Anthony Eggert

System dynamics and efficiency of the fuel processor for an indirect methanol fuel cell vehicle

Fuel cell vehicles powered using hydrogen/air fuel cells have received a lot of attention recently as possible alternatives to internal combustion engine. However, the combined problems of on-board hydrogen storage and the lack of hydrogen infrastructure represent major impediments to their wide scale adoption as replacements for IC engine vehicles. On board fuel processors that generate hydrogen from on-board liquid methanol (and other hydrocarbons) have been proposed as possible alternative sources of hydrogen needed by the fuel cell. This paper investigates the dynamic response and efficiency of the on-board fuel processor in an indirect-methanol fuel cell vehicle. This is carried out using a fuel processor model developed in the Matlab/Simulink environment. The fuel processor model includes detailed subsystem models for the steam-reformer, methanol/hydrogen burner, pre-heaters and CO cleanup stages.

Characteristics of an indirect-methanol fuel cell system

This paper discusses the various system interactions that can affect the efficiency and dynamic performance of an indirect methanol fuel cell system. The characterization of the load following IMFC system is done using the simulation model developed by the Fuel Cell Vehicle Modeling Program at the University of California-Davis. The first part of the paper briefly describes the components within the UCD-IMFC system model. The second part of the paper gives a qualitative look at the system interactions and their impact on the efficiency and dynamics of the system. There are two primary interactions within the IMFC system that are of interest. These two interactions are the fuel processor/stack interaction and the air supply/stack interaction. With respect to the fuel processor/stack anode interaction, we find a trade-off between going to a higher anode hydrogen utilization to maximize efficiency and going to a lower utilization to avoid starving the stack of hydrogen during heavy dynamic loads on the system. On the air supply/stack cathode interaction, we find that dynamics are not as much of an issue and the best operating method is one where the net power output of the stack/compressor combined is greatest for a given partial pressure of oxygen. This gives the highest efficiency for a given cathode/air supply combination. Finally, when considering the water and thermal management (WTM) of the system, we look at methods to reduce the total parasitic load on the satisfying the thermal requirements and maintaining water self-sufficiency.

Summary of California Climate Policy Modeling Forum

California is a leader in developing and implementing policies that reduce greenhouse gas (GHG) emissions, improve air quality, and encourage efficient use of energy and other resources. At the same time policymakers are often limited in their access to transparent and high-quality technical and economic models that can help them evaluate plausible future scenarios and assess environmental and economic impacts of current or proposed policy targets and policy instruments.On December 16-17, the Policy Institute for Energy, Environment and the Economy and the Sustainable Transportation Energy Pathways (NextSTEPS), both of UC Davis, hosted a forum as part of the California Climate Policy Modeling (CCPM) project. The CCPM is an ongoing project to bring together policy makers, modeling groups, and key stakeholders in the state to: Improve the state of knowledge of plausible pathways/scenarios for future technology adoption, energy use, air quality, and GHG emissions; Identify plausible mid-point goals and/or targets for GHG emissions between 2020 and 2050; Discuss policy options needed for meeting the state’s climate and air quality goals, identify policy gaps, and improve existing policies; Improve the state of modeling, including identifying ways to make the models and model findings more useful and accessible to policy-makers and other stakeholders.This document is a brief summary of the primary model findings and insights discussed at the December 2013 forum.