基于多混沌算子遗传算法的混合动力汽车控制策略优化

摘要/Abstract

摘要：

摘要：为某款装备了电池/超级电容混合储能系统的并联型混合动力汽车设计了模糊控制策略.结合遗传算法的种群进化和混沌序列的随机遍历特性，将混沌初始化算子、混沌扰动算子、混沌局部搜索算子引入多目标非占优排序遗传算法（NSGAII）中，构建了新的多混沌算子遗传算法（MCONSGAII）.运用MCONSGAII算法进行了混合动力汽车模糊控制策略优化，以改进车辆的燃油经济性及HC、CO和NOx的排放性能.仿真结果表明，混沌初始化算子和混沌扰动算子可以改善原NSGAII算法的搜索能力并增加种群多样性，而混沌局部搜索算子可以进一步增强算法局部搜索能力，能更好地搜索到理想的Pareto解集.运用MCONSGAII算法进行优化，使混合动力汽车在欧洲城市驾驶循环（ECE）下的燃油消耗降低了11.8%，HC、CO和NOx排放分别下降了7.72%、 15.72%和 11.77%.

关键词: 混沌算子, 遗传算法, 多目标优化, 混合动力汽车

Abstract:

Abstract： This paper presented a fuzzy logic control strategy for a parallel HEV equipped with battery/ultra capacitor based hybrid energy storage system. By combining the population evolution feature of genetic algorithm and the randomicity and ergodicity of chaos sequence, the chaotic initialization, disturbance and local search operators were introduced into non-donminated sorting genetic algorithm-II(NSGA-II) to construct a novel multichaotic operators NSGA-II (MCO-NSGA-II). MCO-NSGA-II was adopted to optimize the fuzzy control strategy for improving the fuel economy and emission performance of the target HEV. The results demonstrate that chaotic initialization, disturbance operators can improve the searching ability of NSGA-II and increase the diversity of the solutions. The chaotic local search operator can further improve the local searching ability to obtain better pareto solutions. By adopting MCO-NSGA-II, the fuel consumption of HEV under ECE driving cycle is reduced by 11.8% while the HC, CO and NOx emissions of HEV are decreased by 7.72%, 15.72% and 11.77%.

Key words: chaotic operators, genetic algorithm, multi-objective optimization, hybrid electric vehicle

中图分类号:

U 469.72文

梁俊毅，张建龙，马雪瑞，殷承良. 基于多混沌算子遗传算法的混合动力汽车控制策略优化[J]. 上海交通大学学报（自然版）, 2015, 49(04): 442-449.

LIANG Junyi,ZHANG Jianlong,MA Xuerui,YIN Chengliang. Control Strategy Optimization for Hybrid Electric Vehicle Based on Multi-Chaotic Operators Genetic Algorithm[J]. Journal of Shanghai Jiaotong University, 2015, 49(04): 442-449.

参考文献

［1］Ehsani M, Gao Y, Emadi A. Modern electric, hybrid electric, and fuel cell vehicles［M］. Boca Raton: CRC Press, 2009:12.

［2］Lee H D, Sul S K. Fuzzylogicbased torque control strategy for paralleltype hybrid electric vehicle［J］. IEEE Transactions on Industrial Electronics, 1998, 45(4): 625632.

［3］Xiong Weiwei, Zhang Yong, Yin Chengliang. Optimal energy management for a seriesparallel hybrid electric bus［J］. Energy Conversion and Management, 2009, 50(7): 17301738.

［4］Valerie H J, Keith B W, David J R. HEV control strategy for realtime optimization of fuel economy and emissions［J］. SAE Technical Paper, 2000, 2000011543.

［5］Zhang B, Chen Z, Mi C, et al. Multiobjective parameter optimization of a series hybrid electric vehicle using evolutionary algorithms［C］//Vehicle Power and Propulsion Conference 2009. Dearborn: IEEE, 2009:921925.

［6］Xiong Weiwei, Yin Chengliang, ZHANG Yong, et al. Seriesparallel hybrid vehicle control strategy design and optimization using realvalued genetic algorithm［J］. Chinese Journal of Mechanical Engineering, 2009, 22(6): 862868.

［7］张志英, 徐建祥,计峰.基于遗传算法的船舶分段堆场调度研究［J］.上海交通大学学报, 2013, 47(7): 10361042.

ZHANG Zhiying, XU Jianxiang, JI Feng. Shipbuilding yard scheduling approach based on genetic algorithm［J］. Journal of Shanghai Jiaotong University, 2013, 47(7): 10361042.

［8］陶猛,王广玮.基于多目标遗传算法的吸声覆盖层参数优化设计［J］.上海交通大学学报, 2013, 47(8): 13001305.

TAO Meng, WANG Guangwei. Parameter optimization of a sound absorption layer based on multiobjective genetic algorithm［J］. Journal of Shanghai Jiaotong University, 2013, 47(8): 13001305.

