串联式混合动力矿卡双模能量管理策略研究
Research on Dual-Mode Energy Management Strategy for a Series Hybrid Electric Mine Truck
梁岩岩 1刘吉超 1陈正 2杨海1
作者信息
- 1. 江苏汇智高端工程机械创新中心有限公司,江苏,徐州 221000
- 2. 中国矿业大学 材料与物理学院,江苏,徐州 221116
- 折叠
摘要
为充分发挥油电混动系统在矿卡上的节能减排性能,针对串联式混合动力矿卡提出一种双模能量管理策略(EMS).利用反向传播神经网络(BPNN)构建了"发动机最优油耗模式"和"增程器效率最优模式"模型,在此基础上,设计了一种双模EMS来调整增程器与电池包的功率输出,实现了整车在复杂工况下的能耗实时调整,以实际工况数据对提出的策略进行了硬件在环仿真验证.结果表明,与规则策略和等效能耗最小策略相比,双模EMS的节油率分别提升了12.74%和7.4%,进一步提升了实时策略的节油性能.
Abstract
To fully utilize the energy-saving and emission-reduction performance of the oil-electric hybrid system on mine truck,a dual-mode energy management strategy(EMS)is proposed for a series hybrid electric mine truck.The back propagation neural network(BPNN)was used to construct the models of"optimal fuel consumption mode of engine"and"optimal efficiency mode of range extender"in this EMS.On this basis,a dual-mode EMS was designed to adjust the power output of the range extender and battery pack,realizing the real-time adjustment of the energy consumption for the vehicle under complex working conditions.Finally,the proposed EMS was verified by hardware in the loop simulation with actual working condition data.The results show that compared with the rule strategy and equivalent consumption minimization strategy,the fuel saving rate of the dual-mode EMS increases by 12.74％ and 7.4％ respectively,further improving the fuel saving performance of the real-time strategy.
关键词
串联式混合动力/矿卡/双模/能量管理策略/反向传播神经网络Key words
series hybrid electric/mine truck/dual-mode/energy management strategy/back propagation neural network引用本文复制引用
基金项目
国家自然科学基金青年项目(62103415)
出版年
2024
国家科技期刊平台
NSTL
万方数据