基于LVQ工况识别的混合动力汽车自适应能量管理控制策略

摘要/Abstract

摘要：

为提高混合动力汽车的燃油经济性，选取6种典型行驶工况代表“市区”、“郊区”和“高速公路”3类主要工况，采用基于规则的模糊能量管理控制策略，以整车燃油经济性为目标，在3类主要工况下用改进型粒子群优化算法优化发动机联合工作曲线与发动机关闭曲线系数，得到相应的优化后的隶属度函数的参数;运用学习向量量化(LVQ)算法识别车辆运行工况，动态选择相应的模糊控制策略，使混合动力汽车控制策略对选定的几种代表性工况具有自适应性，从而提高整车的燃油经济性。仿真对比结果表明，相比于传统混合动力汽车，燃油经济性提高了3.4%。

关键词: 混合动力汽车, 工况识别, 燃油经济性, 粒子群优化算法, 学习向量量化(LVQ)算法

Abstract:

In order to reduce fuel consumption, six kinds of typical driving cycles were chosen to represent the “urban”, “suburban” and “highway”. And for vehicle fuel economy, based on fuzzy control strategy with rules,an improved PSO algorithm was adopted to optimize the engine working with motor curve factor and the engine shutting off curve factor, then the optimized parameters of membership function could be achieved under the above typical driving cycles. Furthermore, LVQ algorithm was adopted to recognize real-time driving cycle, the corresponding fuzzy control strategy could be chosen according to the recognition results, which maintained the adaptability for those driving cycles, and improved HEV's fuel economy. Simulation results show that fuel economy adopted with this control strategy is improved by 3.4% comparing to the traditional methods without cycle recognition.
hybrid electric vehicle(HEV); driving cycle recognition; fuel economy;

Key words: particle swarm optimization(PSO) algorithm, learning vector quantization(LVQ) algorithm

中图分类号:

U469.72

邓涛, 卢任之, 李亚南, 林椿松. 基于LVQ工况识别的混合动力汽车自适应能量管理控制策略[J]. 中国机械工程, 2016, 27(03): 420-425.

Deng Tao, Lu Renzhi, Li Yanan, Lin Chunsong. Adaptive Energy Control Strategy of HEV Based on Driving Cycle Recognition by LVQ Algorithm[J]. China Mechanical Engineering, 2016, 27(03): 420-425.

参考文献

[1]Williamson S S, Emadi A, Dewan A. Effects of Varying Driving Schedules on the Drive Train Efficiency and Performance Characteristics of a Parallel Diesel-Hybrid Bus[J].SAE Technical Paper, 2005-01-3477.

[2]Sharer P, Leydier R, Rousseau A. Impact of Drive Cycle Aggressiveness and Speed on HEV's Fuel Consumption Sensitivity[J].SAE Technical Paper, 2007-01-0281.

[3]Jeon Soon-Il, Jo Sung-Tae, Park Yeong-Il,et al.Multi-Mode Driving Control of a Parallel Hybrid Electric Vehicle Using Driving Pattern Recognition[J]. Journal of Dynamic Systems, Measurement, and Control, 2002,124(1):141-149.

[4]Langari R, Won Jong-Seob. Intelligent Energy Management Agent for a Parallel Hybrid Vehicle-Part I: System Architecture and Design of the Driving Situation Identification Process[J]. IEEE Transactions on Vehicular Technology, 2005, 54(3): 925-934.

[5]田毅, 张欣, 张良, 等. 神经网络工况识别的混合动力电动汽车模糊控制策略[J]. 控制理论与应用, 2011,28(3):363-369.

Tian Yi, Zhang Xin, Zhang Liang, et al. Fuzzy Control Strategy for Hybrid Electric Vehicle Based on Neural Network Identification of Driving Conditions[J]. Control Theory & Applications,2011,28(3):363-369.

[6]吴剑. 并联式混合动力汽车能量管理策略优化研究[D] 济南: 山东大学, 2008.

[7]Brooker A, Haraldsson K, Hendricks T, et al.ADVISOR Documentation. Colorado:National Renewable Energy Laboratory, 2002.http://www.ctts.nrel.gov/analysis/advisor_doc/.html.

