaptive accelerated proximal gradient method is then introduced to recover the information matrix, and the recursive least squares algorithm is applied to estimate the model parameters. Furthermore, we demonstrated that the parameter estimation is unbiased and that the parameter estimation error converges to zero under persistent excitation. A series of bench tests consistently highlighted the practicality and effectiveness of the proposed algorithm. • The accelerated proximal gradient algorithm is introduced for effective outlier recovery. • The proposed adaptive accelerated proximal gradient algorithm can adaptively adjust the step-size. • A comprehensive framework is established by integrating accelerated proximal gradient and recursive least squares algorithms. • The proposed algorithm is validated through a state of charge estimation problem, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

While State-of-Charge (SOC) estimation for supercapacitors has been extensively studied, most research focuses on single units. However, the recent introduction of reconfigurable circuits significantly alters system dynamics, rendering existing SOC estimation techniques inadequate. This paper addresses this challenge by employing a switching systems approach to estimate the SOC of supercapacitors with reconfigurable circuits. We first establish an RC model for the supercapacitor integrated with the reconfigurable circuit and thoroughly analyze the state continuity and observability of the resulting switched system. Subsequently, we propose a switching observer and evaluate its convergence properties by comparing its performance against other observer techniques. Experimental validation on a hardware platform demonstrates the superiority of our proposed observer for accurate SOC estimation in this context. [ABSTRACT FROM AUTHOR]

To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with the peak of the IC curve (ICP) and its corresponding voltage serving as the health factor (HF). Thirdly, the GA-BP neural network is applied to map the relationship between HF, constant current charging time, and SOH, facilitating the estimation of SOH based on HF. Finally, SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time, and by updating the maximum available capacity. Experiments show that the root mean squared error of the joint estimation results does not exceed 1%, which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid systems, due to their combination of the high energy density of fuel cells and the rapid response capability of power batteries, have become an important category of new energy vehicles. This paper discusses energy management strategies in hydrogen fuel cell vehicles. Firstly, a detailed comparative analysis of existing PID control strategies and Adaptive Equivalent Consumption Minimization Strategies (A-ECMSs) is conducted. It was found that although A-ECMS can balance the energy utilization of the fuel cell and power battery well, the power fluctuations of the fuel cell are significant, leading to increased hydrogen consumption. Therefore, this paper proposes an improved Adaptive Low-Pass Filter Equivalent Consumption Minimization Strategy (A-LPF-ECMS). By introducing low-pass filtering technology, transient changes in fuel cell power are smoothed, effectively reducing fuel consumption. Simulation results show that under the 6*FTP75 cycle, the energy loss of A-LPF-ECMS is reduced by 10.89% (compared to the PID strategy) and the equivalent hydrogen consumption is reduced by 7.1%; under the 5*WLTC cycle, energy loss is reduced by 5.58% and equivalent hydrogen consumption is reduced by 3.18%. The research results indicate that A-LPF-ECMS performs excellently in suppressing fuel cell power fluctuations under idling conditions, significantly enhancing the operational efficiency of the fuel cell and showing high application value. [ABSTRACT FROM AUTHOR]

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape. [ABSTRACT FROM AUTHOR]

Electric vehicles need continuous monitoring of the energy supply all the time to provide the user with accurate information about the available energy. However, an increase in the power consumed by the monitoring system may cause a significant decrease in the vehicle’s autonomy. This letter presents a monitoring and estimation system for an electric car with a hybrid energy supply based on supercapacitors and batteries based on an embedded system capable of monitoring the general current, the current from the battery system, the voltage in the battery system, the voltage in the supercapacitor system, estimating the value of the state of charge from batteries, and showing the measurements to the user with an OLED display. The proposed system performs similarly in the estimation but decreases the energy consumption by 99.9% concerning the laboratory systems. [ABSTRACT FROM AUTHOR]

