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<p>Dual-motor pure electric heavy-duty trucks face a number of challenges, such as the dual-motor power allocation for optimal operating efficiency under complicated variable transport conditions. To address this problem, a novel hybrid control strategy for intelligent torque distribution combining genetic algorithm (GA) and genetic algorithm combined with particle swarm optimization (GA-PSO) methods is proposed. This strategy aims to improve motor efficiency and reduce energy consumption. With the aim of studying the energy consumption and power distribution, researchers established a vehicle energy flow simulation model for a dual-motor 2AMT (two-speed automatic transmission) heavy-duty truck. The accuracy of the model was verified through dynamometer test bench experiments. The article compares and analyzes the distribution of vehicle energy flow, motor drive efficiency, and lost power of each component under the same torque strategy and intelligent torque distribution strategy. The simulation results show that under CHTC-TT cycle conditions, the GA-based intelligent torque distribution strategy reduces the 100&#xa0;km power consumption by 0.88% and motor losses by 5.1% compared to the strategy with the same torque. The GA-PSO-based intelligent torque distribution reduces the 100&#xa0;km power consumption by 1.21% and motor losses by 8.2% compared to the same torque strategy.</p>

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Optimization on motor control strategy for a pure electric heavy truck with dual motor

  • 期刊论文

  • 发表日期：

    2025年8月28日

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  • Liange He,
  • Haijun Mo,
  • Limin Wu,
  • Yan Zhang,
  • Jinwang Tang,
  • Hang Wu

摘要

Dual-motor pure electric heavy-duty trucks face a number of challenges, such as the dual-motor power allocation for optimal operating efficiency under complicated variable transport conditions. To address this problem, a novel hybrid control strategy for intelligent torque distribution combining genetic algorithm (GA) and genetic algorithm combined with particle swarm optimization (GA-PSO) methods is proposed. This strategy aims to improve motor efficiency and reduce energy consumption. With the aim of studying the energy consumption and power distribution, researchers established a vehicle energy flow simulation model for a dual-motor 2AMT (two-speed automatic transmission) heavy-duty truck. The accuracy of the model was verified through dynamometer test bench experiments. The article compares and analyzes the distribution of vehicle energy flow, motor drive efficiency, and lost power of each component under the same torque strategy and intelligent torque distribution strategy. The simulation results show that under CHTC-TT cycle conditions, the GA-based intelligent torque distribution strategy reduces the 100 km power consumption by 0.88% and motor losses by 5.1% compared to the strategy with the same torque. The GA-PSO-based intelligent torque distribution reduces the 100 km power consumption by 1.21% and motor losses by 8.2% compared to the same torque strategy.