对锂离子电池动力学系统进行了非线性特性分析,并判别了其混沌特性。采用相空间重构技术恢复锂离子电池动力学系统原有的混沌特性，得到多维状态空间的时间序列，利用LS-SVM模型对重构后的时间序列进行预测，获得荷电状态 (State of Charge,SOC)的预测值。仿真结果表明：与BP神经网络预测模型相比，该预测方法具有较高的预测精度和较好的适应性，对实际应用具有一定的指导意义。

The nonlinear characteristics of lithium ion battery dynamic system were analyzed and its chaotic characteristics were identified. The chaotic characteristics of lithium ion battery dynamic system were restored by phase space reconstruction technology and the time sequence of multidimensional state space was obtained. The prediction of reconstructed time sequence was conducted with the LS-SVM model and the predicted value for state of charge (SOC) was assessed. The simulation results show that the prediction method has higher prediction accuracy and better adaptability than BP neural network prediction model, which has a certain guiding significance for practical application.