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The role and application of convex modeling and optimization in electrified vehicles

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  • Li, Yapeng
  • Tang, Xiaolin
  • Lin, Xianke
  • Grzesiak, Lech
  • Hu, Xiaosong

Abstract

Component sizing and energy management are essential for minimizing vehicle costs and maximizing energy efficiency in electrified vehicles. Usually, a hierarchical optimization framework is used to solve the component sizing problem. The possible component sizes are enumerated in the outer loop, and their effectiveness is assessed in the inner loop. When optimizing energy consumption, highly robust and effective energy management strategies are important for both individual vehicles and vehicle platoons. Convex optimization has become an effective and important method to solve multi-dimensional problems due to its computational efficiency. In this paper, we reviewed the component sizing and energy management issues in electrified vehicles and summarized a variety of convex optimization methods for solving these problems. The prospects and future trends of the convex optimization method in the design and control of electrified vehicles were presented and discussed.

Suggested Citation

  • Li, Yapeng & Tang, Xiaolin & Lin, Xianke & Grzesiak, Lech & Hu, Xiaosong, 2022. "The role and application of convex modeling and optimization in electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:rensus:v:153:y:2022:i:c:s1364032121010650
    DOI: 10.1016/j.rser.2021.111796
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    References listed on IDEAS

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    Cited by:

    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    2. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    3. Hou, Zhuoran & Guo, Jianhua & Chu, Liang & Hu, Jincheng & Chen, Zheng & Zhang, Yuanjian, 2023. "Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy," Energy, Elsevier, vol. 282(C).
    4. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Zhou, Xingyu & Sun, Chao & Sun, Fengchun & Zhang, Chuntao, 2023. "Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency," Applied Energy, Elsevier, vol. 344(C).
    6. Li, Yapeng & Wang, Feng & Tang, Xiaolin & Hu, Xiaosong & Lin, Xianke, 2022. "Convex optimization-based predictive and bi-level energy management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 257(C).

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