[8]Wang R, Lukic S M. Review of Driving Conditions Prediction and Driving Style Recognition Based Control Algorithms for Hybrid Electric Vehicles[C]//Vehicle Power and Propulsion Conference (VPPC), IEEE. Chicago,2011: 1-7.

[9]Feng Lei, Liu Wenjia, Chen Bo. Driving Pattern Recognition for Adaptive Hybrid Vehicle Control[J].SAE Technical Paper, 2012-01-0742.

[10]Jie Xing, Han Xuefeng, Ye Hui, et al. Driving Cycle Recognition for Hybrid Electric Vehicle[C]//Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), IEEE Conference and Expo. Beijing, 2014:1-6.

[11]林歆悠, 孙冬野. 基于工况识别的混联式混合动力客车控制策略研究[J]. 中国机械工程, 2012,23(7):869-874.

Lin Xinyou, Sun Dongye. Development of Control Strategy Fora Series-Parallel Hybrid Electric City Bus Based on Roadway Type Recognition[J]. China Mechanical Engineering,2012,23(7):869-874.

[1]	刘涛;张丽芳. 基于改进粒子群优化算法的变截面涡旋盘瞬时铣削力模型参数求解[J]. 中国机械工程, 2020, 31(24): 2943-2949.
[2]	黄强先;岳龙龙;郭小倩;程荣俊;梅腱;李红莉;张连生. 基于最小包容区域的球度误差评定方法[J]. 中国机械工程, 2020, 31(12): 1387-1393.
[3]	孔骏成;李菊;沈惠平. 2-RPaRSS并联操作手运动副间隙误差分析及补偿[J]. 中国机械工程, 2020, 31(06): 706-713.
[4]	张青雷1;张帆2;段建国1. 基于DMPSO算法的大型船舶动力部件生产车间布局优化[J]. 中国机械工程, 2020, 31(03): 344-351.
[5]	孙明翰;许哲;郑立康;许志强;杜凤山. 基于改进型粒子群优化算法的双辊薄带振动铸轧压下控制系统优化[J]. 中国机械工程, 2020, 31(03): 360-366.
[6]	周友行;马逐曦;石弦韦;孔拓. HSI颜色空间下的直线导轨表面缺陷检测方法[J]. 中国机械工程, 2019, 30(18): 2179-2184.
[7]	隗寒冰；任培林. 插电式柴电混合动力汽车规则控制及处理器在环测试[J]. 中国机械工程, 2019, 30(13): 1590-1599.
[8]	章俨1；杨义勇1；李亮2；李卫兵3；吴琼3. 双电机混联构型混动车辆的制动能量回收策略[J]. 中国机械工程, 2019, 30(13): 1631-1637.
[9]	吴进军1;颜丙杰2;方继根1;王西峰1;谢志鹏3;史洋4;李亮2. 插电式混合动力汽车的次优能量管理策略[J]. 中国机械工程, 2019, 30(11): 1336-1342.
[10]	胡茂杨;常思勤;刘梁;陆佳瑜;徐亚旋. 车用汽油机停缸时循环功耗研究[J]. 中国机械工程, 2019, 30(01): 46-52.
[11]	陈亚绒, 黄佩钰, 李沛, 周富得, 黄沈权. [车间调度]并行多机开放车间调度问题的模型与算法[J]. 中国机械工程, 2018, 29(22): 2666-2673,2681.
[12]	石琴, 仇多洋, 吴冰, 李一鸣, 刘炳姣. [智能感知]基于粒子群优化支持向量机算法的行驶工况识别及应用[J]. 中国机械工程, 2018, 29(15): 1875-1883.
[13]	钱立军1;荆红娟1;邱利宏1，2. 基于随机模型预测控制的四驱混合动力汽车能量管理[J]. 中国机械工程, 2018, 29(11): 1342-1348.
[14]	孟蓉歌1,2张春化1梁继超1. 改进粒子群优化-Elman算法在发动机曲轴脉宽预测中的应用[J]. 中国机械工程, 2018, 29(07): 766-770.
[15]	邓涛;韩海硕;罗俊林. 基于动态规划算法的混合动力汽车改进型ECMS能量管理控制研究[J]. 中国机械工程, 2018, 29(03): 326-332.