［9］Deb K, Pratap A, Agarwal S, et al.A fast and elitist multiobjective genetic algorithm: NSGAII［J］. IEEE Transactions on Evolutionary Computation, 2002， 6(2): 182197.

［10］Guo D, Wang J, Huang J, et al. ChaoticNSGAII: An effective algorithm to solve multiobjective optimization problems［C］//2010 International Conference on Intelligent Computing and Integrated Systems (ICISS). Guilin, China: IEEE, 2010: 2023.

［11］Ren Bingqun, Zhong Weizhou. Multiobjective optimization using chaos based PSO［J］. Information Technology Journal, 2011, 10(10): 19081916.

［12］Guo Yiqiang, Wu Yanbin, Ju Zhengshan, et al. Remote sensing image classification by the Chaos Genetic Algorithm in monitoring land use changes［J］. Mathematical and Computer Modelling, 2010, 51(11): 14081416.

［13］Eslami M, Shareef H, Mohamed A. Power system stabilizer design using hybrid multiobjective particle swarm optimization with chaos［J］. Journal of Central South University of Technology, 2011, 18(5): 15791588.

［14］Liang Junyi, Zhang Jianlong, Zhang Xi, et al. Energy management strategy for a parallel hybrid electric vehicle equipped with a battery/ultracapacitor hybrid energy storage system［J］. Journal of Zhejiang University SCIENCE A, 2013, 14(8): 535553.

［15］May R M. Simple mathematical models with very complicated dynamics［J］. Nature, 1976, 261(5560): 459467.

［16］Zitzler E, Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach［J］. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257271.

［17］Schott J R. Fault tolerant design using single and multicriteria genetic algorithm optimization［D］. Cambridge MA: Massachusetts Institute of Technology, 1995.

[1]	张澳圆, 胡小锋, 张亚辉. 多场景多目标动态变化下船舶小组立装焊重调度[J]. 上海交通大学学报, 2025, 59(4): 476-488.
[2]	张聪, 疏炳南, 张江涛, 金勇. 基于响应面法-遗传算法的船舶推进轴系多目标优化设计[J]. 上海交通大学学报, 2025, 59(4): 466-475.
[3]	孙栋一, 蒲宇亭, 章建榜. 基于GA-BP神经网络的防空导弹实时目标分配方法[J]. 空天防御, 2025, 8(1): 62-70.
[4]	米阳, 陈宇阳, 陈博洋, 韩云昊, 袁明瀚. 考虑微能网接入主动配电网的共享储能多目标配置[J]. 上海交通大学学报, 2024, 58(9): 1309-1322.
[5]	路庆昌, 刘鹏, 秦汉, 徐鹏程. 考虑路段恢复差异的道路网络恢复决策优化[J]. 上海交通大学学报, 2024, 58(7): 1118-1129.
[6]	张赫, 周正凯, 林环宇, 王天慈. 关键车道优先下的港区公铁平交道口优化研究[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(5): 791-800.
[7]	孙乾洋, 周利, 丁仕风, 刘仁伟, 丁一. 基于人工神经网络的极地船舶冰阻力预报方法[J]. 上海交通大学学报, 2024, 58(2): 156-165.
[8]	张良, 郑丽冬, 冷祥彪, 吕玲, 蔡国伟. 基于灰狼算法的风-光-抽水蓄能联合系统多目标优化策略[J]. 上海交通大学学报, 2024, 58(10): 1554-1566.
[9]	钟科星, 丁乐声, 张聪, 毛彦东, 陈金龙. 基于神经网络的风电海缆弯曲限制器优化设计[J]. 海洋工程装备与技术, 2024, 11(1): 70-76.
[10]	彭斌, 刘慧鑫, 陶耀辉. 基于变径基圆渐开线涡旋压缩机的几何模型及优化研究[J]. 上海交通大学学报, 2023, 57(8): 1046-1054.
[11]	赵志斌, 骆彬, 唐婷, 王春芳, 孙中华. 改进型自激谐振无线电能传输系统[J]. 上海交通大学学报, 2023, 57(7): 859-867.
[12]	蒋瑞民, 王宣灵, 张明恩, 赵斌. 基于量子遗传算法的反舰导弹航路规划方法[J]. 空天防御, 2023, 6(4): 31-34.
[13]	夏云松, 谭剑锋, 韩水, 高金娥. 基于反向传播神经网络的风力机涡流发生器优化[J]. 上海交通大学学报, 2023, 57(11): 1492-1500.
[14]	闫青, 鲁建厦, 江伟光, 邵益平, 汤洪涛, 李英德. 考虑双端口布局的紧致化仓储系统堆垛机路径优化[J]. 上海交通大学学报, 2022, 56(7): 858-867.
[15]	王箫剑, 洪君, 陈晶华, 李鸿光. 基于参数化建模和响应面优化的箱体减重研究[J]. 空天防御, 2022, 5(4): 60-66.