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Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index

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  • Chaoying Xia

    (School of Electrical Engineering and Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Cong Zhang

    (School of Electrical Engineering and Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

Abstract

An energy management strategy (EMS) considering both optimality and real-time performance has become a challenge for the development of hybrid electric vehicles (HEVs) in recent years. Previous EMSes based on the optimal control theory minimize the fuel consumption, but cannot be directly implemented in real-time because of the requirement for a prior knowledge of the entire driving cycle. This paper presents an innovative design concept and method to obtain a power management strategy for HEVs, which is independent of future driving conditions. A quadratic performance index is designed to ensure the vehicle drivability, maintain the battery energy sustainability and average and smooth the engine power and motor power to indirectly reduce fuel consumption. To further improve the fuel economy, two rules are adopted to avoid the inefficient engine operation by switching control modes between the electric and hybrid modes according to the required driving power. The derived power of the engine and motor are related to current vehicle velocity and battery residual energy, as well as their desired values. The simulation results over different driving cycles in Advanced Vehicle Simulator (ADVISOR) show that the proposed strategy can significantly improve the fuel economy, which is very close to the optimal strategy based on Pontryagin’s minimum principle.

Suggested Citation

  • Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:11:p:12325-12473:d:58246
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    References listed on IDEAS

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    1. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    2. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    3. Pérez, Laura V. & Bossio, Guillermo R. & Moitre, Diego & García, Guillermo O., 2006. "Optimization of power management in an hybrid electric vehicle using dynamic programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 244-254.
    4. Cipek, Mihael & Pavković, Danijel & Petrić, Joško, 2013. "A control-oriented simulation model of a power-split hybrid electric vehicle," Applied Energy, Elsevier, vol. 101(C), pages 121-133.
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    Cited by:

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    2. Chaoying Xia & Zhiming DU & Cong Zhang, 2017. "A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-23, July.
    3. Ali Solouk & Mahdi Shahbakhti, 2016. "Energy Optimization and Fuel Economy Investigation of a Series Hybrid Electric Vehicle Integrated with Diesel/RCCI Engines," Energies, MDPI, vol. 9(12), pages 1-23, December.
    4. Benmouna, Amel & Becherif, Mohamed & Depernet, Daniel & Ebrahim, Mohamed A., 2018. "Novel Energy Management Technique for Hybrid Electric Vehicle via Interconnection and Damping Assignment Passivity Based Control," Renewable Energy, Elsevier, vol. 119(C), pages 116-128.
    5. João Faria & José Pombo & Maria do Rosário Calado & Sílvio Mariano, 2019. "Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System," Energies, MDPI, vol. 12(5), pages 1-24, March.

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