为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误...为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.展开更多
为充分利用电网电能,以增程式电动汽车(Range-Extended Electric Vehicles,REEV)为研究对象,基于车载导航系统、智能交通系统(ITS)以及智能电网系统,运用迪克斯特拉(Dijkstra)算法对REEV进行充电路径规划,引入里程信息以及参考电池剩余...为充分利用电网电能,以增程式电动汽车(Range-Extended Electric Vehicles,REEV)为研究对象,基于车载导航系统、智能交通系统(ITS)以及智能电网系统,运用迪克斯特拉(Dijkstra)算法对REEV进行充电路径规划,引入里程信息以及参考电池剩余电量(SOC),并根据路径规划结果,综合考虑车辆的燃油经济性和排放性,对规划路径制定分段里程自适应控制策略,使REEV能够在不同里程段内合理分配辅助动力装置(APU)和电池的能量。基于Matlab/Simulink搭建整车控制策略仿真模型进行仿真验证,结果表明,当行驶里程约175km时,在牺牲一部分充电时间的代价下,车辆节省了1.77L燃油,REEV的油耗下降率为92.64%,所制定的分段里程自适应控制策略能够充分利用电网电能,车辆节能效果明显。展开更多
Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of c...Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization（PBIO） energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus（SPHEB） dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.展开更多
文摘为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.
文摘为充分利用电网电能,以增程式电动汽车(Range-Extended Electric Vehicles,REEV)为研究对象,基于车载导航系统、智能交通系统(ITS)以及智能电网系统,运用迪克斯特拉(Dijkstra)算法对REEV进行充电路径规划,引入里程信息以及参考电池剩余电量(SOC),并根据路径规划结果,综合考虑车辆的燃油经济性和排放性,对规划路径制定分段里程自适应控制策略,使REEV能够在不同里程段内合理分配辅助动力装置(APU)和电池的能量。基于Matlab/Simulink搭建整车控制策略仿真模型进行仿真验证,结果表明,当行驶里程约175km时,在牺牲一部分充电时间的代价下,车辆节省了1.77L燃油,REEV的油耗下降率为92.64%,所制定的分段里程自适应控制策略能够充分利用电网电能,车辆节能效果明显。
基金supported by National Natural Science Foundation of China(Grant No. 51075410)
文摘Energy management（EM） is a core technique of hybrid electric bus（HEB） in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization（PBIO） energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus（SPHEB） dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.
