纯电动汽车再生制动控制策略的研究
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摘要
为了提高电动汽车制动能量的回收效率,增加汽车续驶里程,本文针对前、后轮制动力和再生制动力的分配策略进行了研究。结果表明,在制定前、后轮制动力分配策略时,采用以路面特征值识别为前提,将f线、ECE法规线和I曲线相结合的方法,根据当前路面的附着系数选择不同的控制策略,可使汽车在获得较大制动力的同时确保制动的方向稳定性;在制定再生制动力分配策略时,根据车辆实时工况,采用模糊控制的方法分配驱动轮上的再生制动力,可提高制动能量的回收效率。建立了再生制动控制策略的仿真模型,并在CYC_1015和CYC_UDDS两种工况下进行模拟仿真,仿真结果表明,本文提出的控制策略比ADVISOR原车控制策略能更好地实现制动能量回收,提高了纯电动汽车的续驶里程。
In order to improve the recovery efficiency of regenerative braking energy and increase driving range, this paper studies the distribution strategy of front and rear braking force and regenerative braking force. The results show that,when developing the strategy of braking force distribution on front and rear wheels, the method that combines f-line, ECE regulation line and I-curve to select different control strategies according to adhesion coefficient of road surface, can ensure braking direction stability of vehicles while obtaining huge braking force based on the recognition of road eigenvalue. When developing distribution strategy of regenerative braking force, fuzzy control is used according to the real-time working condition of the vehicle, to distribute the regenerative braking force on the driving wheel to improve the recovery efficiency of regenerative braking energy. The simulation model of regenerative braking control strategy is built and simulated in CYC_1015 conditions and CYC_UDDS conditions. The simulation results show that the proposed control strategy can recover more braking energy and improve the driving range of pure electric vehicle than the original ADVISOR control strategy.
In order to improve the recovery efficiency of regenerative braking energy and increase driving range, this paper studies the distribution strategy of front and rear braking force and regenerative braking force. The results show that,when developing the strategy of braking force distribution on front and rear wheels, the method that combines f-line, ECE regulation line and I-curve to select different control strategies according to adhesion coefficient of road surface, can ensure braking direction stability of vehicles while obtaining huge braking force based on the recognition of road eigenvalue. When developing distribution strategy of regenerative braking force, fuzzy control is used according to the real-time working condition of the vehicle, to distribute the regenerative braking force on the driving wheel to improve the recovery efficiency of regenerative braking energy. The simulation model of regenerative braking control strategy is built and simulated in CYC_1015 conditions and CYC_UDDS conditions. The simulation results show that the proposed control strategy can recover more braking energy and improve the driving range of pure electric vehicle than the original ADVISOR control strategy.
引文
[1]翟志强,赵国柱,朱思洪.电动汽车再生制动控制策略研究[J].计算机仿真,2013,30(11):160-163+280.
[2]王耀南,刘东奇.电动汽车机电复合制动力分配策略研究[J].控制工程,2014,21(3):347-356.
[3]靳立强,孙志祥,王熠,等.基于模糊控制的电动轮汽车再生制动能量回收研究[J].汽车工程,2017,39(10):1101-1105+1197.
[4]杨小龙,杨功正,张泽坪.基于多因素输入模糊控制的再生制动策略[J].湖南大学学报(自然科学版),2017,44(10)17-24.
[5]王博,孙仁云.基于状态特征因子的路面识别方法研究[J].汽车工程,2012,34(6):506-510.
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[7]余志生.汽车理论[M].北京:机械工业出版社,2009.
[8]顾亚松.基于ADVISOR的纯电动汽车再生制动控制策略研究[D].西安:长安大学,2014.
[9]尹安东,孟甜甜.电动汽车再生制动模糊控制策略与优化研究[J].农业装备与车辆工程,2016,51(14):1-6.
[10]刘文涛.纯电动中巴车整车控制及仿真研究[D].哈尔滨哈尔滨工业大学,2011.
[11]姜标,张向文,汪阳雄.基于制动力变比值优化分配算法的电动汽车再生制动控制策略研究[J].汽车技术,2017(6):29-34.
[2]王耀南,刘东奇.电动汽车机电复合制动力分配策略研究[J].控制工程,2014,21(3):347-356.
[3]靳立强,孙志祥,王熠,等.基于模糊控制的电动轮汽车再生制动能量回收研究[J].汽车工程,2017,39(10):1101-1105+1197.
[4]杨小龙,杨功正,张泽坪.基于多因素输入模糊控制的再生制动策略[J].湖南大学学报(自然科学版),2017,44(10)17-24.
[5]王博,孙仁云.基于状态特征因子的路面识别方法研究[J].汽车工程,2012,34(6):506-510.
[6]王博.汽车路面附着状况识别方法研究[D].成都:西华大学,2013.
[7]余志生.汽车理论[M].北京:机械工业出版社,2009.
[8]顾亚松.基于ADVISOR的纯电动汽车再生制动控制策略研究[D].西安:长安大学,2014.
[9]尹安东,孟甜甜.电动汽车再生制动模糊控制策略与优化研究[J].农业装备与车辆工程,2016,51(14):1-6.
[10]刘文涛.纯电动中巴车整车控制及仿真研究[D].哈尔滨哈尔滨工业大学,2011.
[11]姜标,张向文,汪阳雄.基于制动力变比值优化分配算法的电动汽车再生制动控制策略研究[J].汽车技术,2017(6):29-34.