Optimization of hybrid electric vehicle control for efficient performance at critical energy levels

Alsibai, Mohammed Hayyan; Sayong, Maxwell; Koay, Li Woon; Atnaw, Samson Mekbib; Hamran, Nurul Nadia Nor · 2016 · Crossref

DOI: 10.1109/iccsce.2016.7893572

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Summary

This paper addresses the challenge of optimizing energy consumption and powertrain control in hybrid electric vehicles (HEVs), specifically focusing on maintaining efficient performance during critical energy levels. The primary motivation is to improve the efficiency of electric energy usage and minimize the reliance on the internal combustion engine (ICE). The study aims to extend the travel distance of the vehicle by managing limited energy resources effectively, particularly when battery levels are low. The research was conducted using a prototype vehicle created by retrofitting an electric motor onto an all-terrain vehicle (ATV). The control system was designed and implemented using an embedded micro-controller unit (MCU). This MCU serves as the central component for regulating the automatic switching between the electric motor and the ICE. A key feature of the proposed algorithm is its ability to provide guidance to the driver for efficient resource usage during critical energy states. To enforce this efficiency, the system restricts driver performance through pulse width modulation (PWM). By adjusting the PWM duty cycle, the control system ensures optimum performance aimed at maximizing the vehicle's travel distance. The experimental design involved analyzing various driving scenarios, taking into account specific factors such as travel speed, current energy levels, and the frequency of stop-and-go driving conditions. The experiments demonstrated significant improvements in the vehicle's performance metrics. The proposed optimization algorithm successfully regulated energy consumption and powertrain control, achieving the study's main targets of improved electric energy efficiency and reduced ICE usage. The system effectively managed the transition between power sources and constrained driver input via PWM adjustments to preserve energy reserves. The analysis of different driving scenarios confirmed that the control strategy could adapt to variables like speed and driving patterns to maintain optimal efficiency. The significance of this work lies in its practical application of embedded systems for optimal control in hybrid vehicles. By demonstrating that an MCU-based algorithm can effectively manage limited resources and guide driver behavior through technical restrictions, the study provides a viable approach for extending the range of HEVs. The findings suggest that integrating such control mechanisms can enhance the overall efficiency of hybrid powertrains, particularly in scenarios where energy conservation is critical. This contributes to the broader field of hybrid vehicle technology by offering a method for efficient resource management and performance optimization under constrained energy conditions.

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