Shepherd's model is considered as an improved and simple to-apply battery dynamic model. The experimental nominal parameters of five different capacities of Lithium-ion Polymer battery samples are established from the manufacturer's datasheets. The discharge and charge dynamics of the battery sample with the highest capacity, 3100 mAh are examined and validated. The clarity to derive the dynamic model parameters from the battery datasheet is a fascinating attribute of this model. The experimental database is utilized to study its dynamic feature and subsequently the dynamic battery model, which describes the relationship between the state of charge, the open circuit voltage and ambient temperature. The model is implemented and simulated by using MATLAB/Simulink tools. The current charge-discharge equations describe the optimized electrochemical activities with sustainable long cycle duration. The temperature model brings under control the aging effect, which plays down on the energy capacity of the battery. A steady load of 5 A is linked to the battery and used in a detail simulation of a direct current machine in determination of load effect. The compatibility of these equations and simulations implies that Shepherd's model is promising to provide guidelines for the analysis of discharge and charge in rechargeable polymer based lithium-ion batteries. The results show that the model can precisely illustrate the dynamic behavior of the batteries. [ABSTRACT FROM AUTHOR]

Significant resources and diligent research have been dedicated to the investigation and enhancement of energy storage devices utilising hydrogen, lithium, or sodium. Efforts of this nature strive to offer sustainable energy storage solutions that are both dependable and environmentally friendly. This report presents a review a thorough an examination of the aforementioned technologies, with a particular emphasis on cost-effectiveness and State of Charge (SoC) administration. Furthermore, it comprehensively assesses their specific advantages and downsides. Sodium-based systems, such as sodium-sulfur batteries, exhibit remarkable stability and efficiency in sustaining desired charge levels, starting from the control of SoC. Lithium-based alternatives, such as Lithium-ion batteries (LIBs) have gained extensive usage adopted in several industries because to their enhanced energy density and improved SoC accuracy. The complexity associated with SoC is a challenge for hydrogen-based systems, including hydrogen fuel cells, due to their dependence on gas storage and conversion techniques. The assessment places significant emphasis on cost analysis, which evaluates the economic feasibility of each storage method. Sodium-based systems, known for their well-established technology and ample availability of raw materials, frequently present a financially viable option, especially for large-scale grid implementations. The pricing of lithium-based systems, particularly in the domains of portable devices and electric cars, is competitive because to their quick improvements and decreasing costs. Hydrogen-based systems, despite their initial expensive capital expenditure, exhibit potential through continuous advancements and the expansion of production capacity. [ABSTRACT FROM AUTHOR]

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a cascade approach based on a sliding mode observer (SMO) for estimating the state of charge (SoC) and state of health (SoH) of lithium-ion (Li-ion) batteries using a single particle model (SPM). After convergence, the observation error signal of the current node in the cascade observer is generated from the output injection signal of the previous node's observer. The current node's observer generates its output injection signal, leading to its convergence. This sequential process accurately determines the observed values of each node using only the battery's current and voltage. Subsequently, the SoC and SoH are estimated using observations of lithium-ion concentrations on the surface and inside the battery anode. The accuracy of this approach is validated using Dynamic Stress Test (DST) and Federal Urban Driving Scheme (FUDS) experimental data. A comparative analysis with conventional SMO and Extended Kalman Filter (EKF) algorithms demonstrates the approach's effectiveness and superior performance, confirming its practical applicability. [ABSTRACT FROM AUTHOR]

