An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition - Zhu, Tao; Wang, Shunli; Fan, Yongcun; Hai, Nan; Huang, Qi; Fernandez, Carlos (Energy (Oxford), 2024)
Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. •The algorithm was validated using both NASA and CALCE datasets.•Improving the dung beetle optimization algorithm using three strategies.•The superiority, robustness and stability of the algorithm are verified.