Capacity Prediction for Lithium-ion Batteries Based on Improved Least Squares Support Vector Machine and Box-Cox Transformation

doi:10.3901/JME.2021.14.118

Abstract

Abstract: Accurate and reliable estimation of battery capacity can help prevent overuse and ensure operation safety; and on this basis, the remaining useful life (RUL) prediction can supply the guidance for maintenance and replacement battery systems. A synchronous estimation method for capacity and RUL is proposed based on the improved dual least squares support vector machine (LS-SVM) and Box-Cox transformation. First, the aging feature variables are extracted from the envelope area of partial incremental capacity curves, and the Box-Cox transformation is employed to improve the correlation between the aging features and target estimation variables. Then, the Renyi entropy is applied to improve the sparseness of traditional LS-SVM; and a joint estimation model for capacity and RUL is constructed. The estimated performance is sufficiently evaluated by applying the analytic hierarchy process and the entropy weight methods. Finally, the particle swarm optimization (PSO) is exploited to search the optimal hyper-parameter combination of least squares support vector machine. The experimental results demonstrate that the proposed method can significantly improve aging feature performance, and the estimation error of capacity and RUL can be respectively restricted within 1.44% of nominal capacity and 47 cycles, thereby verifying the effectiveness of the proposed method.

Key words: lithium-ion batteries, battery capacity, remaining useful life (RUL), least squares support vector machine (LS-SVM), Box-Cox transformation

CLC Number:

TM912

SHU Xing, LIU Yonggang, SHEN Jiangwei, CHEN Zheng. Capacity Prediction for Lithium-ion Batteries Based on Improved Least Squares Support Vector Machine and Box-Cox Transformation[J]. Journal of Mechanical Engineering, 2021, 57(14): 118-128.

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