The existing battery management systems perform many functions, such as simply monitoring the battery's voltage, current, and temperature for the most basic and compensating energy imbalances between battery cells for the most advanced systems. In this last example, the function balancing helps protect the battery from obtaining a better lifespan. However, these systems with such functions remain complex because they involve techniques specific to power electronics and energy conversion. The number of components, implementation complexity, and cost increased. The work presented in this paper fits directly into this context. The main objective is to provide a solution to the problem of battery management and careful pack cell balancing. The proposed system aims to balance the battery pack cells based on the intermediate state of charge by charging or discharging the imbalanced cell. The implementation of the proposed control strategy was for a battery pack composed of five cells under MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Direct current (DC) microgrid has recently gained potential interest since it supports easy integration of distributed generators (DGs) and energy storage devices (ESDs). However, most DGs and ESDs are integrated into the DC bus with the power electronic converter/inverter. Thus, controlling large-scale power electronic-based generators, loads, and ESDs becomes a challenging task. This paper introduces a new control strategy for the DC microgrid to regulate the bus voltage and power sharing among the DGs, ESDs, resistive loads, and constant power loads (CPLs). Each battery's state of charge (SoC) is utilized in the double control loop to determine the charging/discharging speed. Besides, an additional fractional-order proportional-integral voltage compensation loop is introduced to regulate the DC bus voltage. The SoC-based inner loops assist in the automatic balancing of battery charges, while the compensation loop minimizes the DC bus voltage deviation. A detailed small-signal model is derived to design the controller parameter. The optimization approach is applied to the developed model with the objective of reducing the integral square error of the step response. The DC microgrid system with the designed controller is tested with the simulation study. The results depict that the bus voltage deviation is reduced by 63.27% while the SoC balancing is faster for both charging and discharging modes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) nowadays are getting soaring importance in many aspects like agricultural and military fields. A hybrid power system is a promising solution toward high energy density and power density demands for UAVs as it integrates power sources like internal combustion engine (ICE), fuel cell (FC) and lowcapacity lithium-polymer (LIPO) batteries. For robust energy management, accurate state-of-charge (SOC) estimation is indispensable, which necessitates open circuit voltage (OCV) determination and parameter identification of battery. The presented research demonstrates the feasibility of carrying out incremental OCV test and even dynamic stress test (DST) by making use of the hybrid powered UAV system itself. Based on battery relaxation terminal voltage as well as current wave excitation, novel methods for OCV determination and parameter identification are proposed. Results of SOC estimation against DST through adaptive unscented Kalman filter (AUKF) algorithm show that parameters and OCV identified with longer relaxation time don't yield better SOC estimation accuracy. Besides, it also discloses that OCV played the vital role in affecting SOC estimation accuracy. A detailed analysis is presented showing that mean discharging rate and current wave amplitude are the major factors which affect the quality of OCV identified related to SOC estimation accuracy. [ABSTRACT FROM AUTHOR]

There are obstacles to the widespread use of small electric vehicles (EVs) in Rwanda, including concerns regarding the battery range and lifespan. Lithium-ion batteries (LIBs) play an important role in EVs. However, their performance declines over time because of several factors. To optimize battery management systems and extend the range of EVs in Rwanda, it is essential to understand the influence of the driving profiles on lithium-ion battery degradation. This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions under deep discharge (> 80 %). By being aware of these variables, battery failure can be slowed and improved battery performance can be achieved to promote the transition to cleaner transportation in Rwanda for the productive use of energy. The analyzed parameters that affect battery performance are temperature, driving cycles, and state of charge. It was found that the higher the temperature, the higher was the rate of fading. On the other hand, the EVs that operate in the region with higher elevation (hilly region) combined with a flat surface where the riders use their physical forces to propel the E-bike and their batteries lose their capacity rapidly compared to those operating in regions where the energy from the lithium-ion battery assists for the entire mileage. By draining the battery to 10% and charging it to 90% of its initial capacity, the capacity fading decreased by 5%. [ABSTRACT FROM AUTHOR]

Abstract Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.

The global transportation system's need for electrification is driving research efforts to overcome the drawbacks of battery electric vehicles (BEVs). The accurate and reliable estimation of the states of charge (SOC) and health (SOH) of Li-Ion batteries (LIBs) is crucial for the widespread adoption of BEVs. Transformers, cutting-edge deep learning (DL) models, are demonstrating promising capabilities in addressing various sequence-processing problems. This manuscript presents a thorough survey study of previous research papers that introduced modifications in the development of Transformer-based architectures for the SOC and SOH estimation of LIBs. This study also highlights approximately 15 different real-world datasets that have been utilized for training and testing these models. A comparison is made between the architectures, addressing each state using the root mean square error (RMSE) and mean absolute error (MAE) metrics. [ABSTRACT FROM AUTHOR]

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. [ABSTRACT FROM AUTHOR]

Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management. [ABSTRACT FROM AUTHOR]

