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.