A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach

Abstract The state of charge (SOC) estimation is extremely important for the wide commercialization and safe operation of electric vehicle (EV), especially under cold conditions, which is also a critical technology for battery system in EVs used in the 2022 Beijing winter Olympics. Three efforts have been made in this paper: (1) A general joint estimation framework with dual estimators is set up. Based on this frame, a joint algorithm using the recursive least square (RLS) and the adaptive H infinity filter (AHIF) is realized. (2) Four filter-based algorithms have been systematically compared and analyzed at the wide temperature range. The results show that RLS-AHIF algorithm has better performance for SOC estimation even at low temperatures, such as −10 °C, and the SOC error is within 3.5%. (3) A hardware-in-loop validation platform including the battery management system (BMS) and battery test instruments has been built to verify the proposed method. The results from the platform show that the maximum error of SOC is less than 2% at 0 °C and 25 °C. Consequently, the proposed algorithm can achieve the application over a wide temperature range in an actual BMS.

[1]  Linlin Li,et al.  An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application , 2018, Applied Energy.

[2]  Najoua Essoukri Ben Amara,et al.  Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter , 2017 .

[3]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[4]  Hongwen He,et al.  Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles , 2018, IEEE Access.

[5]  Zonghai Chen,et al.  A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter , 2018 .

[6]  H. A. Bastawrous,et al.  Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation , 2017 .

[7]  Huazhen Fang,et al.  State of charge estimation for lithium-ion batteries: An adaptive approach , 2014 .

[8]  Hao Mu,et al.  A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm , 2016 .

[9]  Ping Shen,et al.  The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles , 2018, IEEE Transactions on Vehicular Technology.

[10]  Le Yi Wang,et al.  A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .

[11]  Andreas Jossen,et al.  Validation and benchmark methods for battery management system functionalities: State of charge estimation algorithms , 2016 .

[12]  Michael Pecht,et al.  An adaptive state of charge estimation approach for lithium-ion series-connected battery system , 2018, Journal of Power Sources.

[13]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[14]  Dong Wang,et al.  A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile , 2016 .

[15]  Ali Emadi,et al.  Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[16]  Azah Mohamed,et al.  A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .

[17]  Chenbin Zhang,et al.  An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles , 2016 .

[18]  Weilin Li,et al.  State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer , 2017, IEEE Transactions on Industrial Electronics.

[19]  Jiahao Li,et al.  A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems , 2015 .

[20]  Qiao Zhu,et al.  State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method , 2017 .

[21]  Jianqiu Li,et al.  Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model , 2015 .

[22]  Guangzhong Dong,et al.  Constrained Bayesian dual-filtering for state of charge estimation of lithium-ion batteries , 2018, International Journal of Electrical Power & Energy Systems.

[23]  Van-Huan Duong,et al.  Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares , 2015 .

[24]  Hicham Chaoui,et al.  State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Vehicular Technology.

[25]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[26]  Cheng Chen,et al.  A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation , 2018, IEEE Transactions on Power Electronics.

[27]  Hongjie Wu,et al.  Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration , 2015 .

[28]  Mohamed Becherif,et al.  Experimental validation for Li-ion battery modeling using Extended Kalman Filters , 2017 .

[29]  Binggang Cao,et al.  A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter , 2017 .

[30]  Franck Guillemard,et al.  Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment , 2016 .