Thermal performance evaluation of new energy vehicle pre-charge resistors based on phase change material cooling

This paper proposes a compensation strategy for thermal drift in motor resistance to enhance the control precision of induction motors. The compensation strategy primarily utilizes a sparse search algorithm (SSA) to adjust the initial parameters of a backpropagation (BP) neural network, establishing a refined thermal drift prediction and compensation model (SSA-BP). An 18 kW asynchronous motor is used as the test sample, with stator temperature and phase resistance data collected for model training and testing. The resistance prediction performance of the SSA-BP model is compared with that of BP and particle swarm optimization-BP models and the algorithm models are evaluated using metrics such as R², root mean square error, mean bias error, mean square error, and mean absolute percentage error. The comparison results demonstrate that the SSA-BP algorithm model exhibits higher prediction accuracy and generalization capability in the prediction of motor resistance thermal drift. Finally, by employing the SSA-BP thermal drift prediction compensation strategy in motor control, the tracking performance of d-axis and q-axis currents and speed feedback is superior to that before compensation, thereby optimizing the motor control performance.

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Thermal performance evaluation of new energy vehicle pre-charge resistors based on phase change material cooling

Cited by 3 publications

References 38 publications

Experimental assessment on the thermal control performance of composite phase change material for fast charging power module

Experimental assessment on the thermal control performance of composite phase change material for fast charging power module

Short and long term memory method for predicting the temperature of motor stator based on Harris eagle algorithm optimization

Asynchronous motor resistance thermal drift prediction and compensation strategy based on sparse search optimization of backpropagation (BP) neural network algorithm

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