To address the difficulty in predicting the state-of-charge(SOC)of a Li-ion battery pack,an SOC prediction model based on kernel extreme learning machine(KELM)optimized by the improved sparrow search algorithm(ISSA)is proposed.First,Logistic chaotic mapping is introduced to improve the standard SSA and acquire the best population individuals.Second,the improved algorithm is used to optimize the kernel function parameter S and penalty coefficient C of KELM to create an ISSA-KELM prediction model.The simulation is carried out utilizing the historical data from an energystorage device,and the results predicted by ELM,KELM and ISSA-ISSA-KELM models were compared and analyzed.In addition,the robustness of the model was verified using data under other working conditions.Results show that the root mean square error and mean absolute error of predicted SOC decreased to 2.06％and 1.54％,respectively.The proposed model improved the prediction accuracy,and its convergence,generalization and robustness were also satisfying.
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