基于扩展卡尔曼滤波算法的燃料电池车用锂离子动力电池荷电状态估计

针对燃料电池汽车用锂离子动力蓄电池建立一个简单物理模型和一个复杂物理模型,它们分别以不同等效电路来描述电池的动态特性,然后将这两个物理模型中各物理量和状态量之间的关系表达成离散化的状态空间方程.在此基础上,利用在燃料电池汽车实车运行过程中测得的包括电流、电压等数据,基于扩展卡尔曼滤波算法对锂离子动力电池的荷电状态进行估计,并对利用两种模型进行估计的结果进行比较分析.分析结果表明,基于电池等效电路模型的卡尔曼滤波电池荷电状态估计算法是有效的,并且电池模型对估计结果的影响较大,利用精确的电池模型容易达到较高的估计精度.

ESTIMATE STATE OF CHARGE OF POWER LITHIUM-ION BATTERIES USED ON FUEL CELL HYBRID VEHICLE WITH METHOD BASED ON EXTENDED KALMAN FILTERING

One of the most important tasks of the power lithium-ion battery management systems used on the fuel cell hybrid vehicles (FCHVs) is to precisely estimate the current state of charge (SOC) of the batteries online. A simple physical model and a complex physical model of the battery pack used on FCHVs are proposed, which describe the dynamic behavior of the battery pack by different equivalent circuits respec- lively, and the relationships among different physic variables and state variables of the two models are expressed by the discrete-time state-space functions. Then, based on the Extended Kalman Filter and these two models, the state of charge for the battery pack is estimated by using data such as current and voltage which are sampled when the vehicle is running, and the differences between the two results estimated are also analyzed by using two different models. Our results show that, model based Kalman filter SOC estimation for batteries used on FCHVs is effective. It is also shown that estimating accuracy depends highly on the selected model, and a better model can provide a higher accuracy.