Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms
A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which i...
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| Format: | Article |
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SAGE Publications
2020
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| author | Tey, Jing Yuen Ramli, Rahizar |
| author_facet | Tey, Jing Yuen Ramli, Rahizar |
| author_sort | Tey, Jing Yuen |
| collection | UM |
| description | A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%. © IMechE 2019. |
| first_indexed | 2024-03-06T06:03:20Z |
| format | Article |
| id | um.eprints-24636 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:03:20Z |
| publishDate | 2020 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | um.eprints-246362020-06-04T01:41:22Z http://eprints.um.edu.my/24636/ Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms Tey, Jing Yuen Ramli, Rahizar TJ Mechanical engineering and machinery A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%. © IMechE 2019. SAGE Publications 2020 Article PeerReviewed Tey, Jing Yuen and Ramli, Rahizar (2020) Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234 (5). pp. 1472-1479. ISSN 0954-4070, DOI https://doi.org/10.1177/0954407019859808 <https://doi.org/10.1177/0954407019859808>. https://doi.org/10.1177/0954407019859808 doi:10.1177/0954407019859808 |
| spellingShingle | TJ Mechanical engineering and machinery Tey, Jing Yuen Ramli, Rahizar Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title | Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title_full | Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title_fullStr | Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title_full_unstemmed | Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title_short | Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| title_sort | multi objective optimization of all wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms |
| topic | TJ Mechanical engineering and machinery |
| work_keys_str_mv | AT teyjingyuen multiobjectiveoptimizationofallwheeldriveelectricformulavehicleforperformanceandenergyefficiencyusingevolutionaryalgorithms AT ramlirahizar multiobjectiveoptimizationofallwheeldriveelectricformulavehicleforperformanceandenergyefficiencyusingevolutionaryalgorithms |