Modelling and Control of Alternative Hybrid Propulsion Systems for High Efficiency Heavy-Duty Vehicles, and Hardware-in-the-Loop Considerations

The development of hybrid electric propulsion systems offers significant opportunities to enhance vehicle efficiency and reduce greenhouse gas emissions, particularly for heavy-duty vehicles. This thesis explores the integration of advanced energy storage systems, including batteries, supercapacitors, and fuel cells, within electric vehicles. The primary goal is to optimize energy management strategies and then prepare the vehicle model and control strategies for Hardware-in-the-Loop (HIL) testing to validate these systems under realistic conditions. Initially, the study focuses on a Battery Electric Vehicle (BEV) model using a BYD K9 series bus as the baseline. The simulation environment replicates the vehicle's dynamics and integrates various control strategies. The BEV model serves as a reference to evaluate the benefits of incorporating additional energy sources like supercapacitors and fuel cells. The hybrid energy storage system (HESS) is introduced to enhance the BEV's performance. By adding a supercapacitor bank alongside the battery, the vehicle's ability to handle high power demands and capture regenerative braking energy improves. This integration reduces battery stress and extends its lifespan. The thesis examines different configurations and control strategies for the HESS, including rule-based, adaptive rule-based, and fuzzy logic controllers. Building upon the BEV enhanced with supercapacitors, the next step involves integrating a fuel cell to further optimize the system, resulting in a Fuel Cell Hybrid Electric Vehicle (FCHEV). The objective is to significantly reduce the battery size by leveraging the fuel cell stack as the primary energy source. Moreover, another control strategy was introduced in this phase called the adaptive neuro-fuzzy inference system (ANFIS). The ANFIS controller was used to enhance adaptability to changing vehicle mass and improve overall energy management. It combines the learning capabilities of neural networks with the fuzzy logic control's ability to handle uncertainty, leading to more efficient power distribution and better vehicle performance under varying load conditions. The simulations show a 25% reduction in battery C-rate for the powertrain configuration that includes both a battery and a supercapacitor bank. Additionally, in the powertrain setup featuring a battery, fuel cell, and supercapacitor bank, the overall mass of the HESS is reduced by 495 kg (a 15% reduction) compared to the baseline BEV model, while maintaining the vehicle's range. To validate the simulation results, HIL testing is proposed. This involves integrating hardware components with the simulation models to evaluate system performance under various conditions. One of the key components in setting up the test bench for HIL testing is the modular supercapacitor bank. The modular design of the supercapacitor bank developed and 3D printed as part of this research, allows for flexible testing and optimization of different configurations. In summary, this thesis contributes to developing more efficient and environmentally friendly electric vehicles with hybrid energy storage systems. It provides a comprehensive analysis of energy management strategies and prepares the groundwork for experimental validation through HIL testing, ensuring the practical applicability of the proposed solutions in real-world scenarios.