Load Prediction and Distributed Optimal Control of On-Board Battery Systems for Dual-Source Trolleybuses

Dual-source electric vehicles powered by both on-board battery and grid electricity offer unique advantages in fuel economy, cost reduction, driveability, and grid support, which are especially appealing for public transportation in populated cities. The structures of their power supply systems create naturally a networked system of vehicles sharing common feeders. Their power management and control strategies must individually enhance each vehicle's local performance, and coordinate globally to limit the grid peak current, reduce current fluctuations, and improve efficiency. This paper introduces a novel methodology that employs load prediction, optimal control, and distributed predictive control for current management in such networked systems without vehicle-to-vehicle communications. Estimation and control strategies are introduced, and computationally efficient recursive algorithms are developed. The power system configuration of the Beijing dual-source trolleybus system is used for simulation case studies on the new management strategies. Estimation accuracy, prediction reliability, and performance improvement from the integrated predictive control strategies are demonstrated. Successful implementation of the methodology can potentially attenuate feeder current fluctuations, reduce feeder peak loads, and alleviate disturbances to main grids.