Modeling and control of a hybrid electric vehicle to optimize system performance for fuel efficiency

The improvement of fuel economy and the environmental consequences are important societal goals. In this paper, the modeling and regulation of a hybrid electric vehicle is explored to optimize system performance for fuel-efficiency. The internal combustion engine is a part of the mechanical design, while controller design and electrical subsystem are a part of the electrical design. To improve fuel efficiency in a hybrid electric vehicle is to combine an electric motor, a battery, and an internal combustion engine. The electric motor assists the engine when accelerating, driving longer highways, or climbing hills. This enables the use of a smaller, more efficient engine. It also makes use of the concept of regenerative braking to maximize energy efficiency. Thus, the NEDC and UDDS driving cycles may not accurately represent the actual situation and are progressively being replaced to HWFET driving cycle. In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was used to analyze the engine, motor performance, and the HWFET was used in the vehicle driving test using Matlab/ Simulink. The controller will be based on both the desired driving speed and the battery charge level. The implementation of optimal control based on an adaptive neuro-fuzzy inference system that decreases internal combustion engine fuel consumption is the paper's main contribution.

Automated summary

This paper explores the modeling and control of a hybrid electric vehicle to optimize fuel efficiency, focusing on the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a controller. By combining the operation of an electric motor and an internal combustion engine, as well as utilizing regenerative braking, fuel efficiency can be improved. Additionally, replacing traditional driving cycles with the HWFET driving cycle provides a more accurate representation of real-world conditions.