Design and Real-Time Controller Implementation for a Battery-Ultracapacitor Hybrid Energy Storage System

In this study, two real-time energy management strategies have been investigated for optimal current split between batteries and ultracapacitors (UCs) in electric vehicle applications. In the first strategy, an optimization problem is formulated and solved using Karush-Kuhn-Tucker conditions to obtain the real-time operation points of current split for the hybrid energy storage system (HESS). In the second strategy, a neural network-based strategy is implemented as an intelligent controller for the proposed system. To evaluate the performance of these two real-time strategies, a performance metric based on the battery state-of-health (SoH) is developed to reveal the relative impact of instantaneous battery currents on the battery degradation. A 38 V-385 Wh battery and a 32 V-4.12 Wh UC HESS hardware prototype has been developed and a real-time experimental platform has been built for energy management controller validation, using xPC Target and National Instrument data acquisition system. Both the simulation and real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the two real-time controller designs. It is shown that under a high speed, high acceleration, aggressive drive cycle US06, the two real-time energy management strategies can greatly reduce the battery peak current and consequently decreases the battery SoH reduction by 31% and 38% in comparison to a battery-only energy storage system.