Decentralized convex optimization-based energy management strategy for modular heavy-duty fuel cell vehicles

Abstract

Modular fuel cell (MFC) systems, which have multiple fuel cell stacks that work independently, are becoming increasingly popular in heavy-duty transportation because they are more efficient, reliable, and easy to use (plug-and-play). This paper aims at developing a lifespan-conscious energy management strategy (EMS) for a heavy-duty modular fuel cell vehicle (MFCV) to enhance the economic performance during the whole life cycle of its components. In this work, the main decision-making problem is broken down into two more minor problems that are solved simultaneously with a decentralized optimization algorithm called the auxiliary problem principle (APP). A comparison is made between the proposed EMS and a well-known central algorithm called sequential quadratic programming (SQP). The simulation results show that a decentralized algorithm can significantly speed up convergence while raising the economic cost a little more than a centralized algorithm.