阶次自适应AR等效电路模型的锂电池SOC滑模观测

基于等效电路模型的一类车载动力电池剩余荷电状态(state of charge，SOC)的估算方法，其估算精度高度依赖于模型精度，模型精度又正比于模型复杂度，以至于难以较好地应用于嵌入式控制单元.提出复杂度相对较低、能够自适应确定最优模型阶次的全新等效电路模型——基于阶次自适应AR模型的车载动力电池等效电路灰箱模型.基于此灰箱模型，给出锂离子电池SOC的滑模观测器设计推导及能观性、收敛性证明.结果表明，本文提出的基于阶次自适应AR等效电路灰箱模型的滑模观测器SOC估算方法(adaptive autoregressive-sliding mode observer，AAR-SMO)具有低模型复杂度、高精度、强鲁棒性及快速收敛等性能.

Sliding Mode Observer of Lithium Battery SOC Based on Order Adaptive AR Equivalent Circuit Model

Based on the equivalent circuit model， the estimation method of the remaining state of charge (SOC) of a type of vehicle power battery shows that the estimation accuracy is highly dependent on the model accuracy and the model accuracy is directly proportional to the model complexity， so that it is difficult to be better applied to the embedded control unit problem. A new equivalent circuit model with relatively low complexity and capable of adaptively determining the optimal model order， i.e.， a gray box model of the equivalent circuit of the vehicle power battery based on the order adaptive AR model is proposed. Based on this gray box model， the design derivation of the sliding mode observer for the state of lithium-ion battery SOC and the proof of observability and convergence are given. The results show that the sliding mode observer SOC estimation method (adaptive autoregressive-sliding mode observer， AAR-SMO) based on the order-adaptive equivalent circuit gray box model proposed in this paper has low model complexity， high accuracy， strong robustness and fast convergence performance.