Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions

To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0–1 s and 1–10 s. The extended Kalman filter (EKF) algorithm was improved to present a novel improved Kalman filter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.

[1]  Boyang Liu,et al.  Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries , 2019, Journal of Power Sources.

[2]  Xu Guo,et al.  A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis , 2020 .

[3]  Yujie Wang,et al.  A framework for state-of-charge and remaining discharge time prediction using unscented particle filter , 2020 .

[4]  Hongwen He,et al.  State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction , 2019 .

[5]  Xuning Feng,et al.  Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine , 2019, IEEE Transactions on Vehicular Technology.

[6]  Guangzhong Dong,et al.  Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization , 2021, IEEE Transactions on Industrial Electronics.

[7]  Hongwen He,et al.  Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement , 2021 .

[8]  Rui Xiong,et al.  State-of-Health Estimation Based on Differential Temperature for Lithium Ion Batteries , 2020, IEEE Transactions on Power Electronics.

[9]  Yan Xu,et al.  State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method , 2020, IEEE Transactions on Vehicular Technology.

[10]  Jie Tang,et al.  A data-driven fuzzy information granulation approach for battery state of health forecasting , 2020 .

[11]  Zhan Ma,et al.  Multilayer SOH Equalization Scheme for MMC Battery Energy Storage System , 2020, IEEE Transactions on Power Electronics.

[12]  Xiaosong Hu,et al.  State estimation for advanced battery management: Key challenges and future trends , 2019, Renewable and Sustainable Energy Reviews.

[13]  Sheng Liu,et al.  Reduced-Coupling Coestimation of SOC and SOH for Lithium-Ion Batteries Based on Convex Optimization , 2020, IEEE Transactions on Power Electronics.

[14]  Xiaosong Hu,et al.  An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management , 2020 .

[15]  Zonghai Chen,et al.  A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems , 2020 .

[16]  Dirk Uwe Sauer,et al.  Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries , 2020 .

[17]  Penghua Li,et al.  State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network , 2020, Journal of Power Sources.

[18]  Longcheng Liu,et al.  A model for state-of-health estimation of lithium ion batteries based on charging profiles , 2019, Energy.

[19]  Zhile Yang,et al.  Lithium-ion battery charging management considering economic costs of electrical energy loss and battery degradation , 2019, Energy Conversion and Management.

[20]  Guangcai Zhao,et al.  Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries , 2020, IEEE Transactions on Industrial Electronics.

[21]  Sudipta Bijoy Sarmah,et al.  Numerical and experimental investigation of state of health of Li-ion battery , 2020 .

[22]  Datong Liu,et al.  A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries , 2020 .

[23]  Yujie Wang,et al.  A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory , 2020 .

[24]  Ji Wu,et al.  A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain , 2019, IEEE Transactions on Industrial Electronics.

[25]  Xiaosong Hu,et al.  Battery Lifetime Prognostics , 2020 .

[26]  Jonghyun Park,et al.  A comprehensive single-particle-degradation model for battery state-of-health prediction , 2020 .

[27]  Ruikai Zhao,et al.  An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: Model development and validation , 2020 .

[28]  Yujie Wang,et al.  Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation , 2020 .

[29]  Fei Feng,et al.  Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model , 2020 .

[30]  Peng Zhao,et al.  Fast charging optimization for lithium-ion batteries based on dynamic programming algorithm and electrochemical-thermal-capacity fade coupled model , 2019, Journal of Power Sources.

[31]  Yujie Wang,et al.  Model migration based battery power capability evaluation considering uncertainties of temperature and aging , 2019, Journal of Power Sources.

[32]  Hongwen He,et al.  Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology , 2019, Applied Energy.

[33]  Guangzhao Luo,et al.  Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation With Short-Term Feature , 2020, IEEE Transactions on Power Electronics.

[34]  Akihiko Kudo,et al.  Development of status detection method of lithium-ion rechargeable battery for hybrid electric vehicles , 2021 .

[35]  Ebrahim Farjah,et al.  Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications , 2020, IEEE Transactions on Industrial Electronics.