Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

<div class="section abstract"><div class="htmlview paragraph">This paper focuses on the development and validation of predictive models for battery management systems, specifically targeting State of Health (SOH) and State of Charge (SOC) estimation, as well as the design of a comprehensive Battery Management System (BMS). The study begins by establishing and evaluating SOH prediction models, employing both linear regression and Long Short-Term Memory (LSTM) algorithms. Comparative analysis is conducted to assess the prediction accuracy between Recurrent Neural Networks (RNN) and LSTM, highlighting the superior performance of the LSTM algorithm in forecasting battery health. The second part of the paper addresses SOC estimation, outlining common methods and introducing an Extended Kalman Filter (EKF) algorithm for real-time SOC prediction. The EKF model is constructed through three primary stages: the establishment of the observed signal section, the ECU section, and the algorithmic structure itself. Rigorous validation confirms the EKF model’s effectiveness in providing accurate SOC predictions. Lastly, the paper delves into the design and validation of a robust BMS model. Key components such as the high- and low-limit trigger signal module and SOC subscription module are integrated into the BMS framework. Validation results demonstrate that the proposed BMS model can efficiently monitor and manage battery performance, ensuring reliability and safety. The paper concludes with a discussion of future work aimed at enhancing the predictive capabilities and real-time applications of these models in battery management systems.</div></div>

FPGA-based design of advanced BMS implementing SoC/SoH estimators

Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

The estimation and prediction of state-of-health (SOH) and state-of-charge (SOC) of Lithium-ion batteries are two main functions of the battery management system (BMS). In order to reduce the computation cost and enable deployment of the BMS on the low-cost hardware, a Lebesgue-sampling-based extended Kalman filter (LS-EKF) is developed to estimate the SOH and SOC. An LS-EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is first estimated and the remaining useful life is predicted by the LS-EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of the Lithium-ion battery. The results show that the LS-EKF-based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost.

The battery management system (BMS) is crucial for the efficient operation of batteries, with state of health (SOH) prediction being one of its core functions. Accurate SOH prediction can optimize battery management, enhance utilization and range, and extend battery lifespan. This study proposes an SOH estimation model for lithium-ion batteries that integrates the Crested Porcupine Optimizer (CPO) for parameter optimization, Extreme Learning Machine (ELM) for prediction, and Adaptive Bandwidth Kernel Function Density Estimation (ABKDE) for uncertainty quantification, aiming to enhance the long-term reliability and sustainability of energy storage systems. Health factors (HFs) are extracted by analyzing the charging voltage curves and capacity increment curves of lithium-ion batteries, and their correlation with battery capacity is validated using Pearson and Spearman correlation coefficients. The ELM model is optimized using the CPO algorithm to fine-tune input weights (IWs) and biases (Bs), thereby enhancing prediction performance. Additionally, ABKDE-based probability density estimation is introduced to construct confidence intervals for uncertainty quantification, further improving prediction accuracy and stability. Experiments using the NASA battery aging dataset validate the proposed model. Comparative analysis with different models demonstrates that the CPO-ELM-ABKDE model achieves SOH estimation with a mean absolute error (MAE) and root-mean-square error (RMSE) within 0.65% and 1.08%, respectively, significantly outperforming other approaches.

This paper considers the issue of Li-Ion batteries State of Health (SoH) and State of Charge (SoC) accurate and robust estimation for electric vehicle applications. SoC and SoH are two monitoring indicators of primary importance that are used by the Battery Management System (BMS) , amongst other benefits, to manage and equalize the battery cells. Improving the estimation precision and reliability of the SoC and the SoH indicators is highly beneficial during operation and maintenance of the vehicle. We propose in this paper a new scheme of SoC and SoH estimation using an hybridization of Kalman filtering, Recursive Least Squares approach and Support Vector Machines learning. The battery SoC and SoH indicators are estimated using an adaptive-Sigma Point Kalman Filter. The battery cell impedance equivalent filter is obtained in real-time by a Recursive Least Square. Furthermore, the cell capacity evolution tracking is achieved by using a Support Vector Machine (SVM) method. Finally, the battery cell capacity and impedance equivalent filter are provided to the SoC estimator in order to update its state and observation models. This architecture yields to a complete SoC and SoH algorithmic solution exhibiting a high level of accuracy and robustness. The SVM method which requires the highest computational load in the architecture is designed to be used only for estimating the variable with the lowest evolution dynamics.

Battery management system (BMS), as the key component in electric vehicles (EVs), takes the responsibility of sate-monitoring and safety-protection for the battery pack. State of charge (SOC) and state of health (SOH) are highly correlated with the safe and efficient operation of EVs, thus BMS must realize accurate estimation of them. This paper presents a combined SOC and SOH estimation for lithium-ion battery pack with passive balance control over the battery’s cycle lifespan. Considering the slow variation of SOH and fast variation of SOC, a dual time scale combined SOC and SOH estimation method is proposed. The battery cell with minimum capacity is located offline and then SOC is estimated and updated online, which lightens the computational burden for BMS on EVs. Results show that the proposed method can realize accurate SOC and SOH estimation. The SOH estimation error can be beneath 3% and the SOC estimation accuracy can improve 7%.

