Comparison of Decentralized ADMM Optimization Algorithms for Power Allocation in Modular Fuel Cell Vehicles

Abstract

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems.