采用安全态势评估的PHEV节能控制策略
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摘要
为了同时提高插电式混合动力汽车(PHEV)的行驶安全性和经济性,首先利用组合优化算法以实际道路工况对关键控制参数进行优化,实现将需求转矩合理分配到电机和发动机。其次,通过共用多种传感器所获取的驾驶行为特征和车间运动特征,结合模糊推理和数据驱动的方法,对当前行驶情景的安全态势进行评估。之后,依托安全态势量化值对当前情景的电机输出转矩进行不同程度的修正,既可以避免出现较大的加速度,又可以获得更多的制动能量回收。最后,进行了硬件在环试验,结果表明:通过实际工况优化关键控制参数,使得文中策略较以新标欧洲循环测试(NEDC)或联邦城市运行(FUDS)工况优化后的能耗分别降低了6.69%,5.7%;通过根据安全态势对电机转矩进行修正,使得文中策略较以实际工况优化后的能耗进一步降低了2.09%,并且对车速控制具有一定的"削峰填谷"作用,从而可实现安全性与经济性的双重效果提高。
For further improving the safety and economy of plug-in hybrid electric vehicle(PHEV), the key control parameters are optimized by actual driving cycle using a combinatorial optimization algorithm to realize the reasonable distribution of demand torque to motor and engine. Then, through sharing and using the driving behavior and inter-vehicle motion characteristics obtained from multiple sensors, the security situation of the current driving scenario is estimated by fuzzy inference and data-driven method. Meanwhile, according to the security situation, the motor torque is properly amended to avoid large acceleration and obtain more braking energy recycling. Finally, an HIL simulation is conducted and the results show that through the optimization on key control parameters, the energy consumption is improved by 6.69% and 5.7% compared with that optimized by NEDC and FUDS, respectively. Through motor torque amendment based on security situation, the energy consumption is further reduced by 2.09% compared with that optimized by actual driving cycle, and there is also a peak-cutting and valley-filling function on the speed change, resulting in improvement both in safety and economy.
For further improving the safety and economy of plug-in hybrid electric vehicle(PHEV), the key control parameters are optimized by actual driving cycle using a combinatorial optimization algorithm to realize the reasonable distribution of demand torque to motor and engine. Then, through sharing and using the driving behavior and inter-vehicle motion characteristics obtained from multiple sensors, the security situation of the current driving scenario is estimated by fuzzy inference and data-driven method. Meanwhile, according to the security situation, the motor torque is properly amended to avoid large acceleration and obtain more braking energy recycling. Finally, an HIL simulation is conducted and the results show that through the optimization on key control parameters, the energy consumption is improved by 6.69% and 5.7% compared with that optimized by NEDC and FUDS, respectively. Through motor torque amendment based on security situation, the energy consumption is further reduced by 2.09% compared with that optimized by actual driving cycle, and there is also a peak-cutting and valley-filling function on the speed change, resulting in improvement both in safety and economy.
引文
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[2] 孙超.混合动力汽车预测能量管理研究 [D].北京:北京理工大学,2016:1-2.
[3] 高建平,郗建国.新能源汽车概论 [M].北京:机械工业出版社,2018:27-31.
[4] YANG Chao,DU Siyu,LI Liang,et al.Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle [J].Applied Energy,2017,203:883-896.
[5] ZHANG Shuo,XIONG Rui,SUN Fengchun.Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system [J].Applied Energy,2015,185:1654-1662.
[6] WANG Yanzi,WANG Weida,ZHAO Yulong,et al.A fuzzy-logic power management strategy based on Markov random prediction for hybrid energy storage systems [J].Energies,2016,9(1):1-20.
[7] LEI Zhenzhen,CHENG Dong,LIU Yonggang,et al.A dynamic control strategy for hybrid electric vehicles based on parameter optimization for multiple driving cycles and driving pattern recognition [J].Energies,2017,10(1):1-20.
[8] 解来卿,张东好,罗禹贡,等.雷达共用型智能混合动力汽车节能控制策略 [J].清华大学学报(自然科学版),2018,58(3):286-291.XIE Laiqing,ZHANG Donghao,LUO Yugong,et al.Radar sharing energy-saving control strategy for intelligent hybrid electric vehicle [J].Journal of Tsinghua University (Science and Technology),2018,58(3):286-291.
[9] DING Feng,JIN Hui.On the optimal speed profile for eco-driving on curved roads [J].IEEE Transactions on Intelligent Transportation Systems,2018,19(12):4000-4010.
[10] LI Liang,YOU Sixiong,YANG Chao,et al.Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses [J].Applied Energy,2016,162:868-879.
[11] XIONG Xiaoxia,CHEN Long,LIANG Jun.Vehicle driving risk prediction based on Markov chain model [J].Discrete Dynamics in Nature and Society,2018,2018:1-12.
[12] ZHAO R C,WONG P K,XIE Z C,et al.Real-time weighted multi-objective model predictive controller for adaptive cruise control systems [J].International Journal of Automotive Technology,2017,18(2):279-292.
[13] 裴晓飞,刘昭度,马国成,等.汽车自适应巡航系统的多模式切换控制 [J].机械工程学报,2012,48(10):96-102.PEI Xiaofei,LIU Zhaodu,MA Guocheng,et al.Multi-mode switching controller for vehicle adaptive cruise control system [J].Journal of Mechanical Engineering,2012,48(10):96-102.
[14] CAO Hui,JIA Lixin,SI Gangquan,et al.A clustering-analysis-based membership functions formation method for fuzzy controller of ball mill pulverizing system [J].Journal of Process Control,2013,23(1):34-43.
[15] TASS International.PreScan overview [EB/OL].[2019-01-02].https://www.tassinternational.com/prescan-overview.
[16] YIN Jiali,CHEN Bohao,ROBERT L K,et al.Automatic dangerous driving intensity analysis for advanced driver assistance systems from multimodal driving signals [J].IEEE Sensors Journal,2017,18(12):4785-4794.
[17] 邱小平,孙若晓,于丹,等.改进的基于模糊推理的车辆跟驰行为分析方法 [J].华南理工大学学报(自然科学版),2015,43(8):135-143.QIU Xiaoping,SUN Ruoxiao,YU Dan,et al.An improved method to analyze car-following behavior based on fuzzy inference system [J].Journal of South China University of Technology (Natural Science),2015,43(7):135-143.
[18] 高建平,孙中博,丁伟,等.车辆行驶工况的开发和精度研究 [J].浙江大学学报(工学版),2017,51(10):2046-2054.GAO Jianping,SUN Zhongbo,DING Wei,et al.Development of vehicle driving cycle and accuracy of research [J].Journal of Zhejiang University (Engineering Science),2017,51(10):2046-2054.
[19] 高建平,孙中博,张磊敏,等.基于实际工况的插电式混合动力公交车参数自适应控制策略研究 [J].西安交通大学学报,2017,51(7):130-139.GAO Jianping,SUN Zhongbo,ZHANG Leimin,et al.Plug-in hybrid electric bus variable parameter control strategy considering influence of driving cycle [J].Journal of Xi’an Jiaotong University,2017,51(7):130-139.