An Adaptive Sigma Point Kalman Filter Hybridized by Support Vector Machine Algorithm for Battery SoC and SoH Estimation

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

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.

The state of charge (SoC) and state of health (SoH) monitoring of power lithium-ion batteries is the basis of the battery management system (BMS). The estimation of SoC and SoH is a hot issue in battery management system research today. This paper refers to a third-order electrochemical model based on the average electrode of a lithium battery. Based on this model, the paper establishes a battery SoC and SoH estimation observer based on linear sliding mode and full-order terminal sliding mode algorithms. The paper uses the Luenberger algorithm and terminal sliding mode algorithms to estimate the SoC and SoH of lithium-ion batteries to improve their stability and convergence and uses Lyapunov's theory to verify the convergence of the observer. The accuracy and robustness of SoC and SoH estimations are verified by the dynamic stress test (DST) and federal urban driving conditions (FUDS) test data.

Fusion estimation of lithium-ion battery state of charge and state of health considering the effect of temperature

Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

The state of charge (SOC) and state of health (SOH) of lithium-ion batteries (LIB) are two major indices of the battery management system (BMS) for system monitoring, health prognosis, and optimal usage. This paper presents a new SOC and SOH estimation method based on two recursive least squares (RLS) algorithms. First, a second-order equivalent circuit model is used to describe battery dynamics. Second, an open-circuit-voltage (OCV) and internal resistance estimation method is proposed based on the RLS algorithm. Instead of using SOC as a state in a state space model, the OCV is estimated from a linear regression directly. The battery total capacity is then estimated by a combination of the estimated OCV and another RLS algorithm. The accurate battery SOC and SOH can be obtained without a priori knowledge of battery parameters. Simulation results highlight the accuracy and robustness of the proposed method.

State of charge (SOC) and state of health (SOH) are of critical importance to ensure safety, reliability, and prolong battery life. In this paper, a novel SOC & SOH estimation method considering side reactions over the lifespan of a Li-ion degraded battery is presented. To achieve this, an order-reduced electrochemical model considering side reactions is established first by simplification of the partial differential equations (PDEs) using the Laplace transform, inverse Laplace transform, Pade approximation, etc. The open-circuit-voltage (OCV) versus SOC correlation is utilized as the link between equivalent circuit model (ECM) and electrochemical model. Then the current induced accumulated aging effects of loss of ions and active materials on the electrode stoichiometry could be calculated to obtain a modified OCV- SOC curve. The updated OCV-SOC relationship is applied to an ECM based SOC estimator. The SOH monitoring is achieved by quantifying the capacity fade with the help of equilibrium potentials. Finally, the robustness and effectiveness of the SOC and SOH estimation scheme integrated with the presented OCV curve are validated by simulated and experimental results. The results show that the SOC estimation precision of the presented scheme regarding battery interior properties has been improved compared to the existing method. Future work is also highlighted.

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

Lithium-ion power battery is the power source of many electronic control systems. The battery state of charge (SOC) and state of health (SOH) are very important to the safety and reliability of the electronic system. Therefore, high-precision estimation of battery SOC and SOH has become one of the current research hotspots in the battery field. In order to realize the state estimation of lithium-ion power battery, an equivalent circuit model of lithium-ion power battery is established. This article is based on the HPPC pulse experiment data and the second-order Thevenin equivalent circuit model. At different temperatures (0°C, 25°C, 45°C), the recursive least square (RLS) method is used to identify of parameters of the equivalent model circuit. The state equation and measurement equation of the double extended Kalman filter (DEKF) algorithm are used to estimate SOC and SOH. Finally, this article uses experimental data of the single cell to estimate the battery state. The accuracy and adaptability of the combination of least squares algorithm and dual extended Kalman filter are verified. This is of great significance for improving the safety, reliability and economic benefits of the battery system.

Battery management system (BMS) refers to a critical electronic control unit in the power battery system of electric vehicles. It is capable of detecting and estimating battery status online, especially estimating state of charge (SOC) and state of health (SOH) accurately. Safe driving and battery life optimization are of high significance. As indicated from recent literature reports, most relevant studies on battery health estimation are offline estimation, and several problems emerged (e.g., long time-consuming, considerable calculation and unable to estimate online). Given this, the present study proposes an online estimation method of lithium-ion health based on particle swarm support vector machine algorithm. By exploiting the data of National Aeronautics and Space Administration (NASA) battery samples, this study explores the changing law of battery state of charge under different battery health. In addition, particle swarm algorithm is adopted to optimize the kernel function of the support vector machine for the joint estimation of battery SOC and SOH. As indicated from the tests (e.g., Dynamic Stress Test), it exhibits good adaptability and feasibility. This study also provides a certain reference for the application of BMS system in electric vehicle battery online detection and state estimation.

