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Energy storage devices (ESD) which are intended to power electronic devices, used in close contact of human skin, are desirable to be safe and non-toxic. In light of this requirement, Zn based energy storage devices seem to provide a viable pathway as they mostly employ aqueous based electrolytes which are safe and non-toxic in their functioning. Additionally, having a flexible ESD will play a crucial role as it will enable the ESD to conform to the varying shapes and sizes of wearable electronics which they energize. In this work, we have developed an inkjet-printed Zinc ion battery (IPZIB) with planar electrode configuration over bond paper substrate. Zn has been used as the negative electrode, MnO2 is used as the positive electrode with Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) as the active binder. Conducting tracks of reduced graphene oxide (rGO) are used to construct the current collector on the paper substrate. The fabricated IPZIB delivered a high discharge capacity of 300.14 mAh g−1 at a current density of 200 mA g−1. The energy density of the IPZIB is observed as 330.15 Wh kg−1 at a power density of 220 W kg−1 and retains an energy density of 94.36 Wh kg−1 at a high power density of 1650 W kg−1. Finally, we have demonstrated the capability of the IPZIB to power a LED at various bending and folding conditions which indicates its potential to be used in the next generation flexible and wearable electronic devices. [ABSTRACT FROM AUTHOR]

Wearable electronic energy storage devices usually require the integration of flexible mechanisms and uncompromising electrochemistry of lithium-ion batteries (LIBs). Most of the studies in this field have mainly focused on enhancing the electrochemical properties of the material, while the significance of its flexibility and mechanical properties has been relatively ignored. In this study, the 20 wt % carbon fibers (CFs) composite N-doped flexible carbon nanofiber paper (NFCFP) was prepared through a facile PFI refiner and Bauer sieve of pulping and papermaking machinery sieve-assisted methods combined with annealing technology. The NFCFP-700 with high pyridine N content shows good ion transport and storage performance, the specific capacity remained 308 mAh⸱g−1 after 100 cycles at 0.1 C, and even at 1 C, the specific capacity of the electrode material instead increases constantly at 142 mAh⸱g−1. According to density functional theory (DFT) calculations, the coexistence of pyridine N content and defects in carbon materials was found to enhance the adsorption energy (E ad) of lithium, and the N4V2 locally show the most negative electrostatic potential (ESP) distribution, which was more favorable for the adsorption of Li+ being positively charged. This insight from DFT studies combined with experimental data emphasizes the favorable impact of pyridine N content and defects on the lithium adsorption process in carbon materials, also providing valuable information for the design and optimization of materials for lithium storage applications. [ABSTRACT FROM AUTHOR]

Cellulose is now considered as an appealing environment friendly material for the development of sustainable separators for rechargeable batteries. This study thus aims to develop high performance paper based ceramic separators with superior electrolyte wettability (>170 %), high thermal stability (>200 °C) and excellent flame safety. While developing paper based ceramic separator, extensive studies are carried out to understand thenature and functionality of different nano-structured ceramic materials (Al 2 O 3 , BaTiO 3 and ZrO 2) impregnated in paper matrix. Nano-ZrO 2 facilitates an effective pathway for Li +-ion transport (t Li + = 0.51), whereas BaTiO 3 and Al 2 O 3 ceramics show moderate Li +-ion transport properties (t Li+ of 0.29 and 0.30 respectively). Electrochemical Impedance Spectroscopy (EIS) measurement clearly reveals that ZrO 2 exhibits more interfacial compatibility with the electrodes (MCMB and LiNi 1/3 Mn 1/3 Co 1/3 O 2) compared to the other two ceramic separators during full cell operation. The developed paper separators show excellent electrochemical properties in terms of cycling (tested 300 cycles), multi-electrode compatibility and rate capabilities at different current densities of 0.1–1.2 mAcm−2. The pre- and post-electrochemical EIS data reveal that ZrO 2 based impregnation offers significantly lower charge transfer resistance compared to the other two separators. The study thus demonstrates a real promise for its successful application in Li-ion battery towards achieving sustainability and safety. [Display omitted] • Sustainable, flexible and high safety ceramic based paper separators for LIBs. • Separators were developed using easy to scale up wet-coating process. • Ceramic impregnation affects physical and electrochemical properties of the separators. • Paper separators show superior electrolyte wettability and high thermal stability. • Better electrochemical performances and flame safety are other important advantages. [ABSTRACT FROM AUTHOR]

