Lithium Iron Phosphate Battery Electric Vehicle State-of-Charge Estimation Based on Evolutionary Gaussian Mixture Regression

Lithium batteries have the characteristics of high energy density and charge-discharge rate, but exhibit high chemical activity. State-of-charge (SOC) estimation is critical to the lithium battery electric vehicle (EV) operation safety. In this paper, a novel SOC estimation method is proposed based on Gaussian process regression. A mixture Gaussian process is used in this model to strengthen the reliability of data description and to increase the estimation accuracy. Optimal number of Gaussian processes is obtained by a revolutionary expectation maximum method. A nonlinear correlation feature selection method is introduced to improve the model efficiency. The effectiveness of the proposed method is verified by an EV field test. Compared with other data-based approaches, this method exhibits higher estimation accuracy and computational efficiency.