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Onori, S., Serrao, L. and Rizzoni, G. (2016) Hybrid Electric Vehicles: Energy Management Strategies. In: Gan, W.-S., Jay Kuo, C.-C., Zheng, T.F. and Barni, M., Eds., SpringerBriefs in Electrical and Computer Engineering, Springer, London, 121.
https://doi.org/10.1007/978-1-4471-6781-5

has been cited by the following article:

• TITLE: Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of a Parallel Hybrid Electric Vehicle

  AUTHORS: Jared A. Diethorn, Andrew C. Nix, Mario G. Perhinschi, W. Scott Wayne

  KEYWORDS: Hybrid Electric Vehicle, Fuzzy Logic, Adaptive Control, Charge Sustainability

  JOURNAL NAME: Journal of Transportation Technologies, Vol.14 No.1, January 30, 2024

  ABSTRACT: As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.