A State of Charge Estimator Based Extended Kalman Filter Using an Electrochemistry-Based Equivalent Circuit Model for Lithium-Ion Batteries

• Main Authors: Xin Lai, Chao Qin, Wenkai Gao, Yuejiu Zheng, Wei Yi
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Applied Sciences
• Subjects: lithium-ion batteries; simplified electrochemical model; state of charge estimator; extended kalman filter
• Online Access: http://www.mdpi.com/2076-3417/8/9/1592

In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.