Journal of Energy Storage, Vol. 25, October, 2019, 100838, DOI:10.1016/j.est.2019.100838

State of Charge Estimation Based on a Thermal Coupling Simplified First-principles Model for Lithium-ion Batteries

Chao Lyu 1, Junfu Li 2, Lulu Zhang 1, Lixin Wang 3, Dafang Wang 2, and Michael Pecht 4
1 School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
2 School of Automotive Engineering, Harbin Institute of Technology, Weihai, 264209, Shandong, China
3 School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China
4 CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA


Accurate lithium-ion battery state of charge (SOC) estimation can enhance reliable and safe operation of electric vehicles. A thermal coupling simplified first-principles model has been adopted to achieve high SOC estimation accuracy. Extended Kalman filter and adaptive extended Kalman filter algorithms are separately combined with the model to estimate state of charge for a wide range of environmental temperatures (10–45 °C) and different charge/discharge rates. The SOC estimation method is validated with respect to the accuracy and convergence. The average absolute errors using the adaptive extended Kalman filter algorithm under conditions of dynamic stress tests and hybrid pulse power characteristics are less than 1%, which is 1.5% smaller than that of the EKF algorithm. Compared to the extended Kalman filter algorithm, the adaptive extended Kalman filter algorithm can achieve fast convergence after less than 10 s while maintaining the estimation accuracy given an initial SOC guess error of 50%. The effects of sampling frequency and battery aging states on estimation accuracy are also assessed. A sampling frequency of at least 1 Hz can ensure the accuracy is within 1%. The developed SOC estimation method is also fit for the degraded battery with about 1% estimation error.

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