In this paper, an improved Hopfield Lagrange network (IHLN) is proposed and applied to solve motor efficiency optimization in an electric vehicle (EV) with a permanent magnet synchronous motor (PMSM). In contrast to the Hopfield Lagrange network (HLN) incorporating deterministic gradient descent, a stochastic searching method, simulated annealing (SA), is introduced in IHLN to prevent the network from falling into local minima and improve the optimality. Some important issues regarding the performance of the IHLN are thoroughly investigated such as whether the optimization problem is nonconvex with local minima, the stability of the network's equilibria, and the convergence condition of the SA. Simulations are conducted to validate the effectiveness of IHLN, and results demonstrate the improvement brought by IHLN concerning different indices.

中文翻译：

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改进的 Hopfield Lagrange 网络在电机效率优化中的应用

在本文中，提出了一种改进的 Hopfield Lagrange 网络 (IHLN)，并将其应用于解决具有永磁同步电机 (PMSM) 的电动汽车 (EV) 中的电机效率优化问题。与包含确定性梯度下降的Hopfield Lagrange网络（HLN）相比，IHLN中引入了一种随机搜索方法，模拟退火（SA），以防止网络陷入局部最小值并提高最优性。深入研究了有关 IHLN 性能的一些重要问题，例如优化问题是否具有局部最小值的非凸问题、网络平衡的稳定性以及 SA 的收敛条件。进行了模拟以验证 IHLN 的有效性，结果表明 IHLN 对不同指标带来的改进。