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Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life

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  • Sarvaiya, Shradhdha
  • Ganesh, Sachin
  • Xu, Bin

Abstract

Hybrid Electric Vehicles (HEV) form an important category of the automotive segment and most importantly fill the transition between internal combustion engines powered conventional vehicles and electric motor-powered vehicles. One of the main propagandas and sales factor of HEV is improved fuel economy over conventional vehicles. Over the recent years, considering the longevity of HEV usage, battery life evaluation has been brought to the forefront of research along with fuel economy and the key method to slow down the battery aging is through Energy Management Strategy (EMS). This research paper presents the comparative analysis of battery life optimization with different control strategies in a parallel hybrid vehicle. In the available research work, EMS considering battery aging is still lacking. This research work considers the impact of multiple parameters, including temperature and current on battery aging, providing more accurate battery life prediction. Four different control strategies are analyzed including, Thermostat, Fuzzy logic, Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) and Q-learning considering battery aging. Results are compared concerning battery aging and fuel economy. In this research, ECMS results show a 25% improved fuel economy compared to the rule-based strategy. Also, a market cost-analysis is depicted to show the monetary savings for each of the energy management strategy.

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  • Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:energy:v:228:y:2021:i:c:s0360544221008537
    DOI: 10.1016/j.energy.2021.120604
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    References listed on IDEAS

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    5. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    6. Zewen Meng & Tiezhu Zhang & Hongxin Zhang & Qinghai Zhao & Jian Yang, 2021. "Energy Management Strategy for an Electromechanical-Hydraulic Coupled Power Electric Vehicle Considering the Optimal Speed Threshold," Energies, MDPI, vol. 14(17), pages 1-12, August.
    7. Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    8. Song, Jingeun & Cha, Junepyo, 2022. "Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle," Energy, Elsevier, vol. 244(PB).
    9. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    10. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    11. Zhang, Cetengfei & Zhou, Quan & Hua, Min & Xu, Hongming & Bassett, Mike & Zhang, Fanggang, 2023. "Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives," Applied Energy, Elsevier, vol. 351(C).
    12. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).

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