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium- ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation. [ABSTRACT FROM AUTHOR]

The state of health (SOH) of lithium-ion batteries is an important indicator for evaluating the degradation of battery performance, which is crucial in battery management systems. With the development of science and technology, data-driven models used to predict SOH are widely used, while data-driven models generally suffer from a narrow estimation range, long prediction time, and poor accuracy. Therefore, this paper introduces the extreme learning machine model with a single hidden layer that incorporates hybrid kernel functions, which overcomes the mapping defect problem of single kernel extreme learning machine and extreme learning machine by the introduction of hybrid kernel functions and improves the range and speed of data-driven models for SOH prediction. In addition, to automatically find the optimal parameters of the kernel extreme learning machine to improve the accuracy, the dung beetle algorithm with strong parameter finding ability is used to optimize the model in this paper. Meanwhile, based on the algorithm, we introduced the optimal Latin hypercube idea and weight factor for initializing the population and regulating the position update, respectively, which effectively improved the convergence and parameter-seeking ability of the algorithm to improve the accuracy of the model. The experiments verified the reasonableness and effectiveness of the proposed model, in which the optimal results of root mean square error, average absolute percentage error, and coefficient of determination of the proposed model on the battery dataset were 0.177%, 0.046%, and 99.76%, respectively, which reflected the high-precision prediction ability and strong robustness. [ABSTRACT FROM AUTHOR]

Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The accurate estimation of lithium-ion battery state of charge (SOC) is the key to ensuring the safe operation of energy storage power plants, which can prevent overcharging or over-discharging of batteries, thus extending the overall service life of energy storage power plants. In this paper, we propose a robust and efficient combined SOC estimation method, GRU-ASG, which combines the gated recurrent unit (GRU) neural network and the adaptive Savitzky-Golay filter (ASG). Firstly, the "many-to-one" structure GRU is used to establish the mapping model between the battery-measured variables (voltage, current, temperature) and SOC, and achieve the SOC initial estimation. Then, the output SOC of the GRU network is smoothed online using the Spielman coefficient–based ASG filtering algorithm proposed in this paper to reduce the fluctuation of SOC. Finally, the accurate and stable estimated SOC is obtained. This paper uses six different operating condition datasets collected from an energy storage plant during the discharge process and uses four of them as training datasets and the remaining two as test datasets. The results show that the proposed method can select the optimal window length online adaptively to smooth the initial estimate of SOC. Moreover, the estimation accuracy of the proposed method is the highest compared to the single GRU network and the GRU network with a combination of other filtering algorithms. In particular, the mean square error (MSE) is less than 0.15% and the mean absolute error (MAE) is less than 3% for the two test sets. [ABSTRACT FROM AUTHOR]

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in modern society as important energy storage devices due to their high energy density, rechargeable performance, and light weight. However, the capacity and performance of lithium-ion batteries gradually degrade with the number of charge or discharge cycles and environmental conditions, which can affect the reliability and lifetime of the batteries, so it is necessary to accurately evaluate their health. The belief rule base (BRB) model is an evaluation model constructed based on rules that can handle uncertainties in the operation of lithium-ion batteries. However, lithium-ion batteries may be affected by disturbances from internal or external sources during operation, which may affect the evaluation results. To prevent this problem, this paper proposes a disturbance-considering BRB modeling approach that considers the possible effects of disturbances on the battery in the operating environment and quantifies the disturbance-considering capability of the assessment model in combination with expert knowledge. Second, robustness and interpretability constraints are added in this paper, and an improved optimization algorithm is constructed that maintains or possibly improves the resistance of the model to disturbance. Finally, using the lithium-ion batteries provided by the National Aeronautics and Space Administration (NASA) Prediction Centre of Excellence and the University of Maryland as a case study, this paper verifies that the proposed modeling approach is capable of constructing robust models and demonstrates the effectiveness of the improved optimization algorithm. [ABSTRACT FROM AUTHOR]