To simultaneously solve the problems of the state-of-charge (SOC) equalization and accurate current distribution among distributed energy storage units (DESUs) with different capacities in isolated DC microgrids, a multi-storage DC microgrid energy equalization strategy based on the hierarchical cooperative control is proposed. In the primary control layer, the link between the droop coefficient and SOC is established through a logarithmic function, and the droop coefficient is adaptively adjusted according to the SOC value in order to achieve fast SOC equalization. In the secondary control layer, by designing a coordinated state factor, only one PI controller is required to eliminate the influence of the line impedance on the accurate distribution of the output current and the DC bus voltage drop. In the communication layer, local nodes only need to communicate with neighboring nodes without the need for a central controller, and the dynamic consistency algorithm is used to obtain the average value information about the energy storage system (ESS). Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental analysis on the DC microgrid hardware-in-the-loop experimental platform. [ABSTRACT FROM AUTHOR]

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms. [ABSTRACT FROM AUTHOR]

Li-ion batteries determine the lifespan of an electric vehicle. High power and energy density and extensive service time are crucial parameters in EV batteries. In terms of safe and effective usage, a precise cell model and SoC estimation algorithm are indispensable. To provide an accurate SoC estimation, a current- and temperature-dependent SoC estimation algorithm is proposed in this paper. The proposed SoC estimation algorithm and equivalent circuit model (ECM) of the cells include current and temperature effects to reflect real battery behavior and provide an accurate SoC estimation. For including current and temperature effects in the cell model, lookup tables have been used for each parameter of the model. Based on the proposed ECM, the unscented Kalman filter (UKF) approach is utilized for estimating SoC since this approach is satisfactory for nonlinear systems such as lithium-ion batteries. The experimental results reveal that the proposed approach provides superior accuracy when compared to conventional methods and it is promising in terms of meeting electric vehicle requirements. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lithium-ion batteries (LIBs) experience implausible lithium plating, a deterioration in service life, and a decrease in rate performance at different lithium-ion battery operating conditions, regardless of their substantial advancements in energy conservation. Lithium plating on the anode electrode surface escalates its performance deterioration and creates safety risks for LIBs. The main cause of these issues is the limitations in electrochemical kinetics at the electrode and electrolyte interface. In this study, we emphasize the use of a voltage relaxation profile analysis to identify the onset of lithium plating during progressive cycling at various states of charge (SOC) and discharge rates. In addition, we report the utilization of Al2O3 and TiO2 coatings with different weight percentages on graphite as the new anode electrode materials for LIBs. Anode electrodes with small percentages of Al2O3 and TiO2 coatings (0.5 wt.%) on graphite did not exhibit any lithium plating even at 90% SOC at a 4 C discharge rate. This material's ability to inhibit lithium plating has thus been shown to be better than that of a graphite anode electrode alone. High-rate capability and cycle life are also obtained using these anode electrode materials for full cell system. We further demonstrate that differential open-circuit voltage (dOCV) analysis is capable of detecting the onset of Li plating while cycling, suggesting the economic viability of this approach for active Li detection. [ABSTRACT FROM AUTHOR]

Featured Application: The LM-IEKF algorithm proposed in this paper can effectively estimate the state of charge of a lithium-ion battery, and it is suitable for the estimation of an electric vehicle. The error covariance matrix in the IKEF process is modified by the LM algorithm, and it can still maintain a good convergence speed and estimation accuracy in the face of severe current changes. With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs. [ABSTRACT FROM AUTHOR]

