Modeling and Implementation of Control Strategy for EMS of FCEV Cargo Bike

The growing concerns regarding climate change and the environmental impact of using fossil fuels as the primary source for propulsion systems have led to a transition toward more sustainable propulsion systems. Technological advancements, customer preference changes, and policies have favored this transition in the past decades. Sustainable sources like batteries and fuel cells have introduced alternative solutions for replacing the ICEs. The Utilization of EVs in the transport sector offers lower GHG emissions and can reduce the dependence on finite fossil fuels. In this thesis, a MATLAB/Simulink-based model of a Fuel Cell Electric Vehicle (FCEV) powertrain for a cargo bike has been developed to simulate the powertrain’s dynamic behavior across various driving cycles. The model, which includes key components such as the PEMFC, battery, DC-DC boost converter, electric motor, and energy management system, has shown rapid responsiveness and reliable accuracy. Two control strategies—Fuzzy Logic Control (FLC) and Dynamic Programming (DP)—were designed to manage power distribution between the fuel cell and the battery. The backward simulation technique was employed to assess the model’s performance, yielding fast and accurate results. The PEMFC an battery models, built using the DICK-Larmine equivalent circuit and second-order Thevenin equivalent circuit respectively, demonstrated high fidelity to real-world behavior. The energy management systems were evaluated over various drive cycles, including WLTP3, NEDC, JC08, and Artemis rural drive cycle. The DP-based EMS consistently achieved significant reductions in hydrogen consumption, outperforming the initial FLC. To enhance the FLC, a pattern search (PS) algorithm was used to optimize its membership functions based on data from the DP algorithm. The optimized FLC resulted in considerable reductions in hydrogen consumption across all tested drive cycles.