Cooling Parasitic Considerations for Optimal Sizing and Power Split Strategy for Military Robot Powered by Hydrogen Fuel Cells University of Michigan

Format:

Conference/Event

Author/Creator:

Siegel, Siegel, author.

Contributor:

Prakash, Niket

Rizzo, Denise

Stefanopoulou, Anna G.

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractMilitary vehicles are typically armored, hence the open surface area for heat rejection is limited. Hence, the cooling parasitic load for a given heat rejection can be considerably higher and important to consider upfront in the system design. Since PEMFCs operate at low temp, the required cooling flow is larger to account for the smaller delta temperature to the air. This research aims to address the combined problem of optimal sizing of the lithium ion battery and PEM Fuel Cell stack along with development of the scalable power split strategy for small a PackBot robot. We will apply scalable physics-based models of the fuel cell stack and balance of plant that includes a realistic and scalable parasitic load from cooling integrated with existing scalable models of the lithium ion battery. This model allows the combined optimization that captures the dominant trends relevant to component sizing and system performance. The baseline optimal performance is assessed using dynamic programming for a reduced order model, by assuming a static cooling load required to maintain the stack at the operating temperature with peak efficiency. Pseudo-spectral optimization methods, which enable fast computation even for larger number of states in the model is then used to consider the additional control of the cooling system. For scaling of the battery in the hybrid system we can use a modular approach, adding cells in parallel and series. If the fuel cell operates always with net power above the peak efficiency point, a simple rule based strategy can nearly recover the optimal fuel consumption achieved with dynamic programming. However, for stack operation at powers near and below the peak eff point the simple rule based strategy performs almost 20% worse than the optimal

Notes:

Vendor supplied data

Publisher Number:

2018-01-0798

Access Restriction:

Restricted for use by site license