Multi-objective optimization approach for permanent magnet machine via improved soft actor–critic based on deep reinforcement learning

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As awareness of environmental protection grows, the development of electric vehicles has emerged as a prominent area of research. Naturally, optimizing Permanent Magnet (PM) machines is critical for enhancing the power performance of electric vehicles, as they are essential components of these vehicles. Benefitting from the advantage of Deep Neural Network (DNN), optimizing the PM machine by leveraging DNN has drawn great attention from industry and academia. To increase the torque of the PM machine and reduce torque ripple from a structural design perspective, this paper proposes a Multi-objective Optimization approach for PM machine via improved Soft Actor Critic (called MOPM-SAC) based on Deep Reinforcement Learning (DRL). MOPM-SAC consists of two core components: a surrogate model based on DNN and a SAC algorithm. Specifically, the SAC algorithm based on DNN is used to optimize the PM machine. The surrogate model is established by training the DNN to use Finite Element (FE) simulation samples, which possesses higher accuracy than other methods, such as Support Vector Machine (SVM), Random Forest (RF), and so on. Moreover, the SAC algorithm integrates a trained DNN as the state transition function within the DRL environment, which enhances the optimization algorithm’s generalization ability in the context of PM machines. In addition, the reward function is designed based on optimization requirements to guide the agent in the SAC algorithm toward learning the optimal strategy. Finally, a 75 kW prototype is manufactured and tested. The effectiveness of the proposed method is validated through FE simulations and prototype experiments.