Design and analysis of model reference adaptive control on the energy management system of an electric vehicle

Abstract

Electric vehicles (EVs) have become a favourable choice due to the current environmental conditions and limited fuel resources. For efficient operation, EVs often use a lithium-ion battery as its main power source. Nevertheless, during acceleration, EVs require an instant high load demand, which is quite challenging to satisfy with the lithium-ion battery alone due to its slow discharging rate. This frequent fluctuation can damage the batteries’ State of Health (SoH), and to overcome this issue, a Hybrid Energy Storage System (HESS) is proposed. In the system, a Supercapacitor (SC) is used to support the immediate load demand from a vehicle. To ensure that the correct amount of power is extracted, a suitable controller needs to be integrated with a Bidirectional DC-DC Converter (BDC). As a model disturbance can influence both the load demand and system feedback response, a novel contribution of this work is to introduce the application of Model Reference Adaptive Control (MRAC) to overcome this issue. A detailed derivation of this algorithm, along with the investigation of the tuning effect, is presented. To analyse the efficacy of this controller, several numerical simulations have been carried out using MATLAB/Simulink, where the MRAC performance is benchmarked against the Proportional Integral (PI) controller, based on several performance indexes such as Root Mean Square Error (RMSE) of current and voltage, power demand tracking, and controllers’ characteristics. For regular operation, the results show that MRAC outperforms the PI controller in tracking voltage demand by 67% (with constant voltage) and 85% (with variable voltage) with inverting BDC and current demands by 16% (with variable current) in non-inverting BDC. While in the presence of disturbance, MRAC shows its efficacy in current demand tracking by surpassing PI controller with 15% higher accuracy. In this case, MRAC requires some time due to adjust its mechanism to surpass the PI controller in tracking the load demand. To validate the MRAC design, an EV model, designed by MathWorks has been utilised upon the integration of the HESS with a Power Management System (PMS) that operated with four (4) different driving cycles, approved by the Environmental Protection Agency (EPA), such as US06, Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWEFT) and Federal Test Procedure (FTP). The comparison results show MRAC consistently demonstrates superior current tracking compared to PI controller under disturbance conditions, as evidenced by significantly lower RMSE values in HWFET (8.15 vs. 39.74), UDDS (7.4 vs. 31.97), and FTP (6.34 vs. 24.89) drive cycles, respectively. Finally, the results of this study highlight the potential of adaptive control strategies in improving the efficiency, stability, and reliability of power management systems along with BDC for Hybrid Electric Vehicles (HEVs).