A hybrid energy system is made up of intermittent, nonlinear, and fluctuating renewable energy sources like wind and solar. The cost of implementing and maintaining hybrid energy system can be a significant drawback, particularly due to the high upfront investment required for renewable energy infrastructure and energy storage technologies. The demand for electricity, the quantity of energy generated, the climate, and power management are some of the variables that affect how well HES performs. A hybrid renewable power system's energy management is proposed in this manuscript. The proposed method is Mother Optimization Algorithm (MOA). The system is made up of several components that use renewable energy sources, including a solar energy conservation system (SECS), battery storage system (BSS) and a wind energy conservation system (WECS). The proposed method's primary aim is to improves the system's efficiency and to lessen the system's operating expenses. Additionally, an energy management (EM) based on the battery state of charge is established in order to maintain an energy balance in the hybrid power system. The proposed MOA approach controls the DC/AC converter. The specific contributions include enhancing system efficiency and reducing operating expenses in hybrid renewable power systems using the MOA technique and also the practical implications involve promoting economic viability and environmental sustainability while advancing the transition to cleaner energy systems. By then, the operational MATLAB platform has adopted the proposed model, and the current processes are used to calculate its execution. Compared to all other ways, the proposed strategy yields better outcomes like Genetic Algorithm (GA), Mixed Integer Linear Problem and Particle Swarm Optimization. The proposed method cost is 7100$ and the existing methods cost are 8100$, 1100$, 9100$. From the result, the proposed method displays the cost is lower compared to existing methods. [ABSTRACT FROM AUTHOR]

In this paper, an accelerated proximal gradient based forgetting factor recursive least squares (APG-FFRLS) algorithm is proposed for state of charge (SOC) estimation with output outliers. First, a second-order resistance-capacitance (RC) equivalent circuit model is built to reflect the operating characteristics of the battery. Then, the APG method is applied to correct the output outliers. The FFRLS and extended Kalman filtering (EKF) are used to estimate the battery model parameters and SOC interactively. In order to verify the effectiveness of the proposed algorithm, this paper models the Samsung lithium battery and compares the effectiveness of different algorithms in estimating SOC. The experimental results show that the proposed APG-FFRLS-EKF algorithm has higher accuracy. [ABSTRACT FROM AUTHOR]

The rapid proliferation of electric vehicles (EVs) presents both opportunities and challenges for the electrical grid. While EVs offer a promising avenue for reducing greenhouse gas emissions and dependence on fossil fuels, their uncoordinated charging behavior can strain grid infrastructure, thus creating new challenges for grid operators and EV owners equally. The uncoordinated nature of electric vehicle charging may lead to the emergence of new peak loads. Grid operators typically plan for peak demand periods and deploy resources accordingly to ensure grid stability. Uncoordinated EV charging can introduce unpredictability and variability into peak load patterns, making it more challenging for operators to manage peak loads effectively. This paper examines the implications of uncoordinated EV charging on the electric grid to address this challenge and proposes a novel dynamic optimization algorithm tailored to manage EV charging schedules efficiently, mitigating grid power peaks while ensuring user satisfaction and vehicle charging requirements. The proposed "Proof of Need" (PoN) charging algorithm aims to schedule the charging of EVs based on collected data such as the state of charge (SoC) of the EV's battery, the charger power, the number of connected vehicles per household, the end-user's preferences, and the local distribution substation's capacity. The PoN algorithm calculates a priority index for each EV and coordinates the charging of all connected EVs at all times in a way that does not exceed the maximum allocated power capacity. The algorithm was tested under different scenarios, and the results offer a comparison of the charging power demand between an uncoordinated EV charging baseline scenario and the proposed coordinated charging model, proving the efficiency of our proposed algorithm, thus reducing the charging demand by 40.8% with no impact on the overall total charging time. [ABSTRACT FROM AUTHOR]

In the present paper is analysed the possibility of transforming the current ship electrical energy system of an oil/chemical tanker from the current generation of ships in operation into a hybrid ship electrical energy system by installing electrical energy storage systems. Storage systems made up of Lithium-Ion type batteries offer the possibility of using diesel generators in optimal mode with minimum specific fuel consumption values, achieving reductions in fuel consumption. By transforming ship electrical energy network into hybrid electrical energy system, the operating time of diesel generators is reduced to less than half that leads to significant reductions in maintenance costs of diesel generators. [ABSTRACT FROM AUTHOR]

