DEVELOPING AN OPTIMAL AND REAL-TIME IMPLEMENTABLE ENERGY MANAGEMENT SYSTEM FOR A FUEL CELL ELECTRIC VAN WITH ENHANCED FUEL CELL AND BATTERY LIFE AND PERFORMANCE

Abstract

This research presents a two-part study on a fuel cell electric van (FCEV), focusing on vehicle modelling and developing different control strategies for the modelled vehicle. The modelling phase accounts for the aging effects on the fuel cell (FC) and battery, analyzing FCEV behavior over time. This includes estimating and integrating the degradation impacts on characteristic curves, such as the FC’s polarization and efficiency curves, the battery’s charging and discharging resistance curves, and the open-circuit voltage curve. A simplified fuel cell system (FCS) model is designed to consider power losses in multiple components, including the FC stack, air compressor, and others. The dynamic limits of the FC are also included to yield more realistic results. The model is based on the vehicle Opel Vivaro FC specifications, incorporating parameters like maximum FC power, battery capacity, vehicle weight, and tire dimensions. Subsequently, various control strategies are applied to analyze their effectiveness in FC and battery State-of-Health (SOH) degradation and hydrogen consumption. A rule-based energy management system (EMS) is implemented first, which operates with five different operational modes dependent on the vehicle’s state. This is followed by a look-up table (LUT) based strategy, which uses two two-dimensional tables generated by a Neural Network (NN). The network is trained with discretized optimal