Multi-objective optimum energy management strategies for parallel hybrid electric vehicles: A comparative study

In this study, multi-objective optimum energy management strategies are developed for pre-and post-transmission parallel hybrid electric vehicles. The first strategy aims to improve fuel economy and electric system efficiency, while the second strategy adds the improvement in battery performance and life. Multi-objective genetic algorithm is adopted to solve the formulated optimum energy management problems, hence generates optimum control inputs for each drivetrain configuration. Mathematical models of vehicle dynamics and drivetrain components are integrated with the developed strategies in a simulation environment. Results for both strategies are compared to a baseline rule-based energy management strategy, commonly used as a benchmark in literature. Additionally, both drivetrain configurations are compared and analyzed through different standard driving cycles. The results show the significant improvement in battery performance due to its consideration in the energy management strategy, with either null or positive effect on fuel consumption. Additionally, the results of pre-transmission drivetrain configuration show noticeable improvement in battery performance as compared to the post-transmission results.

Automated summary

This study develops multi-objective optimum energy management strategies for pre-and post-transmission parallel hybrid electric vehicles. The strategies aim to improve fuel economy, electric system efficiency, battery performance, and life. Multi-objective genetic algorithm is used to solve the energy management problems and generate optimum control inputs. Vehicle dynamics and drivetrain models are integrated with the strategies in a simulation environment. Results show significant improvement in battery performance and varied effects on fuel consumption. The pre-transmission drivetrain configuration shows better battery performance than the post-transmission configuration.