Abstract
The ability to combine positive features of an internal combustion engine with those of an electric motor has been fundamental to the advancement of the high-performance energy-optimized hybrid vehicles. However, due to a lack of reliable and realistic hybrid vehicle models, much of the hybrid vehicle controller research has been limited to computer simulations. To overcome this shortcoming, this paper utilizes a highly reliable vehicle model (Autonomie) for simulation. A state-of-the-art fuzzy logic controller was developed that considers the battery state of charge, wheel torque demand and vehicle speed as the input variables. An ARM Cortex M3 microcontroller-based control hardware prototype was developed and the processor in loop simulation was performed to verify the feasibility of the developed controller in an embedded real-time application. The results of this study indicate that the developed fuzzy logic controller significantly improved the performance (up to 48%) of the hybrid vehicle in a real-time application compared to Autonomie's built-in controller. The processor in loop test results provide evidence of the effectiveness of the developed control algorithm in the embedded real-time form
Highlights
An ever-increasing demand for energy combined with a limited supply of sources has led to an increased awareness of the efficient uses of energy
For the parallel hybrid vehicle, the distribution of demand power can be related to the driver's demand torque, State Of Charge (SOC) of the battery, vehicle speed, etc
The fuzzy logic controller has a total of 75 rules with three input variables: State of charge of the battery, vehicle speed, wheel torque demand and two output variables, namely engine output torque and motor output torque
Summary
An ever-increasing demand for energy combined with a limited supply of sources has led to an increased awareness of the efficient uses of energy. A use of dynamic programming is difficult for real-time applications because it needs a priori information about the environment; further, the method requires significant computational times For this reason, the authors developed an artificial neural network and utilized dynamic programming based optimized solutions for its training. Developed a fuzzy logic controller for parallel hybrid vehicles considering the driver power demand, SOC of the battery and electric motor speed as the inputs for the fuzzy logic controller. They used the Powertrain System Analysis Toolkit (PSAT) for simulation and performance evaluation of the developed controller. An Arm Cortex M3 microcontroller-based controller prototype was built for the considered application
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