并联混合动力汽车工况识别与参数优化
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摘要
针对现有混合动力汽车能量管理策略存在的局限性,以某型并联混合动力汽车为研究对象,基于ADVISOR车辆仿真软件中24种标准工况构建组合工况,选定车辆行驶过程中的平均速度、平均绝对加速度以及怠速时间比3个参数为工况识别特征量,利用K均值聚类算法得到4种典型行驶工况,建立整车能耗优化目标函数,采用粒子群算法对电量消耗-电量维持型(CD-CS)规则控制策略主要参数离线寻优,得到4种典型行驶工况下的功率分配权重,且在MATLAB/Simulink平台上建立整车仿真模型验证。仿真结果表明,所采用控制策略能够准确识别行驶工况,与未采用工况识别的能量管理策略相比,电机与发动机间的能量分配更加合理,车辆综合油耗下降5.45%,电池荷电状态变化更加平稳。
In view of limitations of current energy management strategies for hybrid electric vehicle(HEV), take a certain type of parallel hybrid electric vehicle(PHEV) as the research object, the synthetic cycle was built based on 24 standard driving cycles of ADVISOR vehicle simulation software, three parameters, namely mean speed,mean absolute acceleration and idle time ratio of the combined cycle were selected as the features of driving cycles recognition. 4 typical driving cycles were obtained by K means clustering algorithm. An optimization function of vehicle energy consumption was established, and the particle swarm optimization(PSO) algorithm was implemented to optimize the main control parameters of charge depletion-charge sustaining(CD-CS) rule-based control strategy, power distribution weights of 4 typical driving cycles were determined. The vehicle simulation model was built based on the MATLAB/Simulink platform, the simulation results show that the proposed control strategy can accurately identify driving cycle. Compared with the CD-CS rule-based control strategy without driving cycles recognition, the energy allocation between motor and engine is more reasonable, and the vehicle fuel consumption is reduced by 5.45%, besides, the battery state of changel is more stable.
In view of limitations of current energy management strategies for hybrid electric vehicle(HEV), take a certain type of parallel hybrid electric vehicle(PHEV) as the research object, the synthetic cycle was built based on 24 standard driving cycles of ADVISOR vehicle simulation software, three parameters, namely mean speed,mean absolute acceleration and idle time ratio of the combined cycle were selected as the features of driving cycles recognition. 4 typical driving cycles were obtained by K means clustering algorithm. An optimization function of vehicle energy consumption was established, and the particle swarm optimization(PSO) algorithm was implemented to optimize the main control parameters of charge depletion-charge sustaining(CD-CS) rule-based control strategy, power distribution weights of 4 typical driving cycles were determined. The vehicle simulation model was built based on the MATLAB/Simulink platform, the simulation results show that the proposed control strategy can accurately identify driving cycle. Compared with the CD-CS rule-based control strategy without driving cycles recognition, the energy allocation between motor and engine is more reasonable, and the vehicle fuel consumption is reduced by 5.45%, besides, the battery state of changel is more stable.
引文
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[4]王钦普,游思雄,李亮,等.插电式混合动力汽车能量管理策略研究综述[J].机械工程学报,2017, 53(16):1-19.
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[10]蔡锷,李阳阳,李春明,等.基于K均值聚类算法的西安市汽车行驶工况合成技术研究[J].汽车技术,2015(8):33-36.
[11]杨官龙.基于驾驶意图与工况识别的插电式混合动力汽车能量管理策略研究[D].重庆:重庆大学,2014:96-109.
[12]秦大同,杨官龙,刘永刚,等. Plug-in并联式单电机混合动力汽车能量管理优化控制策略[J].中国公路学报,2013, 26(5):170-176.
[13]徐强,章家岩,杜翠翠,等.基于遗传算法的水下机器人滑模变结构控制策略[J].安徽工业大学学报(自然科学版),2016,33(03):261-265.
[14]王钦普,杜思宇,李亮,等.基于粒子群算法的插电式混合动力客车实时策略[J].机械工程学报, 2017, 53(4):77-84.
[2] CAI Y, OU Y M, YANG F. Energy management and design optimization for a series-parallel PHEV city bus[J]. International Journal of Automotive Technology, 2017, 18(3):473-487.
[3] YAN B, HU Y Q, YAN T, et al. Route-based adaptive optimization for energy management of hybrid electric vehicles[J]. International Journal of Automotive Technology, 2014, 15(7):1175-1182.
[4]王钦普,游思雄,李亮,等.插电式混合动力汽车能量管理策略研究综述[J].机械工程学报,2017, 53(16):1-19.
[5] PANDAY A, BANSAL H O. A review of optimal energy management strategies for hybrid electric vehicle[J]. International Journal of Vehicular Technology, 2014, 15(8):1-9.
[6]丁峰,王伟达,项昌乐,等.基于行驶工况分类的混合动力车辆速度预测方法与能量管理策略[J].汽车工程,2017, 39(11):1223-1231.
[7] WU J,CUI N X. Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition[J]. International Journal of Automotive Technology, 2012, 13(7):1159-1167.
[8] WANG J,WANG Q N,ZENG X H, et al. Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles[J]. International Journal of Automotive Technology, 2015, 16(4):685-695.
[9] JAIN, ANIL K. Data clustering:50 years beyond K means[J]. Pattern Recognition Letters, 2010, 31(8):651-666.
[10]蔡锷,李阳阳,李春明,等.基于K均值聚类算法的西安市汽车行驶工况合成技术研究[J].汽车技术,2015(8):33-36.
[11]杨官龙.基于驾驶意图与工况识别的插电式混合动力汽车能量管理策略研究[D].重庆:重庆大学,2014:96-109.
[12]秦大同,杨官龙,刘永刚,等. Plug-in并联式单电机混合动力汽车能量管理优化控制策略[J].中国公路学报,2013, 26(5):170-176.
[13]徐强,章家岩,杜翠翠,等.基于遗传算法的水下机器人滑模变结构控制策略[J].安徽工业大学学报(自然科学版),2016,33(03):261-265.
[14]王钦普,杜思宇,李亮,等.基于粒子群算法的插电式混合动力客车实时策略[J].机械工程学报, 2017, 53(4):77-84.