An accurate battery model is essential for precise estimation of battery performance indicators, such as state of charge (SoC), state of health (SoH), etc. It helps in determining the optimum energy management and therefore, optimum utilization of the battery capacity. Some of the battery model parameters may change with the variation of environmental conditions, such as operating temperature, and therefore, accurate temperature compensated model estimation is necessary to minimize the error in battery model and SoC estimation. This article presents a novel parameters extraction method for the Thevenin equivalent circuit model of Li-ion batteries considering their resiliency on temperature effect. The proposed approach represents each of the RC parallel circuits of the Thevenin-based equivalent circuit battery model as a first-order linear time-invariant (LTI) system. The resistance and capacitance values for each of the RC circuits in the model have been identified as the parameters of a standard LTI system using the system identification theory and represented them as the temperature-compensated model parameters. The proposed model can be applied to various applications, such as SoC, SoH estimation, and battery energy management. In order to demonstrate the suitability of the proposed modeling approach, the model is augmented with the conventional extended Kalman filter to enable accurate SoC estimation under varying operating temperatures. The accuracy and effectiveness of the proposed modeling approach and its suitability to enhance SoC estimation under a wide operating temperature range (-5 to 45 °C) have been validated through laboratory experiments in the LabVIEW platform.

The state of health (SOH) is a significant index for the safe and reliable performance of lithium-ion batteries (LIBs). Although electrochemical impedance spectroscopy (EIS) is a promising nondestructive technique for SOH estimation, existing investigations rarely apply EIS to establish SOH estimation mathematical models. Considering the SOH estimation model based on EIS is imposed by the nonlinear and coupled constraints including temperature and state of charge (SOC), in this paper, the charge transfer resistance is extracted first as the aging feature through EIS analysis under different conditions of SOH, SOC, and temperature. Then, a multi-state SOH model is developed based on the charge transfer resistance. The model quantitatively elucidates the interdependencies among charge transfer resistance, SOH, SOC, and temperature. A two-stage SOH estimation strategy is subsequently proposed based on the proposed model to address the inferior of accuracy from the traditional probabilistic model for SOH estimation due to inaccurate model parameters. Specifically, in the first stage, the SOH is initialized. In the second stage, SOH is re-estimated based on optimal point parameters selected by the SOH obtained in the first stage. Finally, the proposed estimation strategy for SOH is verified through experiments, where the estimation maximum RMSE is less than 0.04. Compared to the previous methods, the proposed strategy demonstrates more accuracy in SOH estimation.

In many electrical and electronic applications there is the need to have an efficient measurement over the State-of-Health (SoH) and the State-of-Charge (SoC). The SoH is an indirect measurement about the age of the battery while the SoC indicates the energy stored in the battery. The SoC and SoH are commonly referred in papers and scientific reports. However, their design and implementation procedure is very often left behind. Moreover, the implementation is not trivial. This paper presents the design and implementation of lead-acid battery SoH and SoC. The purpose is to present carefully the procedure showing how to design and implement the SoH and SoC estimators. The SoH estimation is based on obtaining the battery internal resistance. The SoC is based on the Coulomb Counting method, valid for charging and discharging processes. Experimental results using a 12V/2.3Ah battery show the efficacy of the proposed design and implementation method.

In the energy storage System, the Battery Management System (BMS) is responsible for the charge and discharge control and energy balance control of the lithium battery, and the accuracy of lithium battery state estimation is directly affected by the control effect of BMS. In order to ensure the normal operation of lithium batteries, this paper selects the State of Charge (SOC) and State of Health (SOH) of lithium batteries as the research object and accurately estimates the SOC and SOH of lithium batteries as the research goal. The equivalent circuit model based on lithium battery is established, and the identification model parameters are based on the recursive least squares method with forgetting factor. Based on the Thevenin equivalent circuit model, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate the SOC of the battery, respectively. FFRLS and Double Extended Kalman Filter (DEKF) are used to estimate the ohm internal resistance in the equivalent model based on lithium battery, and then the SOH is estimated.

To prolong battery lifetime and maximize performance, battery management systems (BMSs) need to estimate internal variables that are related to battery aging. For that, accurate battery models that can represent internal variables that are not directly measurable are required. However, most present BMSs use equivalent-circuit models (ECMs or EQMs) for state of charge (SOC) and state of health (SOH) estimation. These models are not able to predict these aging-related variables, and therefore, they cannot be used to limit battery degradation. In this paper, we propose a method for electrode-level SOC (eSOC) and electrode-level SOH (eSOH) estimation using an electrode-level ECM (eECM), which can describe both electrodes’ potentials, giving valuable information for aging prevention. The method produces state of lithiation (SOL) and potential estimates of both electrodes, and updates the eSOH parameters to maintain estimation accuracy through the lifetime of the battery. Furthermore, the eSOH parameter estimates are used to obtain degradation mode information, which could be used to improve state estimation, health diagnosis and prognosis.