This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.


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    Title :

    Design of a Rule-Based Controller and Parameter Optimization Using a Genetic Algorithm for a Dual-Motor Heavy-Duty Battery Electric Vehicle


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    WCX SAE World Congress Experience ; 2022



    Publication date :

    2022-03-29




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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