State of charge estimation of lithium ion battery based on parallel Kalman filter

doi:10.19799/j.cnki.2095-4239.2021.0169

Abstract

Abstract:

The state of charge (SOC) of lithium-ion batteries is studied, and a parallel Kalman filter-based SOC estimation algorithm is proposed, with the goal of solving the problem of power display and life prediction in new energy electric vehicles. The Thevenin battery's first-order RC equivalent circuit model is defined. Data processing of open circuit experiments results in the static OCV-SOC relationship expression. The least-square method with a dynamic forgetting factor is used to identify the model's parameters. The maximum likelihood estimation criterion is used to make the model noise covariance self-learning, using the Ampere-hour integral method as the state transfer equation and the extended Kalman filter as the state transfer equation. Given that the model parameters change as the battery life declines, a parallel structure filter is designed to estimate the battery state and modify the parameters accordingly, ensuring the purity and independence of the data transmission and allowing SOC estimation throughout the life. The simulation results show that the algorithm has fast convergence and real-time performance, and the estimation accuracy is less than 2%.

Key words: electric vehicle, lithium-ion battery, extended Kalman filter, SOC estimation, dynamic forgetting factor

CLC Number:

TP 185

Yi'nan ZHU, Taolin LÜ, Zhiyun ZHAO, Wen YANG. State of charge estimation of lithium ion battery based on parallel Kalman filter[J]. Energy Storage Science and Technology, 2021, 10(6): 2352-2362.

Figures/Tables 6

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