Although possessing well‐defined nanostructures and excellent multi‐electron redox properties, polyoxometalate clusters have poor intrinsic electrical conductivity and are prone to aggregation due to large surface energy, which makes them difficult to be fully utilized when applying as electrode materials for lithium‐ion batteries. In this paper, monodisperse K7MnV13O38 (MnV13) clusters are achieved by rationally utilizing nano‐sized high conductive carbon dots (CDs) as stabilizers. Benefiting from the fully exposed redox sites of MnV13 clusters (high utilization rate) and sufficient interfaces with carbon dots (extra interfacial energy storage), the optimized MnV13/10CDs anode delivers a high discharge capacity up to 1348 mAh g−1 at a current density of 0.1 A g−1 and exhibits superb rate/cycling capabilities. Density functional theory (DFT) calculations verify that ionic archway channels are formed between MnV13 and CDs, eliminating the bandgap and greatly improving the electron/ion conductivity of MnV13 and CDs. This paper paves a brand‐new way for synthesis of monodisperse clusters and maximization of extra interfacial energy storage. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method's efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies. [ABSTRACT FROM AUTHOR]

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system. [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]

Accurate prediction of remaining useful life (RUL) plays an important role in maintaining the safe and stable operation of Lithium-ion battery management systems. Aiming at the problem of poor prediction stability of a single model, this paper combines the advantages of data-driven and model-based methods and proposes a RUL prediction method combining convolutional neural network (CNN), bi-directional long and short-term memory neural network (Bi-LSTM), SE attention mechanism (AM) and adaptive unscented Kalman filter (AUKF). First, three types of indirect features that are highly correlated with RUL decay are selected as inputs to the model to improve the accuracy of RUL prediction. Second, a CNN-BLSTM-AM network is used to further extract, select and fuse the indirect features to form predictive measurements of the identified degradation metrics. In addition, we introduce the AUKF model to increase the uncertainty representation of the RUL prediction. Finally, the method is validated on the NASA dataset and the CALCE dataset and compared with other methods. The experimental results show that the method is able to achieve an accurate estimation of RUL, a minimum RMSE of up to 0.0030, and a minimum MAE of up to 0.0024, which has high estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

Environmental issues have driven the booming development of lithium-ion battery technology, and improving the accuracy of state-of-charge (SOC) measurements will play an important role in prolonging battery cycle life and improving safety. In this paper, an improved genetic-back propagation network construction strategy is built to stably and accurately estimate the SOC at different temperatures. The design of the established SOC neural network estimation model is optimized by using a genetic algorithm to initialize the weights and thresholds of the backpropagation (BP) neural network. Then, the above algorithms are validated using Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) datasets. The experimental results show that the MAE and RMSE of the SOC estimation results based on the BBDST dataset are 0.0133, 0.0143 and 0.0058, 0.0082 under the conditions of − 5 °C and 35 °C. Therefore, the improved genetic-back propagation network construction algorithm has a more stable iterative process and higher estimation accuracy, which provides a new method for SOC performance estimation of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry. [ABSTRACT FROM AUTHOR]

This paper describes the design of a control unit for efficient battery charge management in battery electric vehicles (BEVs). The system design aims at controlling the performance of the charging process of dual lithium-ion battery blocks in electric vehicles, with a main battery that powers the vehicle and an auxiliary one for servicing the ancillary equipment. In this paper, we design and analyze the protocol of a control unit that operates and regulates the battery charge in electric vehicles to obtain optimum performance. The so-designed system enhances the battery charge process and protects the main battery from capacity reduction, thus enlarging the driving range of the electric vehicle. We design a specific protocol for an electric circuit that reproduces the structure of the battery charge system of an electric vehicle. The control system improves the efficiency of the auxiliary battery charge by 4.5%. The theoretical simulation matches experimental values in a simulation test by 98.4%. [ABSTRACT FROM AUTHOR]

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise. [ABSTRACT FROM AUTHOR]

The open-circuit voltage (OCV) curve has a significant influence on the accuracy of the state of charge (SOC) estimation based on equivalent circuit models (ECMs). However, OCV curves are tested through offline experiments and are hard to be very accurate because they constantly change with the test method's ambient temperature and aging status. Recently, researchers have attempted to improve the accuracy of OCV curves by increasing the volume of sample data or updating/reconstructing the curve combined with practical operation data. Still, prior offline tests are essential, and experimental errors inevitably exist. Consequently, a SOC estimation method without any offline OCV tests might be an efficient route to improve the accuracy of SOC. According to this idea, this paper presents a novel method for SOC estimation, which is based on online OCV curve construction. Meanwhile, a stepwise multi-timescale parameter identification algorithm is designed to improve the interpretability and precision of the estimated ECM parameters. The results demonstrate that the maximum SOC estimation error is only 0.05% at 25 °C, indicating good robustness under various ambient temperatures and operational conditions. [ABSTRACT FROM AUTHOR]

