Pla, Benjamín (Universitat Politècnica de València), Bares, Pau (Universitat Politècnica de València), Aronis, André (Universitat Politècnica de València), Pinto, Douglas (Universitat Politècnica de València)

Degradation-Aware Predictive Energy Management for Fuel Cell Electric Vehicles

Scheduled for presentation during the Regular session "Powertrain Control I" (FrA1), Friday, November 1, 2024, 08:30−08:50, Room T1

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

Controlling fuel cell degradation poses a significant challenge for the widespread adoption of Fuel Cell Electric Vehicles (FCEVs) due to the high costs associated with the materials used in these systems. Given the complex powertrain of such vehicles, energy management strategies (EMS) are crucial for their efficiency and energy consumption. This paper develops a predictive EMS for FCEVs by designing an objective function to minimize the impact of fuel cell system degradation and employing offline Dynamic Programming optimization of a previously covered trip. The resulting cost-to-go matrix serves as a proxy for the terminal cost in an iterative finite time horizon to be optimized in an MPC approach. In parallel, an Extended Kalman Filter (EKF) is used for the online adaptation of the fuel cell model utilized in the EMS. This adaptive strategy enhances model accuracy under different driving conditions, correcting bias and drift caused by long-term fuel cell usage. Simulations of real-world driving cycles validate the proposed EMS, highlighting the trade-off between fuel cell lifespan and fuel consumption. The study shows that the fuel cell lifespan can be extended by up to 60% without increasing fuel consumption, or fuel consumption can be reduced by 1.2% while increasing the fuel cell lifespan by 42%, compared to the baseline strategy, which does not employ adaptive calibration and the designed objective function.