基于STM32-OCV法的纯电动汽车剩余里程预测

摘要

The accurate real-time prediction of the remainder range of the pure electric vehicle is one of the important fields in the research of the power battery energy management system(BMS).In this paper,to solve the prediction difficulties of the large error,poor adaptability and complex mathematical modeling,the OCV is only used to predict the initial SOC value.When the standing time of the battery is long enough and its SOC is large,and the standard range SN and the standard capacity SOCN are updated in time with self-adaptability to eliminate the accumulated error.Then,the slope parameters and the non-essential energy consumption equipment capacity parameters are transformed into the range parameters under the standard SN.Finally,the optimized OCV mathematical model is established and the STM32 hardware hardware circuit is designed to predict the remainder range.It is tested by the pure electric vehicle EV-1 and the test values are recorded to compared with the predicted values.The results show that the maximum relative error is 5.2％,which is obviously improved compared with the other current methods.%纯电动汽车的剩余里程精准实时预测是动力电池能量管理系统(BMS)研究的重要领域之一.为解决其预测时误差大、自适应性差和数学建模复杂的难点,利用优化开路电压法,在电池包静止足够长和荷电状态较大阶段时预测荷电状态初值,并及时更新标准里程SN和标准准容量SOCN使其具有自适应性,消除累积误差,然后把坡度参数、非必要能耗设备容量参数转化为里程参数,最后建立优化OCV法数学模型及设计STM32硬件来预测剩余里程,利用EV-1型纯电动车试验记录测试值与预测值比较,其最大相对误差为5.2％,预测精度较现有其他方法有明显提高.