Design and Optimization of Control Strategy for Plug-in 4WD Hybrid Electric Vehicles
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摘要: 为有效识别驾驶员的驾驶意图,在保障插电式四驱混合动力汽车动力性的基础上,提高其燃油经济性,提出了一种转矩识别系数计算方法,设计了基于发动机输出转矩最优能量管理控制策略,讨论了每种工作模式的判别条件以及转矩分配方法.为避免单一优化算法运算时间长、容易陷入局部最优的固有缺陷,使用优拉丁超立方的方法进行试验设计,利用径向基函数神经网络(radial basis function, RBF)建立近似模型,使用多岛遗传算法对近似模型进行了优化.研究结果表明:对优化后的控制策略进行离线仿真得出,混合动力汽车在满足动力性能的前提下,百公里油耗降低了16.4%;将优化后的控制策略在dSPACE上进行硬件在环试验表明,所制定的控制策略,可以实现基本的能量管理,且加入转矩识别之后平均车速误差降低了39.9%,百公里油耗降低了8.5%.Abstract: In order to identify drivers' intentions, and improve the fuel economy of plug-in 4WD hybrid electric vehicles (HEVs) with their power performances guaranteed, a method for calculating the torque identification coefficient was put forward. An energy management control strategy was designed based on the engine optimal control, and the judging condition of every working mode as well as its torque distribution method was introduced. In order to avoid the inherent defects of a single optimization algorithm that the calculation time was long and it was easy to end up with a local optimal solution, design of experiment (DOE) was conducted by the method of optimal Latin-hypercube design. An approximate model was designed using the RBF (radial basis function) neural network and then optimized using the multi-island genetic algorithm. In addition, an off-line simulation was conducted to verify the optimized control strategy. The results showed that the optimized control strategy could reduce the fuel consumption per 100 km of the plug-in hybrid electric vehicle by 16.4%, without degrading the power performances. After optimization, the control strategy is validated by a hardware-in-the-loop test on dSPACE and the experimental results show that the control strategy can realize the basic energy management. What's more, with torque identification, the average velocity error is reduced by 39.9% and the fuel consumption is reduced by 8.5%.
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Key words:
- hybrid electric vehicle /
- control strategy /
- design of experiment /
- approximate model /
- optimization
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肖任鑫,李涛,秦颖,等. 并联混合动力汽车能量管理的马尔科夫决策[J]. 西南交通大学学报,2012,47(6): 982-988. XIAO Renxin, LI Tao, QIN Ying, et al. Markov decision process for the energy management of parallel hybrid vehicles[J]. Journal of Southwest Jiaotong University, 2012, 47(6): 982-988. 钱立军,邱利宏,辛付龙,等. 基于模糊转矩识别的混合动力汽车控制策略[J]. 科学技术与工程,2014,14(35): 135-141. QIAN Lijun, QIU Lihong, XIN Fulong, et al. Control strategy of a HEV based on fuzzy torque identification[J]. Science Technology and Engineering, 2014, 14(35): 135-141. 钱立军,邱利宏,辛付龙,等. 插电式四驱混合动力汽车能量管理与转矩协调控制策略[J]. 农业工程学报,2014,30(19): 55-64. QIAN Lijun, QIU Lihong, XIN Fulong, et al. Energy management and torque coordination control for plug-in 4WD hybrid electric vehicle[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(19): 55-64. WU Lianghong, WANG Yaonan, YUAN Xiaofang, et al. Multi-objective optimization of HEV fuel economy and emissions using the self-adaptive differential evolution algorithm[J]. IEEE Transactions on Vehicular Technology, 2011, 60(6): 2458-2470. 秦大同,彭志远,刘永刚,等. 基于工况识别的混合动力汽车动态能量管理策略[J]. 中国机械工程,2014,25(11): 1550-1555. QIN Datong, PENG Zhiyuan, LIU Yonggang, et al. Dynamic energy management strategy of HEV based on driving pattern recognition[J]. Chinese Journal of Mechanical Engineering, 2014, 25(11): 1550-1555. 王欣,李高,朱万力,等. 并联混合动力汽车能量管理建模及优化研究[J]. 控制工程,2014,21(3): 357-360. WANG Xin, LI Gao, ZHU Wanli, et al. Modeling and optimization simulation on energy management of PHEV[J]. Control Engineering of China, 2014, 21(3): 357-360. 杨观赐,李少波,璩晶磊,等. 基于Pareto最优原理的混合动力汽车多目标优化[J]. 上海交通大学学报,2012,46(8): 1297-1304. YANG Guanci, LI Shaobo, QU Jinglei, et al. Multi-objective optimization of hybrid electrical vehicle based on Pareto optimality[J]. Journal of Shanghai Jiaotong University, 2012, 46(8): 1297-1304. NEJHAD A Z, ASAEL B. A fuzzy-genetic algorithm approach for finding a new HEV control strategy idea[C]// PEDSTC 2010 1st Power Electronics and Drive Systems and Technologies Conference. Tehran: IEEE, 2010: 224-229. 王庆年,唐先智,王鹏宇,等. 基于驾驶意图识别的混合动力汽车控制策略[J]. 吉林大学学报:工学版,2012,42(4): 789-795. WANG Qingnian, TANG Xianzhi, WANG Pengyu, et al. Control strategy of hybrid electric vehicle based on driving intention identification[J]. Journal of Jilin University: Engineering and Technology Edition, 2012, 42(4): 789-795. OPRICA T, VINATORU M. Vehicle-following modeling utilizing neuro-fuzzy networks[C]//International Joint Conference on Computational Cybernetics and Technical Informatics. Timisoara: IEEE, 2010: 67-71. DONGSUK K, HUEI P, BUCKNOR N K. Control of engine-starts for optimal drivability of parallel hybrid electric vehicles[J]. Journal of Dynamic Systems, Measurement, and Control, 2013, 135(2): 1-10. YANG Zhenzhong, WAN Lijun, HE M, et al. Research on optimal control to resolve the contradictions between restricting abnormal combustion and improving power output in hydrogen fueled engines[J]. International Journal of Hydrogen Energy, 2012, 37(1): 774-782. 赖宇阳. Isight参数优化理论与实例详解[M]. 北京: 北京航空航天大学出版社,2012: 94-95. SUN H, TOSSAN B, BROUNS D. Behavior study on HEV air-cooled battery pack[J]. SAE Technical Paper, 2011, No. 2011-01-1368: 1-11. 李振磊. 基于双离合器的插电式MPV混合动力系统匹配技术研究[D]. 长沙:湖南大学,2010. OSORIO J, MOLINA A, PONCE P, et al. A supervised adaptive neuro-fuzzy inference system controller for a hybrid electric vehicle's power train system[C]// IEEE International Conference on Control and Automation. Santiago: IEEE, 2011: 404-409.
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