Optimal Controller Design for Bidirectional DC–DC Converter in Battery Electric Vehicle System Using the Adaptive Tabu Search Algorithm

This paper presents an optimal controller design based on an artificial intelligence (AI) technique for battery electric vehicles. In almost electric vehicles, a bidirectional DC–DC converter is employed to regulate the high-voltage DC bus at a constant level. In this study, a bidirectional buck–boost converter is used to maintain the high-voltage DC bus voltage at 800 V. Two controller design approaches are investigated: the conventional method and an AI-based optimization using the adaptive Tabu search (ATS) algorithm. In the proposed ATS framework, a control signal penalty condition is integrated into the objective function to ensure that the designed controller parameters can be implemented in practice without control signal saturation. After the optimal controller parameters are obtained, the system performance is evaluated through simulations using SimPowerSystems® and Hardware-in-Loop testing. The results can confirm that the ATS-based controller achieves better overall performance compared with the conventionally designed controller. Furthermore, additional simulation scenarios not used during the ATS optimization process verify that the proposed controller maintains the high-voltage DC bus compliance with ISO 21498-1 and ISO 21498-2 standards.