An Adaptive Strategy for PMSM-Based Disturbance Estimation and Online Parameter Identification
Sai Zhang, Anwen Shen, Xin Luo, Qipeng Tang, Zicheng Li
Abstract
Electric vehicles (EVs) mainly use permanent magnet synchronous motors (PMSMs) as the core power units. Moreover, when EVs navigate complex roads, the requirements become even more stringent. This paper addresses the real-time parameter estimation and state evaluation of PMSMs in EVs, considering the inevitable external interference. By leveraging the stability condition of the energy function, a simple adaptive scheme is proposed to enhance the robustness of PMSMs against current disturbances. At the same time, the online identification scheme for motor parameters is considered, which extends the practical application method of Lyapunov energy function stability. Subsequently, in order to optimize the current disturbance caused by parameter mismatch, the known model will be adjusted to the unknown model and the adaptive fuzzy scheme is introduced to discuss with the relevant parameters designed from the stability condition of the adaptive fuzzy Lyapunov energy function. Finally, the effectiveness of the proposed scheme in this paper is proved by various experiments.