Adaptive Dynamic Surface Control With Disturbance Observers for Battery/Supercapacitor-Based Hybrid Energy Sources in Electric Vehicles
Xizheng Zhang, Yaonan Wang, Xiaofang Yuan, Yongpeng Shen, Zhangyu Lu
Abstract
The underlying control in the hybrid energy source system (HESS) of an electric vehicle (EV) plays a pivotal role. Uncertainty is unavoidable in system modeling owing to variations in electrical parameters and unknown external disturbances, which inevitably deteriorate the control performance of the HESS. In this study, an innovative adaptive dynamic surface control with disturbance observers (ADSCDOBs) is adopted as a control scheme for underlying tracking in the HESS to counteract the adverse effects and improve control accuracy. First, disturbance observers (DOBs) are designed by employing a nonlinear DOB (NDO) and an extreme learning machine (ELM) approximator to estimate the mismatched and matched uncertainties. Subsequently, the proposed ADSCDOB scheme integrates the adaptive dynamic surface technique and second-order differentiators to achieve robust control, in which voltage/current references are obtained through the rule-based energy management strategy (EMS). The established ADSCDOB obviates the “differential explosion” problem and ensures that the closed-loop system is semi-globally and uniformly bounded. Comprehensive simulations and prototype experiments prove the effectiveness of the ADSCDOB, confirming its satisfactory performance in terms of a fast response, reduced error, and robust stability under hybrid driving conditions.