基于变分模态分解和核极限学习机集成模型的电动汽车锂电池健康状态预测

In the traditional prediction of lithium batteries for electric vehicles, the state-of-health (SOH) prediction is usually regarded as a whole, and the result of single SOH prediction is obtained accordingly. However, in the actual operation of a car, the single prediction of SOH has a large error, and its prediction effect is not satisfying. To improve the accuracy of battery SOH prediction for electric vehicles, a novel prediction method based on variational modal decomposition (VMD) and sparrow search algorithm (SSA) optimization of kernel-based extreme learning machine (KELM) integrated prediction model, i.e., VMD-SSA-KELM, is proposed. First, the battery SOH sequence is decomposed by VMD to reduce the influence of SOH fluctuations. Meanwhile, the Person correlation method is used to reduce the influence of noise and improve the accuracy of prediction. The KELM is introduced, which improves the accuracy of prediction while retaining the advantages of extreme learning machine. The proposed model is validated based on the operation data of four electric vehicles, and experimental results show that compared with the VMD-DBO-KELM, VMD-POA-KELM, VMD-KELM and VMD-ELM models, the proposed model has a prediction trend which is the same as that of the original data, while the results of other models fluctuate a lot. The root mean square error of results predicted by the novel model is less than 0.2%, the prediction accuracy becomes higher, the prediction efficiency is faster and the time used is shorter, indicating that the proposed method has higher accuracy and better robustness.