Data-Driven Prediction of Li-Ion Battery and PEM Fuel Cell Performance Degradations for Balanced Optimal Energy Management of Electrified Propulsion Systems

Abstract

With the increasing pace of commercialization, the proton exchange membrane fuel cell (PEMFC) system-powered fuel cell electric vehicles/vessels (FCEVs) present a highly efficient, zero tailpipe emission propulsion solution. A battery energy storage system (BESS) is normally integrated with the PEMFC system to improve its performance, energy efficiency and operational life. However, both the PEMFC system and the BESS suffer from relatively short operation life and high replacement costs. Optimal energy management strategies (EMSs) become essential to improve their working conditions, thus extending their working life based on their distinct performance degradation behaviours and achieving the minimum lifecycle costs (LCCs). Extending from the present static modelling approach, this research introduces three new methods for dynamically updating the performance and degradation models of lithium-ion (Li-ion) batteries and PEMFCs using real-time operation data of a fuel cell–battery hybrid electric propulsion system. The combined methods more accurately capture the performance and capability of each specific fuel cell hybrid propulsion system’s BESS and PEMFC system. This enables precise performance tracking, degradation assessment and optimal energy management. A new integrated approach to the hybrid electric propulsion system’s component sizing design optimization and optimal energy management is introduced using these new modelling schemes, minimizing the LCC by balancing the propulsion system performance, fuel economy and the BESS and PEMFC system degradations. These modelling and optimization methods are applied to a medium-sized vehicle and passenger ferry to produce the optimal fuel cell–battery hybrid propulsion system design and EMS to strike the best balance between fuel efficiency and the PEMFC and BESS operation life.