Online estimation of battery model parameters and state of health in electric and hybrid aircraft application

The battery is merely an energy storage and the key for all-electric vehicles is understanding how to use the battery in the most optimal way in order to secure vehicle performance over a long period of time. The operating and controlling strategies of a battery rely on the understanding of the fundamental cell constraints, which are turned into battery and vehicle control strategies, and implemented as algorithms in the battery management system (BMS): the control unit of the battery. The BMS will control and monitor the performance and status of the battery and communicate the operational constraints currently available to the control system of the vehicle. There are many cross-dependent parameters to be understood and to be incorporated in a robust and reliable control system. Input data for the BMS are the state functions, e.g. state of charge and state of health, battery temperature, and usage history, required to secure optimal performance in a durable and safe manner. How this control and communication is handled depends on the battery and vehicle manufacturers, and is not covered in this book. Instead, the underlying fundamentals will be discussed in terms of electrochemical and material constraints. In the following sections, battery control and management will be described: charge control and methods, thermal and safety management, as well as the state functions, i.e. state of charge (SOC), state of health (SOH), and state of function (SOF). Battery management system The battery management system (BMS) utilises a number of parameters that are linked to each other and most of the key parameters are path dependent, and the usage and environmental history affects future operational possibilities. Each of these parameters affects the battery control and management system: temperature, voltage range, current, and energy throughput. Temperature is one of the most important parameters for the BMS and the corresponding control strategies. The battery should be used within a specific temperature range, a range defined by the chemistry inside the cell. At temperatures outside this predefined range, higher as well as lower, side reactions may take place, side reactions limiting battery life and possibly causing abuse situations.

It is necessary to establish a sufficiently advanced Battery Management System (BMS) for safe driving of electric vehicles. Lithium-ion batteries have been widely used in electric vehicles due to their advantages of high specific energy and low-temperature resistance, so this paper takes lithium-ion batteries as the research object. BMS can monitor various status information of lithium-ion batteries in real-time, and the State of Charge (SOC) of lithium-ion batteries is a key parameter among them. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. However, the value of SOC cannot be directly measured. In order to more accurately estimate the SOC, this paper proposes a prediction method that combines an immune genetic algorithm, gated recurrent unit, and multi-head attention mechanism (MHA), using battery experimental data from the University of Maryland as the dataset. Compared with the traditional parameter optimization approach, this paper uses the immune genetic algorithm to find the optimal hyperparameters of the model, which on the one hand has a wider choice of parameters, and on the other hand has been improved for the genetic algorithm is easy to fall into the local optimal solution, so as to improve the SOC estimation accuracy of the GRU model. The model also incorporates a multi-attention mechanism to capture different levels of information, which enhances the expressive power of the model. The data preprocessing part adopts the sliding window technique, through which the original time series data is converted into several different training samples when training the machine learning model, as a way to increase the diversity of the dataset and improve the robustness of the model. Finally, the prediction performance of the fusion model proposed in this paper is verified by Pycharm simulation, and the average absolute error, root mean square error and maximum prediction error of the model are 1.62%, 1.55% and 0.5%, respectively, which proves that the model can accurately predict the SOC of lithium-ion battery. It is shown that the model can significantly improve the accuracy and robustness of SOC estimation, enhance the intelligence, real-time and interpretability of the battery management system, and bring a more efficient, safe and long-lasting battery management solution to the fields of electric vehicles and energy storage systems.

Online Estimation and Error Analysis of both SOC and SOH of Lithium-ion Battery based on DEKF Method

Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%.

Accurate estimation of State of Charge (SOC) and State of Health (SOH) in lithium-ion batteries is critical for effective Battery Management Systems (BMS). A key factor in enhancing estimation accuracy is the appropriate selection of the Open-Circuit Voltage (OCV-SOC) model. This study investigates two prevalent methods for OCV modeling: the Low-Current (LC) method and the Incremental Current (IC) method, and evaluates their impact on the performance of the Unscented Kalman Filter (UKF) for SOC estimation and instantaneous internal resistance (R0) tracking. In this work, SOH is primarily assessed through R0​, which is adopted as the main degradation indicator under dynamic operating conditions. The dynamic model employed is an Equivalent Circuit Model (ECM) of a lithium-ion battery, with estimations conducted using real-world driving data (Federal Urban Driving Schedule, FUDS). The results demonstrate that the IC method, due to its higher local resolution in the OCV modeling, achieves faster convergence and higher estimation accuracy for SOC, particularly when the initial SOC is subject to error. In contrast, the LC method, despite its experimental simplicity, exhibits significant errors in the early stages of simulation due to excessive smoothing of the OCV curve. Furthermore, a comparison of R0 trends reveals that the IC method provides a more consistent and stable estimation, whereas the LC method shows greater short-term fluctuations and reduced stability. This difference is attributed to the higher accuracy of the OCV curve in the IC method. Ultimately, the choice of the OCV extraction method directly influences the accuracy and stability of SOC estimation and SOH analysis based on R0 tracking.

This critical review paper delves into the complex and evolving landscape of the state of health (SOH) and state of charge (SOC) in electric vehicles (EVs), highlighting the pressing need for accurate battery management to enhance safety, efficiency, and longevity. With the global shift towards EVs, understanding and improving battery performance has become crucial. The paper systematically explores various SOC estimation techniques, emphasizing their importance akin to that of a fuel gauge in traditional vehicles, and addresses the challenges in accurately determining SOC given the intricate electrochemical nature of batteries. It also discusses the imperative of SOH estimation, a less defined but critical parameter reflecting battery health and longevity. The review presents a comprehensive taxonomy of current SOC estimation methods in EVs, detailing the operation of each type and succinctly discussing the advantages and disadvantages of these methods. Furthermore, it scrutinizes the difficulties in applying different SOC techniques to battery packs, offering insights into the challenges posed by battery aging, temperature variations, and charge–discharge cycles. By examining an array of approaches—from traditional methods such as look-up tables and direct measurements to advanced model-based and data-driven techniques—the paper provides a holistic view of the current state and potential future of battery management systems (BMS) in EVs. It concludes with recommendations and future directions, aiming to bridge the gap for researchers, scientists, and automotive manufacturers in selecting optimal battery management and energy management strategies.

VLSI design and FPGA implementation of state-of-charge and state-of-health estimation for electric vehicle battery management systems