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Although lithium-ion batteries offer significant potential in a wide variety of applications, they also present safety risks that can harm the battery system and lead to serious consequences. To ensure safer operation, it is crucial to develop a mechanism for assessing battery health and estimating remaining service life, enabling timely decisions on replacement or removal. For this purpose, this paper develops a Long Short-Term Memory (LSTM) neural network model with a Multilayer Perceptron (MLP) layer to predict the state of charge (SOC) of Li-ion batteries. The model consists of three core modules: data processing module, neural network module and analysis plotting module. In contrast to the traditional three-characteristic input model of voltage, current, and battery temperature, this paper pioneers the addition of a new characteristic: the number of charge/discharge cycles to enrich the feature engineering of SOC prediction. In addition, our proposed model utilizes the metric of Mean Square Error (MSE) as the loss function, which is very stable for the scenario of SOC prediction. By processing the dataset and introducing a new application scenario, the accuracy of the SOC prediction has substantially increased. The average SOC forecast error is as low as 0.2% using the aforementioned four input features for a double-layer LSTM and double-layer MLP model, regardless of the level of battery aging. [ABSTRACT FROM AUTHOR]

Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. [ABSTRACT FROM AUTHOR]

With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also cause inaccurate SoC estimation resulting in reduced reliability and performance of battery management systems (BMSs). To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. The TCN and GRU capture both long-term and short-term dependencies and the attention mechanism focuses on important features within input sequences, improving model efficiency. With inputs of voltage, current, and temperature, along with their moving average, the hybrid GRU-TCN-Attention (GTA) model is trained and tested in a range of operating cycles and temperatures. Performance metrics, including average RMSE (root mean squared error), MAE (mean absolute error), MaxE (maximum error), and R 2 score indicates the model is performing well, with average values of 0.512%, 0.354%, 1.98%, and 99.94%, respectively. The proposed model performs well under both high and low noise conditions, with an RMSE of less than 2.18%. The proposed hybrid approach is consistently found to be superior when compared against traditional baseline models. This work offers a potential method for accurate SoC estimation in Li-ion batteries, which has an important impact on clean energy integration and battery management systems in EVs. [ABSTRACT FROM AUTHOR]

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies. [ABSTRACT FROM AUTHOR]

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB's Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability. [ABSTRACT FROM AUTHOR]

This paper presents the development of an advanced battery management system (BMS) for electric vehicles (EVs), designed to enhance battery performance, safety, and longevity. Central to the BMS is its precise monitoring of critical parameters, including voltage, current, and temperature, enabled by dedicated sensors. These sensors facilitate accurate calculations of the state of charge (SOC) and state of health (SOH), with real-time data displayed through an IoT cloud interface. The proposed BMS employs data-driven approaches, like advanced Kalman filters (KF), for battery state estimation, allowing continuous updates to the battery state with improved accuracy and adaptability during each charging cycle. Simulation tests conducted in MATLAB’s Simulink across multiple charging and discharging cycles demonstrate the superior accuracy of the advanced Kalman filter (KF), in handling non-linear battery behaviours. Results indicate that the proposed BMS achieves a significantly lower error margin in SOC tracking, ranging from 0.32% to 1%, compared to traditional methods with error margins up to 5%. These findings underscore the importance of integrating robust sensor systems in BMSs to optimise EV battery management, reduce maintenance costs, and improve battery sustainability.

The state of charge (SoC) is a critical parameter in lithium-ion batteries and their alternatives. It determines the battery's remaining energy capacity and influences its performance longevity. Accurate SoC estimation is essential for making informed charging and discharging decisions, mitigating the risks of overcharging or deep discharge, and ensuring safety. Battery management systems rely on SoC estimation, utilising both hardware and software components to maintain safe and efficient battery operation. Existing SoC estimation methods are broadly classified into direct and indirect approaches. Direct methods (e.g., Coulumb counting) rely on current measurements. In contrast, indirect methods (often based on a filter or observer) utilise a model of a battery to incorporate voltage measurements besides the current. While the latter is more accurate, it faces challenges related to sensor drift, computational complexity, and model inaccuracies. The need for more precise and robust SoC estimation without increasing complexity is critical, particularly for real-time applications. Recently, sliding mode observers (SMOs) have gained prominence in this field for their robustness against model uncertainties and external disturbances, offering fast convergence and superior accuracy. Due to increased interest, this review focuses on various SMO approaches for SoC estimation, including first-order, adaptive, high-order, terminal, fractional-order, and advanced SMOs, along with hybrid methods integrating intelligent techniques. By evaluating these methodologies, their strengths, weaknesses, and modelling frameworks in the literature, this paper highlights the ongoing challenges and future directions in SoC estimation research. Unlike common review papers, this work also compares the performance of various existing methods via a comprehensive simulation study in MATLAB 2024b to quantify the difference and guide the users in selecting a suitable version for the applications. [ABSTRACT FROM AUTHOR]

