Optimization Design of Fuzzy Energy Management for Plug-in Hybrid Electric Vehicles

doi:10.16182/j.issn1004731x.joss.201801031

Abstract

Abstract: Because of complex system configuration, it is difficult to find precise mathematics model of PHEV drivetrain. The fuzzy controller design depends mainly on expert’s experience and has much subjectivity. A fuzzy energy management strategy (EMS) based on particle swarm optimization (PSO) is presented. In this EMS, the fuzzy rules obtained from expert knowledge are unaltered and the PSO is used to optimize the parameters of membership functions of the fuzzy controller. The provided EMS model is built by Matlab/simulink and embedded in the Advisor software for simulation and comparative analysis. The result shows that, compared with the conventional fuzzy EMS, the fuzzy EMS based on PSO can more effectively reduce oil consumption and reduce tail gas emission.

Key words: plug-in hybrid electric vehicle, energy management strategy, particle swarm optimization, fuzzy control

CLC Number:

TP391.9

Wu Xiaolan, Bai Zhifeng, Shi Xiaohui, Cao Binggang. Optimization Design of Fuzzy Energy Management for Plug-in Hybrid Electric Vehicles[J]. Journal of System Simulation, 2018, 30(1): 242-248.

References

[1] Silva C, Ross M, Farias T.Evaluation of Energy Consumption, Emissions and Cost of Plug-In Hybrid Vehicles[J]. Energy Conversion and Management (S0196-8904), 2009, 50(7): 1635-1643.
[2] Torres J L, Gonzalez R, Gimenez A, et al.Energy management strategy for plug-in hybrid electric vehicles. A comparative study[J]. Applied Energy(S0306-2619), 2014, 113: 816-824.
[3] 李锦, 徐兆坤, 许建昌. 浅谈PHEV的发展现状及趋势[J]. 上海汽车, 2009(2): 10-12.Li Jin, Xu Zhaokun, Xu Jianchang. Discussion of the Situation and Development Trend of PHEV[J]. SHANGHAI AUTO, 2009 (2): 10-12.
[4] Banvait H, Anwar S, Chen Y B.A Rule-Based Energy Management Strategy for Plug-in Hybrid Electric Vehicle (PHEV)[C]//Paper Presented at the American Control Conference, St. Louis, MO, USA, 2009.
[5] Moura S J, Fathy H K, Callaway D S, et al.A stochastic optimal control approach for power management in plug-in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology (S1063-6536), 2011, 19(3): 545-555.
[6] 张松, 吴光强, 郑松林. 插电式混合动力汽车能量管理策略多目标优化[J]. 同济大学学报(自然科学版), 2011, 39(7): 99-103,108. Zhang Song, Wu Guangqiang, Zheng Songlin. Multi-objective Optimization of Energy Management Strategy of Plug-in Hybrid Electric Vehicle [J]. Journal of Tongji University (Natural Science), 2011, 39(7): 99-103,108.
[7] 张博, 李君, 高莹, 等. Plug-in混合动力汽车能量管理策略优化设计[J]. 农业机械学报, 2009, 40(9): 20-25. Zhang Bo, Li Jun, Gao Ying, et al. Optimization of Plug-in Hybrid Electric Vehicle Energy Management Control Strategy [J]. Transactions of the Chinese Society for AGRICHLTURAL Machinery, 2009, 40(9): 20-25.
[8] 付主木, 王聪慧, 普杰信. PHEV模糊控制能量管理策略建模与仿真[J]. 系统仿真学报, 2012, 24(6): 50-54. Fu Zhumu, Wang Conghui, Pu Jiexin. Modeling and Simulation of Fuzzy Control Energy Management Strategy for Parallel Hybrid Electric Vehicle [J]. Journal of System Simulation, 2012, 24(6): 50-54.
[9] Bayindir K Ç, Gözüküçük M A, Teke A. A comprehensive overview of hybrid electric vehicle: Powertrain configurations, powertrain control techniques and electronic control units[J]. Energy Conversion and Management (S0196-8904), 2011, 52(2): 1305-1313.

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