This paper investigates modeling techniques for the mathematical representation of HV (high-voltage) Li-ion batteries to be used in conjunction with battery emulators for the test cell environment. To enable the impact of the battery response to be assessed in conjunction with other electrified systems, battery emulators are used with advanced mathematical models describing the expected voltage output with respect to current load. This paper conducted research into different modeling types: electrochemical, thermal, and electronic equivalent circuit models (EECMs). EECMs were identified as the most suitable to be used in conjunction with emulation techniques. A foundation EECM was created in conjunction with a thermal part to simulate thermal dependency. Hybrid Pulse Power Characterization (HPPC) tests were conducted on an NMC Li-ion cell across a range of temperatures from −20 °C to 25 °C. Using parameter optimization techniques, the HPPC test data were used to identify the resistance, capacitance, and the open-circuit voltage of the cell across a range of state of charge bounds and across a temperature range of 0 °C to 25 °C. The foundation model was assessed using identified parameters on two current profiles derived from drive cycles across a temperature range of 0 °C to 10 °C. The FMU (Functional Mockup Unit) model format was determined as the required interface for an AVL battery emulator. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects. [ABSTRACT FROM AUTHOR]

This paper presents an experimental comparison of two types of Li-ion battery stacks for low-voltage energy storage in small urban Electric or Hybrid Electric Vehicles (EVs/HEVs). These systems are a combination of lithium battery cells, a battery management system (BMS), and a central control circuit—a lithium energy storage and management system (LESMS). Li-Ion cells are assembled with two different active cathode materials, nickel–cobalt–aluminum (NCA) and lithium iron phosphate (LFP), both with an integrated decentralized BMS. Based on experiments conducted on the two assembled LESMSs, this paper suggests that although LFP batteries have inferior characteristics in terms of energy and power density, they have great capacity for improvement. [ABSTRACT FROM AUTHOR]

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology trends and guide future development. The research employed statistical tools to analyze patent activities, including the frequency and scope of patent filings, and comparative analysis to highlight differences between countries. This study reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China's large number of patents in battery manufacturing processes contrasts with the USA's focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The novelty of this paper lies in its detailed comparative analysis of patent trends across these three major economies, providing insights into the technological focuses and priorities of each country. The study also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector. [ABSTRACT FROM AUTHOR]

This paper proposes a high-efficiency and compact fuel cell–battery hybrid power system without DC/DC converters. Generally, fuel cells supply power to charge lithium batteries or loads using DC/DC converters. The disadvantages of a DC/DC converter are its complex design, poor efficiency, and large volume. Therefore, improvements in the volume, weight, and efficiency are the main objectives of the proposed topology, which is suitable for stable operation in power equipment. This paper proposes a novel topology without DC/DC converters for a fuel cell–battery hybrid forklift system and analyzes, discusses, and verifies it with experimental measurements. Additionally, the proposed topology uses an average charging method to charge the Li-ion battery. The dynamic response of fuel cells is slower than that of Li-ion batteries. By properly configuring the voltages of a fuel cell and a lithium battery, we propose a hybrid system that can maintain a stable output and high efficiency in different operating modes without DC/DC converters. Detailed efficiency calculations and comparisons reveal that the method proposed in this paper achieves an efficiency increase of 5.36% compared with traditional approaches, while maintaining a set charging current. The proposed topology and charging method are verified with experiments on a 10 kW fuel cell–battery system, and the results indicate that the proposed method without DC/DC converters is more suitable for hybrid applications than traditional methods. The proposed system achieves optimal efficiency of 98.27%, surpassing the performance of a traditional hybrid system employing regulated DC/DC converters. Additionally, the system incorporates a mechanism to achieve constant current control, ensuring precise control over the desired charging current. The error in the desired charging current, determined through the average charging method, is 5%. [ABSTRACT FROM AUTHOR]

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively. [ABSTRACT FROM AUTHOR]

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles. [ABSTRACT FROM AUTHOR]

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles. [ABSTRACT FROM AUTHOR]

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability. [ABSTRACT FROM AUTHOR]