Effective health management and accurate state of charge (SOC) estimation are crucial for the safety and longevity of lithium-ion batteries (LIBs), particularly in electric vehicles. This paper presents a health management system (HMS) that continuously monitors a 4s2p LIB pack's parameters—current, voltage, and temperature—to mitigate risks such as overcurrent and thermal runaway while ensuring balanced charge distribution between cells. An improved online battery model (IOBM) is developed to enhance SOC estimation accuracy. The system utilises forgetting factor recursive least squares (FFRLS) for real-time parameter updates, an adaptive nonlinear sliding mode observer (ANSMO) for SOC estimation, and a long short-term memory (LSTM) network to dynamically adjust capacity based on operating conditions. Validation using the urban dynamometer driving schedule (UDDS) test demonstrated high accuracy, with the proposed battery model achieving a root mean square error (RMSE) of 12.13 mV and the LSTM achieving an RMSE of 0.0118 Ah. Regular updates to the battery's current capacity, along with the proposed IOBM, significantly improved SOC estimation performance, maintaining estimation errors within 1.08%. [ABSTRACT FROM AUTHOR]

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm. [ABSTRACT FROM AUTHOR]

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.)

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.)

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery's characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs. [ABSTRACT FROM AUTHOR]

Recent studies have focused on accuracy as the key state of charge (SoC) estimation algorithms' performance metrics, whereas just a few of them compare algorithms in terms of energy efficiency. Such a comparison is important when selecting an algorithm that should be implemented on a resource-constrained, low-power embedded system. In this paper, recursive model-based SoC estimation algorithms, such as the extended Kalman filter, have been identified as well-suited solutions for implementation on an embedded platform, providing a good compromise between estimation accuracy and computational complexity that is correlated to energy consumption. Assuming that a decrease in the estimation rate will result in a decrease in both accuracy and energy consumption of the estimator, a theoretical analysis has been conducted to establish how these two metrics depend on the estimation rate. To verify results obtained in theory, two extended Kalman filter-based SoC estimation algorithms of different complexities have been implemented and compared in terms of accuracy, quantified by root mean square error (RMSE), and energy consumption. The obtained results confirm that for a selected type of recursive model-based SoC estimation algorithm, it is possible to achieve an optimal algorithm estimation rate in the sense of satisfactory accuracy and acceptable energy consumption. The analysis and results presented in this paper establish a foundation for a future development of energy-efficient algorithms for SoC estimation in applications where the energy consumption of the estimation process is comparable to the energy consumption of the complete system. [ABSTRACT FROM AUTHOR]

Battery modeling and state of charge (SoC) estimation are critical functions in the effective battery management system (BMS) operation. Temperature directly affects the performance and changes the model accuracy of a battery. Most studies have focused on estimating the internal temperature of the battery from the surface temperature of the battery with the help of sensors. However, due to the high number of cells in battery packs, the increase in sensor costs and the number of parameters have been ignored. Therefore, this article presents a new framework for the temperature effect using the electrical circuit model. The terminal voltage of the battery includes the effect under different operating conditions. This effect was associated with internal resistance in the battery model. The developed temperature-effective battery model was tested at different temperatures and operating currents. The model was validated with a maximum average root mean square error of 0.05% from the test results. The SoC of the LTO battery was estimated with the sigma-point Kalman (SPK) filter incorporating the developed model. The maximum average root mean square error in the estimation results is 0.11%. It is suitable for practical applications due to its low cost, simplicity, and reliability. [ABSTRACT FROM AUTHOR]

