Health prognosis for lithium-ion battery with multi-feature optimization

With the widespread use of lithium-ion batteries, battery failure will bring serious safety problems and economic losses. State of health (SOH) is a key and challenging issue in the prognostics and health management of lithium-ion batteries. In this paper, we propose a new SOH estimation method for lithium-ion batteries with multi-feature optimization. Firstly, we obtain the initial feature set by sampling the voltage curve and incremental capacity curve to comprehensively describe the battery aging process. Then, the Gaussian process regression model is improved by designing a dual kernel to track the long-term battery aging process with dynamic fluctuations. Finally, we introduce a genetic algorithm to optimize the SOH estimator establishing process by considering the estimation accuracy, the number of features, and the difficulty of feature acquisition. To verify the effectiveness of the proposed method, commercial batteries are tested under different charging and discharging conditions. The experimental results show that the proposed method with multi-feature optimization can achieve a prediction error of less than 0.6%.