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Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

Author

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  • Yu-Huei Cheng

    (Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan)

  • Ching-Ming Lai

    (Department of Vehicle Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Road, Taipei 106, Taiwan)

Abstract

Hybrid electric vehicle (HEV) control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA)” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NOx) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC), Federal Test Procedure (FTP), Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC), and urban dynamometer driving schedule (UDDS) are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

Suggested Citation

  • Yu-Huei Cheng & Ching-Ming Lai, 2017. "Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm," Energies, MDPI, vol. 10(3), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:305-:d:92069
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    References listed on IDEAS

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    4. Jingxian Hao & Zhuoping Yu & Zhiguo Zhao & Peihong Shen & Xiaowen Zhan, 2016. "Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm," Energies, MDPI, vol. 9(12), pages 1-24, November.
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    3. Ming Ye & Yitao Long & Yi Sui & Yonggang Liu & Qiao Li, 2019. "Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment," Energies, MDPI, vol. 12(19), pages 1-21, September.
    4. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    5. Haeseong Jeoung & Kiwook Lee & Namwook Kim, 2019. "Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles," Energies, MDPI, vol. 12(10), pages 1-17, May.
    6. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
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    8. Ching-Ming Lai & Jiashen Teh & Yuan-Chih Lin & Yitao Liu, 2020. "Study of a Bidirectional Power Converter Integrated with Battery/Ultracapacitor Dual-Energy Storage," Energies, MDPI, vol. 13(5), pages 1-23, March.
    9. Stefano Rinaldi & Marco Pasetti & Emiliano Sisinni & Federico Bonafini & Paolo Ferrari & Mattia Rizzi & Alessandra Flammini, 2018. "On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-27, May.
    10. Álvarez Fernández, Roberto & Corbera Caraballo, Sergio & Beltrán Cilleruelo, Fernando & Lozano, J. Antonio, 2018. "Fuel optimization strategy for hydrogen fuel cell range extender vehicles applying genetic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 655-668.
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