Highlights: Research progress of advanced optical fiber sensors in traction batteries and energy storage batteries is summarized. The embedded application mechanisms of different optical fiber sensors in batteries are discussed. Advanced optical fiber sensors adapting to batteries with diverse materials are reviewed. Advanced optical fiber sensors driving the development of future smart batteries are prospected. The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist. Now the battery is still a "black box", thus requiring a deep understanding of its internal state. The battery should "sense its internal physical/chemical conditions", which puts strict requirements on embedded sensing parts. This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed, focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano–micro-level battery material structural phase transition, electrolyte degradation, electrode–electrolyte interface dynamics to three-dimensional macro-safety evolution. The paper contributes to understanding how to use optical fiber sensors to achieve "real" and "embedded" monitoring. Through the inherent advantages of the advanced optical fiber sensor, it helps clarify the battery internal state and reaction mechanism, aiding in the establishment of more detailed models. These advancements can promote the development of smart batteries, with significant importance lying in essentially promoting the improvement of system consistency. Furthermore, with the help of smart batteries in the future, the importance of consistency can be weakened or even eliminated. The application of advanced optical fiber sensors helps comprehensively improve the battery quality, reliability, and life. [ABSTRACT FROM AUTHOR]

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components. [ABSTRACT FROM AUTHOR]

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

Highlights: Recent progress in Zn–air batteries is critically reviewed. Current challenges of rechargeable Zn–air batteries are highlighted. Strategies for the advancement of the anode, electrolyte, and oxygen catalyst are discussed. Future research directions are provided to design commercial Zn–air batteries. Zinc–air batteries (ZABs) are gaining attention as an ideal option for various applications requiring high-capacity batteries, such as portable electronics, electric vehicles, and renewable energy storage. ZABs offer advantages such as low environmental impact, enhanced safety compared to Li-ion batteries, and cost-effectiveness due to the abundance of zinc. However, early research faced challenges due to parasitic reactions at the zinc anode and slow oxygen redox kinetics. Recent advancements in restructuring the anode, utilizing alternative electrolytes, and developing bifunctional oxygen catalysts have significantly improved ZABs. Scientists have achieved battery reversibility over thousands of cycles, introduced new electrolytes, and achieved energy efficiency records surpassing 70%. Despite these achievements, there are challenges related to lower power density, shorter lifespan, and air electrode corrosion leading to performance degradation. This review paper discusses different battery configurations, and reaction mechanisms for electrically and mechanically rechargeable ZABs, and proposes remedies to enhance overall battery performance. The paper also explores recent advancements, applications, and the future prospects of electrically/mechanically rechargeable ZABs. [ABSTRACT FROM AUTHOR]

Traditional methods for developing new materials are no longer sufficient to meet the needs of the human energy transition. Machine learning (ML) artificial intelligence (AI) and advancements have caused materials scientists to realize that using AI/ML to accelerate the development of new materials for batteries is a powerful potential tool. Although the use of certain fixed properties of materials as descriptors to act as a bridge between the two separate disciplines of AI and materials chemistry has been widely investigated, many of the descriptors lack universality and accuracy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning logic of AI/ML has become mandatory for materials scientists to develop more accurate descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic of AI and machine learning to help materials developers understand their operational mechanisms. Meanwhile, the paper also compares the accuracy of different descriptors and their advantages and disadvantages and highlights the great potential value of accurate descriptors in AI/machine learning applications for battery research, as well as the challenges of developing accurate material descriptors. [ABSTRACT FROM AUTHOR]

Effective charging techniques must consider factors such as charging efficiency, lifecycle, charging time (CT), and battery temperature. Currently, most charging strategies primarily focus on CT and charging losses (CL), overlooking the crucial influence of battery temperature on battery life. Therefore, this study proposes a constant temperature–constant voltage (CT-CV) charging method based on minimizing energy losses. The charging process is primarily divided into three stages. In the first stage, a constant current (CC) charging is implemented using a 2C rate that aims to expedite battery charging but may result in a rapid temperature increase. The second stage involves constant temperature charging, where the charging current is regulated based on battery temperature feedback using a PID controller to maintain a stable battery temperature. The third stage is constant voltage (CV) charging, where a fixed current is applied continuously until the current drops below the charging cutoff current. After completion of the charging process, the charging time can be calculated, and charging losses can be determined by incorporating the battery equivalent circuit model (ECM). To determine the optimal transition time, the paper employs Coulomb counting and the battery ECM, considering both CT and losses to simulate the transition time with minimal CL. This approach achieves optimization of transition points by establishing ECM, measuring internal impedance of the battery, and simulating various charging scenarios, and eliminates the need for multiple actual experiments. Experimental results show that the charging time (CT) should be reduced and the maximum temperature rise (TR) should be reduced under the same average TR condition of the proposed method. At the same CT, the average TR and the maximum TR should both decrease. The charging method proposed in this study exhibits the following advantages: (1) simultaneous consideration of the battery's equivalent circuit model and charging time; (2) the achieved transition point demonstrates characteristics of minimized charging losses; (3) eliminates the need for multiple experimental iterations. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the thermal management strategies employed in cylindrical lithium-ion battery packs, with a focus on enhancing performance, safety, and lifespan. Effective thermal management is critical to retain battery cycle life and mitigate safety issues such as thermal runaway. This review covers four major thermal management techniques: air cooling, liquid cooling, phase-change materials (PCM), and hybrid methods. Air-cooling strategies are analyzed for their simplicity and cost-effectiveness, while liquid-cooling systems are explored for their superior heat dissipation capabilities. Phase-change materials, with their latent heat absorption and release properties, are evaluated as potential passive cooling solutions. Additionally, hybrid methods, such as combining two or more strategies, are discussed for their synergistic effects in achieving optimal thermal management. Each strategy is assessed in terms of its thermal performance, energy efficiency, cost implications, and applicability to cylindrical lithium-ion battery packs. The paper provides valuable insights into the strengths and limitations of each technique, offering a comprehensive guide for researchers, engineers, and policymakers in the field of energy storage. The findings contribute to the ongoing efforts to develop efficient and sustainable thermal management solutions for cylindrical lithium-ion battery packs in various applications. [ABSTRACT FROM AUTHOR]

This paper deals with the analyses of batteries used in current military systems to power the electric drives of military vehicles. The article focuses on battery analyses based on operational data obtained from measurements rather than analyses of the chemical composition of the tested batteries. The authors of the article used their experience from the development test-laboratory of military technology. This article presents a comparative analysis of existing and promising technologies in the field of energy storage and buffering for military electric vehicles. The overview of these technologies, including the design, operating principles, advantages, and disadvantages, are briefly presented to produce theoretical comparative analyses. However, this article mainly focuses on the experimental verification of operational ability in varied conditions, as well as the comparison and analysis of these results. The main part of the article provides more experimental studies on technologies of energy storage and buffering using the results of several experiments conducted to demonstrate the behavior of each technology in different working conditions. The output parameters, as well as the state of charge of each technology's samples, were surveyed in various temperatures and loading characteristics. The results presented in this paper are expected to be useful for optimizing the selection of energy storage and buffering solutions for military electric vehicles in different applications and functional environments. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) are a widely used energy storage technology as they possess high energy density and are characterized by the reversible intercalation/deintercalation of Li ions between electrodes. The rapid development of LIBs has led to increased production efficiency and lower costs for manufacturers, resulting in a growing demand for batteries and their application across various industries, particularly in different types of vehicles. In order to meet the demand for LIBs while minimizing climate-impacting emissions, the reuse, recycling, and repurposing of LIBs is a critical step toward achieving a sustainable battery economy. This paper provides a comprehensive review of lithium-ion battery recycling, covering topics such as current recycling technologies, technological advancements, policy gaps, design strategies, funding for pilot projects, and a comprehensive strategy for battery recycling. Additionally, this paper emphasizes the challenges associated with developing LIB recycling and the opportunities arising from these challenges, such as the potential for innovation and the creation of a more sustainable and circular economy. The environmental implications of LIB recycling are also evaluated with methodologies able to provide a sustainability analysis of the selected technology. This paper aims to enhance the comprehension of these trade-offs and encourage discussion on determining the "best" recycling route when targets are in conflict. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive exploration of float current analysis in lithium-ion batteries, a promising new testing method to assess calendar aging. Float currents are defined as the steady-state trickle charge current after a transient part. In the literature, a correlation to capacity loss was reported. Assuming the float current compensates for the voltage decay over time and is linked to calendar aging, effects from voltage slippery must be considered. The dU/dQ analysis suggests solely a loss of active lithium. Therefore, we investigate the solid electrolyte interphase (SEI) growth as the general aging mechanism to explain the origin of float currents. Our results show that the voltage slippery theory holds true within the low to middle test voltage ranges. However, the theory's explanatory power begins to diminish at higher voltage ranges, suggesting the existence of additional, yet unidentified, factors influencing the float current. A shuttle reaction or lithiation of the cathode by electrolyte decomposition are the most promising alternative aging mechanisms at high voltages. The paper proposes a unique voltage slippery model to check for correlations between aging mechanisms, the float current test and the check-up test. For a better understanding, test strategies are proposed to verify/falsify the aging mechanisms beyond SEI. [ABSTRACT FROM AUTHOR]