This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of batteries to predict the SOC. The LSTM model was trained using battery data collected under various temperature and load conditions. To evaluate the performance of the artificial intelligence model, measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering was utilized. The LSTM model excels in learning long-term dependencies from sequence data, effectively modeling temporal patterns in battery data. The study trained the LSTM model based on battery data collected from various charge and discharge cycles and evaluated the model's performance by epoch to determine the optimal configuration. The proposed model demonstrated high SOC estimation accuracy for various charging and discharging profiles. As training progressed, the model's predictive performance improved, with the predicted SOC moving from 14.8400% at epoch 10 to 12.4968% at epoch 60, approaching the actual SOC value of 13.5441%. Simultaneously, the mean absolute error (MAE) and root mean squared error (RMSE) decreased from 0.9185% and 1.3009% at epoch 10 to 0.2333% and 0.5682% at epoch 60, respectively, indicating continuous improvement in predictive performance. In conclusion, this study demonstrates the effectiveness of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance the performance of battery management systems. [ABSTRACT FROM AUTHOR]

This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF). The low-magnitude noise signals are specifically crafted to be stealthy, evading easy detection while still effectively causing malfunctions in the estimation processes. Additionally, we introduce a verification case using a different battery model and estimation algorithm to enhance generalization. This case involves high-noise signals with defined thresholds for current and voltage noise levels, which cause significant disruptions to Kalman Filters. These signals serve as a complementary example of adversarial attacks, demonstrating how such noise can destabilize estimation algorithms and lead to critical control errors. This dataset is valuable for researchers and engineers aiming to understand and mitigate the effects of smart cyber-physical attacks on BESS. These attacks can disrupt real-time BESS controllers by injecting false input data related to SoC and SoH, leading to physical control manipulations, increased energy costs, inefficiencies in demand-side management, and incorrect day-ahead scheduling, thereby destabilizing grid operations. The data is reusable in studies focused on enhancing the resilience of SoC and SoH estimation methods, as well as in developing robust defensive strategies against smart DRL-based adversarial attacks.

The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process ( Q ) and measurement ( R ) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests. [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.)

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the variable forgetting factor recursive least square (VFFRLS) - unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles. [ABSTRACT FROM AUTHOR]

Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias.

With the rapid global growth in demand for renewable energy, the traditional energy structure is accelerating its transition to low-carbon, clean energy. Lithium-ion batteries, due to their high energy density, long cycle life, and high efficiency, have become a core technology driving this transformation. In lithium-ion battery energy storage systems, precise state estimation, such as state of charge, state of health, and state of power, is crucial for ensuring system safety, extending battery lifespan, and improving energy efficiency. Although physics-based state estimation techniques have matured, challenges remain regarding accuracy and robustness in complex environments. With the advancement of hardware computational capabilities, data-driven algorithms are increasingly applied in battery management, and multi-model fusion approaches have emerged as a research hotspot. This paper reviews the fusion application between physics-based and data-driven models in lithium-ion battery management, critically analyzes the advantages, limitations, and applicability of fusion models, and evaluates their effectiveness in improving state estimation accuracy and robustness. Furthermore, the paper discusses future directions for improvement in computational efficiency, model adaptability, and performance under complex operating conditions, aiming to provide theoretical support and practical guidance for developing lithium-ion battery management technologies.

With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also cause inaccurate SoC estimation resulting in reduced reliability and performance of battery management systems (BMSs). To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. The TCN and GRU capture both long-term and short-term dependencies and the attention mechanism focuses on important features within input sequences, improving model efficiency. With inputs of voltage, current, and temperature, along with their moving average, the hybrid GRU-TCN-Attention (GTA) model is trained and tested in a range of operating cycles and temperatures. Performance metrics, including average RMSE (root mean squared error), MAE (mean absolute error), MaxE (maximum error), and R2 score indicates the model is performing well, with average values of 0.512%, 0.354%, 1.98%, and 99.94%, respectively. The proposed model performs well under both high and low noise conditions, with an RMSE of less than 2.18%. The proposed hybrid approach is consistently found to be superior when compared against traditional baseline models. This work offers a potential method for accurate SoC estimation in Li-ion batteries, which has an important impact on clean energy integration and battery management systems in EVs.

