Work

Active Disturbance Rejection Versus Model Predictive Controllers for CC–Taper Bidirectional EV Charger Control Based on Genetic Algorithm and Particle Swarm Parameter Optimization

Year: 2026

Type: article

Abstract: Bidirectional electric vehicle (EV) chargers must regulate current and voltage accurately in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes while respecting the tight real-time computational budget of embedded hardware. This paper compares three controllers, active disturbance rejection control (ADRC), a linear model predictive controller (MPC), and a reduced-order quadratic-programming predictive variant (rMPC), for a bidirectional charger supplying a 96-cell nickel–manganese–cobalt battery modeled as a second-order equivalent circuit. The gains of all three controllers are tuned systematically with particle swarm optimization (PSO) and a genetic algorithm (GA) against a weighted objective covering current tracking, terminal-voltage error, disturbance recovery, and control effort. Each controller is evaluated over a constant-current/taper charging profile, a V2G discharge, and injected load disturbances, with robustness quantified by a 500-run Monte Carlo sweep of inductance, resistance, and temperature. After tuning, every controller meets the 5% normalized-error target across all phases, with constant-current tracking error below 2%. ADRC’s mean per-step solve time (∼14.5 μs) is 19–30× smaller than that of the predictive controllers. A 20-seed statistical comparison at equal budget shows PSO and GA to be statistically equivalent tuners, and the decisive practical distinction among three comparably accurate and robust controllers is computational: ADRC offers bounded, negligible worst-case timing, while rMPC and MPC offer marginally faster disturbance recovery at a far higher and less predictable solve cost that exceeds the real-time budget at the 99th percentile.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj17090450

Authors: Abdelrahman M. Hassan, Hamdy A. Ziedan, Mohamed Abdelrahem, Jose Rodriguez, Ali M. Yousef, Essam Ali

Institutions: Assiut University, Technical University of Munich, San Sebastián University

Language: English

Topic: Advanced DC-DC Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,689