Sodium‐ion batteries (SIBs) work similarly to lithium‐ion batteries, but they cost less and are safer. However, the battery has some shortcomings, such as low energy density and poor stability, which hinder its development and application. The development of electrode materials with low cost and high‐performance characteristics is a hot and difficult point in the research of SIBs. In this paper, the existing sodium ion cathode and anode materials are systematically reviewed. Firstly, the electrochemical structures and properties of various cathode and anode materials were introduced, and the limitations of cathode and anode materials of SIBs were analyzed. Based this, the current modification methods were summarized, such as surface coating of materials, doping of heteroatoms and metal elements to improve the electrochemical performance. In this paper, the research progress and modification measures of SIBs are summarized and prospected. It is helpful to further improve its comprehensive performance and lay a foundation for the future application of cathode and anode materials for SIBs. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period. [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]

In response to the dual carbon policy, the proportion of clean energy power generation is increasing in the power system. Energy storage technology and related industries have also developed rapidly. However, the life-attenuation and safety problems faced by energy storage lithium batteries are becoming more and more serious. In order to clarify the aging evolution process of lithium batteries and solve the optimization problem of energy storage systems, we need to dig deeply into the mechanism of the accelerated aging rate inside and outside the lithium ion from the perspective of the safety and stability of a lithium battery in view of the complex and changeable actual working conditions during the operation of the battery. This paper takes a lithium-iron phosphate battery and a lithium-ion battery as examples to analyze. According to the specific scene of lithium battery operation, the actual operating conditions of lithium battery environmental impact factors and attenuation mechanisms are described in detail. The damage to the internal structure of lithium batteries was systematically analyzed. Furthermore, the correlation between the external influencing factors and the aging rate of lithium batteries under the coupling effect of internal failure mechanisms is analyzed. Finally, future energy storage failure analysis technology is anticipated, hoping to play a positive role in promoting the development of energy storage and lithium battery failure analysis technology. [ABSTRACT FROM AUTHOR]

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction. [ABSTRACT FROM AUTHOR]

Since solid electrolytes have a broad electrochemical stability window, are exceptionally electrochemically stable against Li metal, and function as a physical separator to prevent dendrite growth, they are at the forefront of alternate possibilities, further increasing the stability and energy density of Li-ion batteries. NASICON-type electrolytes are a promising candidate due to their negligible moisture sensitivity, which results in outstanding stability and a lower probability of Li2CO3 passivity under the ambient atmosphere. However, one of the most promising representatives, Li1+xYxZr2−x(PO4)3 (LYZP), has multiple stable phases with significant variation in their corresponding Li-ion conductivity. In this paper, we have successfully synthesized the highly ionically conductive rhombohedral phase of LYZP via spray-flame synthesis. Two different solvent mixtures (e.g., 2-ethyl hexanoic acid/ethanol, propanol/propanoic acid) were chosen to explore the effect of precursor composition and combustion enthalpy on the phase composition of the nanoparticle. The as-synthesized nanoparticles from spray-flame synthesis consisted of the crystalline tetragonal zirconia (t-ZrO2) phase, while lithium, yttrium, and phosphate were present on the nanoparticles' surface as amorphous phases. However, a short annealing step (1 h) was sufficient to obtain the NASICON phase. Moreover, we have shown the gradual phase conversion from orthorhombic β phase to rhombohedral α phase as the annealing temperature increased from 700 °C to 1300 °C (complete removal of β phase). In this context, Y3+ doping was also crucial, along with the appropriate solvent mixture and annealing temperature, for obtaining the much-desired rhombohedral α phase. Further, 0.2 at% Y3+ doping was added to the solvent mixture of 2-ethyl hexanoic acid/ethanol, and annealing at 1300 °C for 1 h resulted in a high ionic conductivity of 1.14∙10−5 S cm−1. [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]