One of the most crucial and pricey parts of electric automobiles is the battery. The state of charge of lithium-ion batteries, which are primarily found in electric vehicles (EV's), is essential to their ongoing functioning. To guarantee precise battery balancing and accurate assessment of the vehicle's remaining driving range, a robust state of charge prediction model is needed. The exact charge level attests to the dependability and safety of electric cars. Nonetheless, state of charge is influenced by a variety of factors that cannot be easily quantified. This work mainly focuses on designing the state of charge of lithium-ion batteries in electric vehicles using a novel deep learning model and a dimensionality reduction mechanism. Initially, the current, voltage, and temperature data are collected from the openly available dataset. After that, normalization is performed on the collected data to standardize the dataset. Moore–Penrose pseudo-inverse (MPPI)-based principal component analysis is then implemented to diminish the dimensions of the standardized data. These dimensions reduced dataset are finally fed into the optimal parameter selection-based long short-term memory model, which predicts the lithium-ion batteries' state of charge. The experiments are carried out employing the Panasonic 18650PF lithium-ion battery dataset. Simulation findings demonstrate that the suggested algorithms can accurately predict the lithium-ion batteries' states in a short period of time with minimal error when contrasted with existing techniques. [ABSTRACT FROM AUTHOR]

Accurate state of charge (SOC) estimation of lithium-ion batteries is a fundamental prerequisite for ensuring the normal and safe operation of electric vehicles, and it is also a key technology component in battery management systems. In recent years, lithium-ion battery SOC estimation methods based on data-driven approaches have gained significant popularity. However, these methods commonly face the issue of poor model generalization and limited robustness. To address such issues, this study proposes a closed-loop SOC estimation method based on simulated annealing-optimized support vector regression (SA-SVR) combined with minimum error entropy based extended Kalman filter (MEE-EKF) algorithm. Firstly, a probability-based SA algorithm is employed to optimize the internal parameters of the SVR, thereby enhancing the precision of original SOC estimation. Secondly, utilizing the framework of the Kalman filter, the optimized SVR results are incorporated as the measurement equation and further processed through the MEE-EKF, while the ampere-hour integral physical model serves as the state equation, effectively attenuating the measurement noise, enhancing the estimation accuracy, and improving generalization ability. The proposed method is validated through battery testing experiments conducted under three typical operating conditions and one complex and random operating condition with wide temperature variations under only one condition training. The results demonstrate that the proposed method achieves a mean absolute error below 0.60% and a root mean square error below 0.73% across all operating conditions, showcasing a significant improvement in estimation accuracy compared to the benchmark algorithms. The high precision and generalization capability of the proposed method are evident, ensuring accurate SOC estimation for electric vehicles.

Abstract Due to the highly complex and non‐linear physical dynamics of lithium‐ion batteries, it is unfeasible to measure the state of charge (SOC) directly. Designing systems capable of accurate SOC estimation has become a key technology for battery management systems (BMS). Existing mainstream SOC estimation approaches still suffer from the limitations of low efficiency and high‐power consumption, owing to the great number of samples required for training. To address these gaps, this paper proposes a memristor‐based denoising autoencoder and gated recurrent unit network (MDGN) for fast and accurate SOC estimation of lithium‐ion batteries. Specifically, the DAE circuit module is designed to extract useful feature representation with strong generalization and noise immunity. Then, the gated recurrent unit (GRU) circuit module is designed to learn the long‐term dependencies between high‐dimensional input and output data. The overall performance is evaluated by root mean square error (RMSE) and mean absolute error (MAE) at 0, 25, and 45°C, respectively. Compared with the current state‐of‐the‐art methods, the entire scheme shows its superior performance in accuracy, robustness, and operation cost (referring to time cost).

The crucial function of Battery management systems in the world of electric vehicles is thoroughly examined in this extensive article. With the popularity of Electric vehicles skyrocketing, accurate state-of-charge estimation has emerged as a critical element for guaranteeing the best battery performance, longevity, and safety. This work conducts a comprehensive analysis of 210 papers by an organized literature study and thematic analysis. It aims to offer a precise description of State charge estimate approaches, identify areas where information is lacking, and highlight the necessary topics for future research. This paper presents a thorough investigation of the evolution and significance of electric cars in the current period, a topic that has not been previously explored by other reviews. During the process of conducting theme analysis, the advantages and difficulties of high-performing techniques for state of charge prediction are examined, and a comprehensive categorization of hybrid approaches with high accuracy is generated. The literature examines several sorts of approaches based on the Kalman filter. Finally, the most effective method for getting the best possible prediction for the State of charge is explained, along with the progression of these algorithms. This paper concludes with a complete review of current trends in state of charge estimate, derived from an examination of several research paths.

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs.