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Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles

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  • Girade, Piyush
  • Shah, Harsh
  • Kaushik, Karan
  • Patheria, Akil
  • Xu, Bin

Abstract

In this era of vehicle electrification from mild hybrids to fully electric cars, the importance of fuel economy improvements has led to technological advancements in energy management strategies. The control algorithm is pivotal to the increase in the energy efficiency of a plug-in hybrid system. The existing energy management strategies lack the adaptiveness and utilization of advancements of vehicle-to-vehicle technology. This paper proposes a Cost Optimization for Finite Horizon strategy and also an adaptive version of Equivalent Consumption Minimization Strategy (A-ECMS). The Adaptive-ECMS adds a battery State of Charge (SOC) based reference to ensure the most efficient blended operation for a charge-discharge cycle. The Cost Optimization for Finite Horizon strategy utilizes future driving condition information from vehicle-to-vehicle technology to assist fuel consumption. A forward-looking vehicle propulsion system simulator is developed in Simulink®. A battery model is developed with parameters from the Nickel Cobalt Aluminum chemistry cell. To understand the extent of improvement, the proposed strategies are then compared with the prevalent Finite State Machine strategy (FSM) in three representative driving cycles. The results show an average fuel economy improvement of 5% when compared to the baseline strategy. Among the three strategies, Cost Optimization for Finite Horizon strategy is best suitable for urban driving conditions and Adaptive-ECMS is best suitable for highway driving conditions. For a conventional series-hybrid vehicle, implementing the proposed energy management strategies can help save approximately 8.5 gallons of fuel per year.

Suggested Citation

  • Girade, Piyush & Shah, Harsh & Kaushik, Karan & Patheria, Akil & Xu, Bin, 2021. "Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s036054422101104x
    DOI: 10.1016/j.energy.2021.120856
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    References listed on IDEAS

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    1. Fengqi Zhang & Haiou Liu & Yuhui Hu & Junqiang Xi, 2016. "A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI," Energies, MDPI, vol. 9(11), pages 1-26, November.
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    4. Xiao-Guang Yang & Teng Liu & Chao-Yang Wang, 2021. "Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles," Nature Energy, Nature, vol. 6(2), pages 176-185, February.
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    Citations

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    Cited by:

    1. Yonghong Xu & Cheng Li & Xu Wang & Hongguang Zhang & Fubin Yang & Lili Ma & Yan Wang, 2022. "Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation," Sustainability, MDPI, vol. 14(23), pages 1-25, November.
    2. Xiaodong Liu & Hongqiang Guo & Xingqun Cheng & Juan Du & Jian Ma, 2022. "A Robust Design of the Model-Free-Adaptive-Control-Based Energy Management for Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 15(20), pages 1-24, October.
    3. Lin, Xinyou & Xu, Xinhao & Wang, Zhaorui, 2022. "Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle," Applied Energy, Elsevier, vol. 321(C).
    4. Wu, Wei & Luo, Junlin & Zou, Tiangang & Liu, Yin & Yuan, Shihua & Xiao, Bingqing, 2022. "Systematic design and power management of a novel parallel hybrid electric powertrain for heavy-duty vehicles," Energy, Elsevier, vol. 253(C).

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