This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Research on PHEV Multi-Objective Energy Management Strategy Based on Genetic Algorithm Optimized Feed-Forward-Neural Network
Technical Paper
2022-01-7048
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
With the transformation and upgrading of the automotive industry, plug-in hybrid
vehicles have become a research hotspot in the automotive field due to their
advantages such as long range and environmental efficiency. Most of the previous
studies on P13 plug-in hybrid vehicles have focused on their economy, ignoring
the problem of the balance between fuel economy and emission performance. In
this paper, the CS stage control strategy is optimized, and a control algorithm
based on neural network-genetic algorithm multi-objective optimization is
designed for the engine stable operation conditions. In this paper, the fuel
consumption and NOX emission of the engine are taken as the
optimization objectives, and the engine series torque and speed are optimized by
neural network fitting and genetic algorithm seeking, which improves the overall
performance of the whole vehicle. Finally, the multi-objective optimal control
strategy based on neural network-genetic algorithm proposed in this paper is
simulated and verified. The simulation results show that the control strategy
proposed in this paper has significantly improved the overall vehicle
performance of the plug-in P13 hybrid vehicle.
Authors
- Chaobo Huo - 1. Tianjin University, Institute of Internal Combustion
- Bingfeng Zu - Tianjin University, Institute of Internal Combustion Engines
- Yuliang Xu - Tianjin University, Institute of Internal Combustion Engines
- Jianwei Zhou - Tianjin University, Institute of Internal Combustion Engines
- Yezhen Wu - 1. Tianjin University, Institute of Internal Combustion
Topic
Citation
Huo, C., Zu, B., Xu, Y., Zhou, J. et al., "Research on PHEV Multi-Objective Energy Management Strategy Based on Genetic Algorithm Optimized Feed-Forward-Neural Network," SAE Technical Paper 2022-01-7048, 2022, https://doi.org/10.4271/2022-01-7048.Also In
References
- Zhang , J. , Richter , J. , and Kaczmarek , C. Catalysts for Post Euro 6 Plug-In Hybrid Electric Vehicles SAE Int. J. Adv. & Curr. Prac. in Mobility 2 6 2020 3151 3163
- Bingzheng , F. Research on Emission Characteristics of Light-Duty Vehicles under Actual Driving Test Conditions Beijing Institute of Technology 2016
- Huang , Y. , Surawski , N.C. , Organ , B. et al. Fuel Consumption and Emission Performance under Real Driving: Comparison between Hybrid and Conventional Vehicles Science of the Total Environment 659 APR.1 2018 275 282
- Wenlin , Y. , Yunshan , G. , Xin , W. et al. Research on the Emission Characteristics of Hybrid Electric Vehicles on Actual Roads Automotive Engineering 40 10 2018 1139 1145
- Xiaowei , W. , Xiaojun , J. , Kongjian , Q. et al. The Actual Driving Pollutant Emission Characteristics of Light-Duty Plug-In Hybrid Electric Vehicles Journal of Automotive Safety and Energy 9 02 2018 209 214
- Yuping , Z. , Datong , T. , Ling , S. et al. Multi-objective Parameter Optimization of Cost, Fuel Consumption and Emissions of Plug-In Hybrid Electric Vehicle Power System Automotive Engineering 38 4 2016 397 402
- Hanbing , K. and Ning , Z. Research on PHEV Rule Control Strategy Based on Two-State Dynamic Programming Algorithm Mechanical Transmission 42 02 2018 6 13+35
- Zhiliang , L. Research on Energy Management of Plug-in Hybrid Electric Vehicles Zhejiang University 2014
- Suarez-Bertoa , R. and Astorga , C. Unregulated Emissions from Light-Duty Hybrid Electric Vehicles Atmospheric Environment 136 2016 134 143
- Bagheri , S. , Huang , Y. , Walker , P.D. , Zhou , J.L. et al. Strategies for Improving the Emission Performance of Hybrid Electric Vehicles Science of the Total Environment 771 2021 144901
- Wang , J. and Jiao , Z. Energy Management Strategy of Four-Wheel Drive Hybrid Electric Vehicle Based on ECMS Algorithm DEStech Transactions on Environment Energy and Earth 2019
- Zhang , L.P. , Liu , W. , and Qi , B.N. Energy Optimization of Multi-mode Coupling Drive Plug-In Hybrid Electric Vehicles Based on Speed Prediction Energy 206 2020 118126