This paper presents the development of a global and integrated sizing approach under different performance indexes applied to fuel cell/battery hybrid power systems. The strong coupling between the hardware sizing process and the system supervision (energy management strategy EMS) makes it hard for the design to consider all the possibilities, and today’s methodologies are mostly experience-based approaches that are impervious to technological disruption. With a smart design approach, new technologies are easier to consider, and this approach facilitates the use of new technologies for transport applications with a decision help tool. An automotive application with a hybrid fuel cell (PEMFC)/battery (Li-Ion) is considered to develop this approach. The proposed approach is based on imbricated optimization loops and considers multiple criteria such as the fuel consumption, reliability, and volume of the architecture, in keeping with industry expectations to allow a good trade-off between different performance indexes and explore their design options. This constitutes a low computational time and a very effective support tool that allows limited overconsumption and lifetime reduction for designed architecture in extreme and non-optimal use. We obtain, thanks to this work, a pre-design tool that helps to realize the first conception choice.
This paper presents an optimal design methodology enabling to exhibit the best parameters of a complex energy system combing several components and their related control parts. It is based on a particle swarm optimization technique for component sizing, combined with optimal control to consider energy management constraints. This approximate resolution is valuable since it allows to achieve a robust and effective optimal design using low computational resources: it enables to tackle large search spaces in engineering time constraints. The selected use case is a fuel cell/battery hybrid power source based on a power-split parallel architecture. Its performance index is defined as the fuel consumption. Regarding this objective, the drivetrain components size and the control parameters values are both strongly coupled and physically constrained. In this context, the methodology makes a tradeoff between component sizing and energy saving. Simulation results show the relevance and robustness of this approach regarding different driving cycles and operating conditions. It validates the replicability of this method to other optimization problems in the field of energy optimization. A comprehensive review of the simulation tests highlights the present limits of this optimization and provides new perspectives for future works.
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