Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Abstract

Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various charging modes, the traditional charging time prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) charging modes, a charging time prediction method with charging mode recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and random forest fusion is proposed to classify vehicle charging modes. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance charging time prediction method is constructed by fully integrating long short-term memory (LSTM) and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed charging time prediction algorithm. Experimental results show that the new method can effectively distinguish the charging modes of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.

摘要

电动汽车动力电池过度充电容易导致电池加速老化和严重的安全事故。因此, 准确预测车辆充电时间对充电安全防护意义重大。由于电池组结构复杂, 充电方式多样, 传统方法因缺乏充电模式识别而预测精度不高。本文应用数据驱动和机器学习理论, 提出一种新的基于充电模式识别的充电时间预测方法。首先, 基于动态加权密度峰值聚类(DWDPC)和随机森林融合的智能算法对车辆充电模式进行分类; 然后, 采用改进的简化粒子群优化算法(ISPSO)和强跟踪滤波器(STF), 对LSTM神经网络进行优化, 构建了一种高性能的充电时间预测方法; 最后, 通过实际工程数据对所提出的ISPSO-LSTM-STF方法进行了验证。实验结果表明, 该方法能够有效区分充电模式, 提高了充电时间预测精度, 具有实际工程意义。