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For fuel cell vehicles to become a commercial reality, a number of challenges must be met including fuel infrastructure, durability, cost, performance, and efficiency. To help address these challenges, hybrid systems that combine fuel cells with an electrical energy storage system such as batteries or ultracapacitors is a potentially important configuration to increase efficiency and extend fuel cell life times by mitigating transient loads. However, the successful implementation of a hybrid fuel cell system requires achieving performance and efficiency benefits that offsets the additional costs, weight, and complexity of the energy storage system. The key to achieving the levels of efficiency required to justify the hybrid system lies in the power management control strategy. To this end, this paper examines the extension of a novel cost based control strategy developed for conventional internal combustion engine hybrid vehicles to a fuel cell platform (Patent WO 02/42110).

For electric vehicles (EVs), driving range is one of the major concerns for wider customer acceptance and the cabin climate system represents the most significant auxiliary load for battery consumption. Unlike internally combustion engine (ICE) vehicles, EVs cannot utilize the waste heat from an engine to heat the cabin through the heating, ventilation and air conditioning (HVAC) system. Instead, EVs use battery energy for cabin heating, this reduces the driving range. To mitigate this situation, one of the most promising solutions is to optimize the recirculation of cabin air, to minimize the energy consumed by heating the cold ambient air through the HVAC system, whilst maintaining the same level of cabin comfort. However, the development of this controller is challenging, due to the coupled, nonlinear and multi-input multi-output nature of the HVAC and thermal systems.

This paper presents a simulation approach to assess the impact of changes to the charge point infrastructure and policies on Electric Vehicle (EV) user satisfaction, combining both market drivers with the practicalities of EV usage. An agent-based model (ABM) approach is developed where a large number of EVs, that represent the user population, drive within a region of interest. By simulating the driver’s response to their charging experience, the model allows large scale trends to emerge from the population to guide infrastructure policies as the number of EVs increases beyond the initial early adopter market. The model incorporates a Monte Carlo approach to generate EV and driver agent instances with distinct characteristics, including battery size, vehicle type, driving style, sensitivity to range. The driver model is constructed to respond to events that may increase range anxiety, e.g. increasing the likelihood of charging as the driver becomes more anxious.