Multiple objective optimization of a fuel-cell hybrid electric truck using genetic algorithm

Abstract

The urgency to mitigate CO2 emissions has driven a significant shift towards electrification in the transportation sector. Heavy-duty 40-tonne long-haul vehicles stand out as a crucial target for electrification due to their substantial contribution to emissions and energy consumption. In this study, a novel approach is presented to optimize the energy management system (EMS) of a fuel-cell electric truck using a genetic algorithm (GA). The primary aim of this research is to optimize the EMS for FCEVs to enhance performance while reducing environmental impact and operational costs. The study begins with a comparative analysis focusing on lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC) batteries. The initial BEV model sets a baseline, helping to facilitate subsequent advancements in FCHEV design. Within the FCHEV's EMS, LFP batteries are specifically chosen for their robust charge retention capabilities under varied operational conditions. Detailed simulations ensure that the EMS aligns with real-world driving conditions and meets industry standards. The algorithm prioritizes battery usage for vehicle start-up, peak loads, and regenerative braking, while the fuel-cell system (FCS) provides the primary energy during lower power demands, also recharging the battery to maintain an optimal state of charge (SOC). Using a genetic algorithm, the study optimized the operational SOC range to minimize hydrogen consumption and extend battery lifespan. Initial SOC thresholds were set between 40% and 80%. As a result of the optimization, these thresholds were adjusted to 40-60% to achieve lower DoD of the Battery. This adjustment led to a battery life extension, reduction in hydrogen consumption, decrease in overall fuel-cell system usage reflecting in significant energy and packaging volume savings. The initial optimization phase showed a 3.97% decrease in hydrogen usage (3.21 kg) and a 0.9% drop in unrecoverable energy (5.1 Wh) by keeping the battery's DoD at 35%. Further optimization involved maintaining the FCS operational setpoints at their original levels while adjusting the high and low SOC limits of the EMS using GA. The adjusted SOC thresholds, set at 60% and 40%, led to additional efficiency improvements when integrated into the model. By keeping the battery DoD at 20%, 1.27% reduction in hydrogen consumption (1.02 kg) has been achieved. The final optimization phase involved substituting the initial FCS operating points with those optimized and adjusting the battery's SOC operational boundaries to 40% and 60% following the same GA approach. This final setup reduced hydrogen consumption by 3.96% which equals to 3.17 kg of liquid hydrogen and decreased unrecoverable energy by 5.2 Wh, maintaining the battery DoD at 20%. The management of battery cycling between 40% and 60% SOC significantly extended the battery's lifecycle by reducing electrode degradation and stress on the cells. This optimized strategy not only improves the FCHEV efficiency but also extends its range by roughly 40 km for every 3.17 kg of saved hydrogen, achieving a fuel economy of about 12.62 km per kilogram of hydrogen. Additionally, it reduces the hydrogen storage requirements by about 51.3 liters, providing space that could be used for extra packaging or system components, thereby enhancing the vehicle's utility and functionality. This thesis contributes significantly to the field of EMS for fuel-cell electric trucks, offering valuable insights into optimizing such systems for better efficiency and sustainability. The findings suggest that genetic algorithms are an effective tool for achieving significant enhancements in FCEV performance, marking a promising step towards greener heavy-duty transport solutions.