基于能量预测的增程式电动汽车分时混动能量管理策略  

Part-time Hybrid Energy Management Strategy for Range-extended Electric Vehicle Based on Energy Prediction

作　　者：林歆悠[1] 莫李平 罗勇[2] 张少博[1] 

机构地区：[1]福州大学机械工程及自动化学院,福州350002 [2]重庆理工大学汽车零部件先进制造技术教育部重点实验室,重庆400054

出　　处：《汽车工程》2017年第4期369-375,380,共8页Automotive Engineering

基　　金：国家自然科学基金(51505086);重庆市基础与前沿研究计划项目(cstc2013jcyj A60004);重庆市教委科学技术研究项目(KJ1500927)资助

摘　　要：本文中针对增程式电动车提出一种基于能量预测的分时混动能量管理策略。首先,根据静态导航历史数据,利用决策树算法的原理分别设计了基于移动平均和基于突发事件的两种能量预测算法;接着,对两种预测算法分别进行测试,分析其特点;最后,根据模拟的历史数据和未来数据,预测能量使用情况,分析预测精度,并比较分时混动能量管理策略在两种预测算法下的能量分配情况。结果表明:无论对于循环初期SOC的渐增,中期的突变,还是末期的波动,基于移动平均预测都优于基于突发事件的预测。A part-time hybrid energy management strategy based on energy prediction is proposed for rangeextended electric vehicle in this paper. Firstly according to static navigation data,two energy prediction algorithms based on moving average and emergency respectively are devised by utilizing the principle of decision tree algorithm.Then the two prediction algorithms are tested with their features analyzed respectively. Finally,according to historical and future data simulated,the situation of energy use is predicted,the accuracy of prediction is analyzed,and the energy distributions of part-time hybrid energy management strategy with two different prediction algorithms are compared. The results show that moving average-based prediction is better than emergency-based prediction no matter for the gradual increase of SOC in initial stage,its mutation in middle stage or its fluctuation in end stage.

关 键 词：增程式电动汽车 能量预测 分时混动 移动平均 突发事件 

分 类 号：U469.7[机械工程—车辆工程]