A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions

The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.

A Novel Adaptive Multi-time Scale Joint Online Estimation Method for SOC and SOH of Lithiumion Batteries

The state of charge (SOC) and state of health (SOH) are essential indicators for estimating the performance of lithium-ion batteries. In most of the existing methods to estimate SOC and SOH through step-by-step calculation may bring obstacles to real-time prediction of battery performance. To adapt the complex and dynamic situation of the batteries and estimate SOC and SOH in an accurate and fast manner, a novel multi-time scale joint online estimation method is proposed. In order to quickly identify the battery model and estimate the battery state, SOC and SOH are evaluated on a multi time scale framework based on extended Kalman filter (EKF). To improve the accuracy of the equivalent circuit model (ECM), a variable forgetting factor recursive least square (VFFRLS) method is introduced to identify the internal parameters in the battery model. A fuzzy variable time scale EKF (FVEKF) is proposed to estimate SOC and SOH online, where the fuzzy inference engine change the time scale to increase the convergence speed especially in complex stress conditions. Database from the University of Maryland is adopted to testify the effectiveness and efficiency of the algorithm. The results demonstrate that the method has better estimation accuracy and efficiency comparing to traditional joint estimation method, and meet the requirements of real-time estimation.