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A model is presented to simulate the production, storage, and dispensing of hydrogen under different conditions and demands. It entails modeling four main processes: production of hydrogen by electrolysis, its compression by an electrochemical process, a high-pressure storage, and a fuel-cell car-dispensing system. The Proton Exchange Membrane (PEM) electrolysis model is based on an equivalent circuit to compute the electric energy needed for a certain demand of hydrogen. A thermal model is integrated to determine the operating temperature during the electrolysis. The second phase of the model deals with the compression of hydrogen using an electrochemical compressor whose operating advantages are highlighted and compared to a mechanical compressor. A thermal model is also integrated to the compression process.

Two fuel cell hybrid electric vehicle (FCHEV) designs are being proposed and evaluated. The first is a baseline model inspired by a Toyota Venza (2009) design where the body of the FCHEV is mainly composed of steel/iron 67% while the plastic and aluminum forming only 14%, and with a body or glider mass of 1290 kg. The advanced model is based on a light body vehicle inspired from a Lotus Engineering design where plastic and aluminum constitute around 39 % of the total glider mass while mild steel and iron are only 7%. The use of such light materials allows the reduction of the glider mass by around 38.4 % down to 795 kg and a projected cost increase of 3% only. Although some material used are more expensive than steel/iron, the significant mass reduction offsets the increased cost due to using more expensive material. Furthermore, the mass of the added components in the advanced design was significantly lower than those added in the baseline.

The development of an energy management system for a fuel cell hybrid electric vehicle (FCHEV) based on single step dynamic programming (SSDP) is described in this paper. The SSDP method is used to minimize a weighted cost of hydrogen and battery degradation with the latter being controlled to carry out charge-depleting (CD) as well as charge-sustaining (CS) strategies with simple lower bound enforcement or relaxation. The problem formulation accounts for the power balance at each stage, the fuel cell and battery power limits, the battery state-of-charge limits, and the ramp-rates constraints of the fuel cell and battery. Its chief advantage over forward dynamic programming (DP) or other formal optimization methods is that it does not require the speed forecast of the whole drive cycle but requires only a one-step-ahead speed forecast.

The University of Applied Sciences Esslingen (UASE) is a partner in the collaborative EU project PHAEDRUS (high Pressure Hydrogen All Electrochemical Decentralized RefUeling Station) as part of the EU work programme SP1-JTI-FCH.2011.1.8 Research and Development of 700 bar refueling concepts and technologies. The subtask of UASE is the simulation, sizing and analysis of a new concept for a 100 MPa hydrogen refueling station enabling self-sustained infrastructure roll-out for early vehicle deployment volumes, showing the applicability of the electrochemical hydrogen compression (EHC) technology in combination with an on-site anion exchange membrane electrolyser (AEMEC), storage units, precooling and a dispensing system. The electrolyser and the compressor are modeled using the electrochemical equations and the conservation of mole balance.

An optimal design methodology is developed in this paper for fuel cell hybrid electric vehicles (FCHEV) based on ordinal optimization (OO) and dynamic programming (DP); the optimal design aims to determine the appropriate sizes of the hydrogen tank, fuel cell, battery, and motor for the purpose of minimizing investment and operational cost given some specification of the car range, the road type and its gradeability. The DP simulates the operation of the vehicle for a set of specified components' sizes for given driving cycles and provides the total vehicle cost per year. The OO method offers an efficient approach for optimization by focusing on ranking and selecting a finite set of “good enough” alternatives through two models: a simple model and an accurate model. The OO program uses the specified sizes of the components that uniformly sample the search space and evaluates these designs using a simple but fast model.

An optimal energy management system is presented to minimize hydrogen utilization over driving cycles using forward dynamic programming (FDP). The objective is to minimize the cost of hydrogen with the battery cost being used as a parameter to carry out charge-depleting as well as charge-sustaining strategies along with bound enforcement or relaxation. The problem formulation accounts for the power balance at each stage, the power limits, the state-of-charge limits, and the ramp rates constraints of the fuel cell and battery. FDP is selected because it can easily cater for non-linearity in system cost and constraints. It employs heuristic rules to limit the number of states at each stage and is shown to be a very fast algorithm using simple computations and thus may easily lend itself for real-time implementation.