Optimal power flow for hybrid ultracapacitor systems in light electric vehicles

This work demonstrates a predictive power optimization algorithm to control the power mix in a hybrid energy storage system, consisting of an ultracapacitor module and a lithium-ion battery pack for light electric vehicle applications. The algorithm uses a state-based approach, organized as a probability-weighted Markov process to predict future load demands. Decisions on power sharing are made in real-time, based on the predictions and probabilities of state trajectories along with associated system losses. A real-time global optimizer is then used to control the appropriate power mix using dc-dc converters. The full hybrid storage system, along with the mechanical drivetrain is implemented and validated experimentally on a 350 W, 50 V system with a programmable drive-cycle having a strong regenerative component. It is shown that the HESS system runs more efficiently and captures the excess regenerative energy that is otherwise dissipated in the mechanical brakes due to the battery's limited charge current capability.