Real-time state of charge estimation for electric vehicle power batteries using optimized filter

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

This paper proposes a real-time state of charge (SOC) and electrical impedance estimation method for lithium-ion batteries based on a hybrid battery model. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A particle swarm optimization (PSO)-based online parameter identification algorithm is designed to estimate the electrical impedances and open-circuit voltage of the electrical circuit battery model. A SOC compensator is designed to correct the error of the Coulomb counting-based SOC estimation, leading to a closed-loop, accurate, robust real-time SOC estimation. The proposed method is validated by using experimental data collected from a battery tester for a polymer lithium-ion battery cell. The proposed method is applicable to other types of electrochemical battery cells.

A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods

The state of charge (SOC) displays the remaining power of the vehicle battery, its accurate estimation benefits the protection of the battery and determines the adjustment of the operating state. In this paper, a genetic algorithm-optimized back propagation neural network (GA-BP) for optimization of the Adaptive Extended Kalman Filter (AEKF) is proposed to estimate the SOC of li-ion batteries. The 2-order RC equivalent model is established. Based on the 2-order RC equivalent model and Hybrid Pulse Power Characterization (HPPC), parameters of the NCR18650B lithium battery is identified. Preliminarily estimated SOC, using EKF and battery parameters. In order to improve the accuracy of SOC estimation, a BP neural network optimization method based on genetic algorithm (GA-BP) is proposed. The AEKF is obtained by optimizing the EKF through an adaptive sampling strategy. The SOC estimated by GA-BP is converted into the input of the AEKF, which further improves the accuracy of the vehicle battery SOC estimation. The simulation and experimental results show that, compared with EKF, the maximum SOC estimation error of AEKF optimized by GA-BP neural network is reduced from 2.3% to 0.7%, and the average SOC estimation error is reduced from 1.28% to 0.41%. The latter has better convergence and robustness.

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

A FPGA implementation for a model‐based state of charge (SOC) estimation is described in this paper. A Thevenin equivalent circuit model is designed for SOC estimation. The extended Kalman filter (EKF) is designed to complete the SOC estimation, and the error is within 1 % . The FPGA is chosen to achieve realtime SOC estimation. A fast matrix method is proposed to improve the calculation speed of the EKF in FPGA because the EKF algorithm requires many matrix operations. In addition, the embedded system based on the FPGA with a system on a programmable chip (SOPC) technique is built using the Qsys platform in Quartus II. Based on the embedded system, an online testing platform is established to monitor the terminal voltage and load current of the experimental battery in real time; experimental results show that the online SOC estimation is successful. The measurement results show that the FPGA embedded scheme of the EKF allows for successful implementation of the SOC estimation with accuracy and speed. The fast matrix method requires 0.00007 s to implement the SOC estimation and is four times faster than the conventional matrix method.

In order to compare the performance of different state estimation algorithms in electrochamical model-based SOC(state of charge) estimation for lithium-ion battery, this paper proposed a series of SOC estimation approaches which use different algorithms including extended Kalman filter(EKF), adaptive extended Kalman filter(AEKF), particle filter(PF) and dichotomy. Their accuracy, convergence and computation efficiency was examined at the end of the paper.

This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.

An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries

An accurate battery State of Charge (SOC) estimation has great significance in improving battery life and vehicle performance. An improved second-order battery model is proposed in this paper through quantities of LiFePO4 battery experiments. The parameters of the model were acquired by the HPPC composite pulse condition under different temperature, charging and discharging rates, SOC. Based on the model, battery SOC is estimated by Extended Kalman Filter (EKF). Comparison of three different pulse conditions shows that the average error of SOC estimation of this algorithm is about 4.2%. The improved model is able to reflect the dynamic performance of batteries suitably, and the SOC estimation algorithm is provided with higher accuracy and better dynamic adaptability. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.2629

State of Charge (SOC) estimation plays a crucial role in managing the performance and longevity of electric vehicle (EV) batteries. Various estimation techniques, including various Kalman Filters (KF), have been employed for this purpose. The mathematical process and limits of different KF family algorithms are analysed. In this study, an impact of nonlinear KF parameters on SOC estimation accuracy for EV batteries is analyzed. Specifically, the performance of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) is examined. EKF is most preferred for SOC estimation whereas introducing sigma points in UKF is the basic difference between EKF and UKF. In comparison to EKF and UKF, PF is more flexible since it applies to nonlinear systems with non-Gaussian distributions. Utilising a sequence of noisy measurements, KF is a recursive method that approximates the state of a dynamic system. It requires accurate parameters such as process noise covariance (Q) and measurement noise covariance (R) to work effectively. In this work, the effect of these parameters on state of charge estimation using nonlinear KFs has been analysed under different settings of noise covariance matrices. Tuning these parameters is essential for optimizing the performance of the KF and ensuring accurate and reliable state estimation in real-world applications. To find parameter values that provide the best balance between accuracy, robustness, and computational efficiency, the tuning process involves experimentation, simulation, and validation using real-world data. According to the results observed, the filter parameters such as three diagonal elements of Q are selected as 2e-8, 0.005, 0.003 and R as 2e-6. By evaluating the state estimation accuracy under different driving scenarios, robustness and sensitivity of filtering technique in estimating SOC for EV batteries has been studied. This analysis contributes to the optimization of SOC estimation algorithms, enhancing the reliability and efficiency of EV battery management systems.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Improving Li-S Battery SOC Estimation using an SOC-Dependent Resistance Model

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

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.