Energy Management Design of Dual-Motor System for Electric Vehicles Using Whale Optimization Algorithm
Chien-Hsun Wu, Chieh-Lin Tsai, Jie-Ming Yang
TL;DR
This paper explores how using the whale optimization algorithm can improve energy efficiency in dual-motor electric vehicles compared to other control strategies.
Contribution
The study introduces the whale optimization algorithm as a novel energy management strategy for dual-motor electric vehicles.
Findings
The whale optimization algorithm improved energy efficiency by 8.9% in simulations and 3.8% in hardware-in-the-loop tests.
Regenerative braking combined with global grid search improved energy efficiency by 29.4% in hardware-in-the-loop tests.
The whale optimization algorithm is identified as a promising alternative energy management strategy for dual-motor systems.
Abstract
Dual-motor electric vehicles enhance power performance and overall output capabilities by enabling the real-time control of the torque distribution between the front and rear wheels, thereby improving handling, stability, and safety. In addition to increased energy efficiency, a dual-motor system provides redundancy: if one motor fails, the other can still supply partial power, further enhancing driving safety. This study aimed to optimize the energy management strategies of the front- and rear-axis motors, examining the application effects of rule-based control (RBC), global grid search (GGS), and the whale optimization algorithm (WOA). A simulation platform based on MATLAB/Simulink® (R2021b, MATLAB, Natick, MA, USA) was constructed and validated through hardware-in-the-loop (HIL) testing to ensure the authenticity and reliability of the simulation results. Detailed tests and analyses…
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Taxonomy
TopicsElectric and Hybrid Vehicle Technologies · Electric Vehicles and Infrastructure · Vehicle emissions and performance