Optimizing battery discharge management of PMSM vehicles using adaptive nonlinear predictive control and a Generalized Integrator

Abstract

Electric vehicles (EVs) are key to a sustainable future, but extending battery life is essential to reduce costs and environmental impact. Thus, this paper presents the development of an Adaptive Nonlinear Predictive Model (ANLPM), integrated with a Third Order Generalized Integrator (TOGI) flux observer, which enhances induced torque estimation and stator reactance in Permanent Magnet Synchronous Motor (PMSM) systems. The model employs a Sequential Quadratic Programming (SQP) algorithm, ensuring numerical stability and efficiency within the Model Predictive Control (MPC) framework to handle nonlinear constraints effectively. Moreover, simulation results demonstrate that the ANLPM significantly outperforms classical Adaptive Linear Predictive Models (ALPM), Seven-Dimensional LPM (SDLPM), and Proportional-Integral (PI) control strategies. It achieves marked reductions in battery discharge current and energy consumption rates. Therefore, simulation comparisons, across different scenarios, show that ANLPM reduces battery discharge current by 3% over ALPM and 44.7% over PI, while cutting energy consumption by 12.2% and 28.2%, and decreasing parallel battery cells by 14.2% and 28%, respectively. Under high temperatures, ANLPM cuts battery consumption by 45.3% and reduces cells by 43.7% compared to SDLPM, highlighting its efficiency in managing energy and extending battery life in EVs.