Predicting power demand and optimizing energy management for fuel cell battery hybrid construction vehicles

Sammanfattning: The automotive industry has been actively seeking ways to reduce emissions and combat global warming. While pure battery electric vehicles have shown promise in achieving zero-tailpipe emissions, they face challenges in meeting the energy demands of large construction machines like excavators and wheel loaders, due to the heavy batteries required. To overcome this issue, Fuel Cell Hybrid Electric Vehicles (FCHEV) have emerged as a potential solution. However, efficient energy management systems are crucial for FCHEV, as fuel cells are slow-reacting devices and construction machines operate with highly transient work cycles. This thesis addresses the need for an effective energy management strategy by developing a controller and machine load predictor for an FCHEV. The proposed approach utilizes Model Predictive Control (MPC) to minimize an objective function encompassing hydrogen consumption and rate constraints. The controller determines the optimal power split between the fuel cell and battery over a specific time-horizon, ensuring power demand is met while adhering to system constraints. Additionally, an auto-correlation-based machine load predictor is integrated with the controller to optimize the power split between the battery and fuel cell. By implementing the MPC combined with the auto-correlation-based load predictor, the FCHEV effectively utilizes a narrower battery State of Charge (SoC) window, potentially reducing the required battery size in the machine. Moreover, the strategy reduces transients in fuel cell power, slowing down degradation and enhancing its lifetime, in comparison to Volvo Construction Equipment AB’s (Volvo CE) previous real-time power-split function. This research contributes to the development of energy-efficient solutions for large construction machines, particularly in the context of FCHEV. The proposed energy management strategy utilizing MPC and load prediction techniques holds promise for improving overall system performance, reducing hydrogen consumption, and limiting the degradation of fuel cell and battery components.