基于电池能量状态和车辆能耗的电动汽车续驶里程估算  

A Driving Range Estimation Model for Electric Vehicles Based on Battery Energy State and Vehicle Energy Consumption

作　　者：陈燎[1] 杨帆[1] 盘朝奉[1,2] 

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013 [2]江苏大学汽车工程研究院,江苏镇江212013

出　　处：《汽车工程学报》2017年第2期113-122,共10页Chinese Journal of Automotive Engineering

基　　金：国家自然科学基金(51105178;51475213);江苏省"六大人才高峰"项目(2013-XNY-002);江苏省自然科学基金(BK2011489)

摘　　要：为了提高续驶里程估算精度,在工况识别基础上提出一种基于电池能量状态和车辆能耗的续驶里程估算模型,该模型能有效地消除里程误差并具有较好的收敛性及鲁棒性。在Matlab/Simulink下建立电池模型及整车能耗模型,基于该模型建立工况特征参数与能耗之间的模糊规则库,再对电动空调单独进行续驶里程估算,基于卡尔曼滤波的方法对输出剩余里程进行优化。仿真及试验结果表明,与传统的续驶里程估算方法相比,采用基于电池能量状态和车辆能耗的方法不仅能提高剩余续驶里程估算精度,而且能解决在急剧变化的工况下剩余续驶里程大幅度波动的问题。In order to improve the estimation accuracy of the driving range, a new model based on battery energy state and vehicle energy consumption is proposed. This model can effectively eliminate mileage errors and has good convergence and robustness. First, the battery model and the vehicle energy consumption model were established by using Matlab/Simulink. Next, the filzzy rules for tile characteristic parameters and energy consumption were built up based on the model. Then, the driving range of air conditioning was evaluated individually. Finally, the output of the remaining mileage based on the Kahnan filtering was optimized. The simulation and experimental results show that compared with the traditional methods of driving range estimation, the proposed model can not only improve the estimation accuracy of the remaining driving range, but also can eliminate the problem of the fluctuations of the remaining mileage with tile rapidly changing conditions.

关 键 词：电动汽车 续驶里程估算 车辆能耗 空调 卡尔曼滤波 

分 类 号：U469.72[机械工程—车辆工程]