State of charge(SOC)is the key parameter of the lithium-ion battery management system, which needs to be estimated accurately to ensure the battery's safe operation. The traditionally used data-driven SOC estimation methods(e.g.,neuro-network)have limitations on interpretability and parameter tuning. This article proposes a novel nnethod by combining the local model network(LMN)and the beetle antenna search(BAS)algorithm. Firstly,LMN,known as a grey-boxmodel that can model complex non-linear systems with some extent of interpretability,is employed to partition the working condition space into some sub-regions that can be represented by simple models. Then, they are combined by validation function. Secondly, during the training of LMN,BAS optimization is utilized to find the optimal splitting location and orientation globally, which reaches a good trade-off between the model identification accuracy and the computation complexity. Finally,the proposed SOC estimation method is compared with two existing methods on a lithium-ion battery dynamic characteristic dataset. The RMSE is less than 0.4% on the training set under simple test driving cycle,and less than 0.9% on the testing set under complex test driving cycles. The performance on different temperatures is relativelystable too. Therefore, it shows anexcellent identification accuracy and generalization capability of the method. The advantage of the proposed method is verified on real measured dataset too.