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Lithium-ion battery energy storage systems are rapidly gaining widespread adoption in power systems across the globe. This trend is primarily driven by their recognition as a key enabler for reducing carbon emissions, advancing digitalization, and making electricity grids more accessible to a broader population. In the present study, we investigated the dynamic behavior of lithium-ion batteries during the charging and discharging processes, with a focus on the impact of terminal voltages and rate parameters on the state of charge (SOC). Through modeling and simulations, the results show that higher terminal charging voltages lead to a faster SOC increase, making them advantageous for applications requiring rapid charging. However, large values of voltage-sensitive coefficients and energy transfer coefficients were found to have drawbacks, including increased battery degradation, overheating, and wasted energy. Moreover, practical considerations highlighted the trade-off between fast charging and time efficiency, with charging times ranging from 8 to 16 min for different rates and SOC levels. On the discharging side, we found that varying the terminal discharging voltage allowed for controlled discharging rates and adjustments to SOC levels. Lower sensitivity coefficients resulted in more stable voltage during discharging, which is beneficial for applications requiring a steady power supply. However, high discharging rates and sensitivity coefficients led to over-discharging, reducing battery life and causing damage. These new findings could provide valuable insights for optimizing the performance of lithium-ion batteries in various applications. [ABSTRACT FROM AUTHOR]

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This paper studies the effect of flow rate control modes on VRB performance based on a validated numerical model. Four modes were put forward, i.e., constant flow rate, variable flow rate with equal anolyte and catholyte (Variable modes I and III) and variable flow rate with unequal anolyte and catholyte (Variable mode II). Under the optimal condition (80 mA/cm2 current and 85% state of charge, SOC), the charging efficiency under the Variable mode II is 0.59% higher than that under the constant flow, while 0.45% and 0.85% higher than those under Variable modes I and III, respectively. The charging efficiency improvement could be largely ascribed to anolyte and catholyte flow adjustment independently, which balances the pump power needed and concentration overpotential loss, benefitting the reduction of total power loss. Moreover, when the charging current is 120 mA/cm2, the maximum SOC of tank under Variable modes I, II, and III increase by 7.63%, 6.41%, and 13%, respectively, in comparison with that under the constant flow rate. The significant increase under Variable mode III is due to an introduction of new objective function (pseudo power loss function), which limits the concentration overpotential difference between anolyte and catholyte, extending time to reach cut-off voltage. [ABSTRACT FROM AUTHOR]

As the demand for electric vehicles (EVs) rises globally, ensuring the safety and reliability of EV battery systems becomes paramount. Accurately predicting the state of health (SoH) and state of charge (SoC) of EV batteries is crucial for maintaining their safe and consistent operation. This paper introduces a novel approach leveraging deep learning methodologies to predict battery SoH, focusing on implementing a system prototype for real-world applications. The proposed system integrates an extended Kalman filter (EKF) with a deep learning framework, forming a system prototype known as FELL, aimed at EV battery diagnosis and prediction. We devise an algorithm utilizing the EKF to estimate the SoH of the battery. We present a detailed overview of the system architecture and implementation, showcasing its predictive capabilities. Experimental results demonstrate the effectiveness of the system in accurately estimating battery SoH with notable improvements in prediction accuracy. Additionally, the FELL system provides users with real-time predictions and comparative analysis across multiple prediction models, offering valuable insights for EV battery management. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The strain on the cell casing can serve as an indicator of the internal state of the cell. Monitoring strain during the charging and discharging processes aids in determining optimal charging and discharging operations, thereby maximizing the lifespan and performance of the battery. Herein, strain dynamic curves are obtained for lithium‐ion batteries at low, medium, and high charge and discharge rates by affixing strain gauges to individual cells. The investigation delves into parameters such as strain relaxation time, maximum strain, and residual strain at various charge rates and states of charge. The experimental findings reveal distinctive patterns, indicating that the strain curve during high‐rate charging resembles a second‐order function, exhibiting more pronounced fluctuations as the rate increases. This stands in stark contrast to the strain exponential decay observed during conventional medium and low‐rate charging. Notably, the strain residual resulting from high‐rate charging proves to be several times higher than that observed in low‐rate charging, hinting at potential differences in the dynamic distribution of lithium ions within batteries during high‐rate versus medium‐low‐rate charging modes. [ABSTRACT FROM AUTHOR]

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs. [ABSTRACT FROM AUTHOR]