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Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle

Author

Listed:
  • Omkar Parkar

    (Cummins, Inc., CTC, Columbus, IN 47201, USA)

  • Benjamin Snyder

    (Cummins, Inc., CTC, Columbus, IN 47201, USA)

  • Adibuzzaman Rahi

    (Department of Mechanical and Energy Engineering, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

  • Sohel Anwar

    (Department of Mechanical and Energy Engineering, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA)

Abstract

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.

Suggested Citation

  • Omkar Parkar & Benjamin Snyder & Adibuzzaman Rahi & Sohel Anwar, 2023. "Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle," Energies, MDPI, vol. 16(13), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5082-:d:1184163
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    References listed on IDEAS

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    1. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    2. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    3. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    4. Chen, Syuan-Yi & Hung, Yi-Hsuan & Wu, Chien-Hsun & Huang, Siang-Ting, 2015. "Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization," Applied Energy, Elsevier, vol. 160(C), pages 132-145.
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    1. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.

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