Battery Life Prediction Based on a Hybrid Support Vector Regression Model

Abstract

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An accurate state of health and remaining useful life prediction is important to provide effective judgment for the lithium-ion battery and reduce the probability of battery effectiveness. This article proposes a hybrid model for the prediction by combining an improved decomposition algorithm, an improved parameterization algorithm, and a least squares support vector regression algorithm. The capacity signal is decomposed by the improved complete ensemble empirical mode decomposition with an adaptive noise algorithm to solve the backward problem. Then, the least squares support vector regression algorithm is used to predict each decomposition component separately. To obtain better parameters of the prediction model, a good point set principle and inertia weights are introduced to optimize a sparrow search algorithm. Experimental results confirm that the proposed hybrid prediction model has high accuracy, good stability, and strong robustness, which achieves a minimum 0.3% mean absolute error of the B0005 battery. The impact of prediction steps on accuracy is also discussed in this article. The results verified the capacity accuracy of the batteries predicted by eight steps.

Keywords

• lithium-ion battery
• life prediction
• parameterization algorithm
• mode decomposition
• support vector regression