Patrick Guerin

Simulation of Real-World Vehicle Missions Using a Stochastic Markov Model for Optimal Powertrain Sizing

Growing concerns about petroleum supplies and air pollution have spurred increased interest and research into hybrid electric vehicles (HEVs). While standard driving cycles are commonly used for the purpose of propulsion chain optimization, the issue of how representative they actually are is questioned. This paper proposes a novel methodology for modeling real-world vehicle missions by considering the stochastic characteristics of the driving pattern and the dependence among the variables of the mission, i.e., vehicle speed, acceleration, and road gradient. The modeling procedure is based on a Markov matrix formulation, and a simulation algorithm is implemented to generate an unlimited number of stochastic mission profiles. Two kinds of mission natures have been modeled and discussed so as to stress the mission impact on the energy consumption according to the propulsion chain sizing. The approach is then validated using the architecture of a series HEV powered by a diesel generator and batteries. The results on fuel consumption are presented, and the benefit of the proposed method over conventional approaches is argued.

Simulation of real-world vehicle missions using a stochastic Markov model for optimal design purposes

Growing concerns about petroleum supplies and air pollution have spurred the search on hybrid electric vehicles. While standard driving cycles are commonly used for propulsion chain optimization purposes, their representativeness in terms of real-world driving conditions may be questioned. The present paper aims at proposing a vehicle mission model based on a Markov matrix formulation and a general algorithm for generating stochastic mission profiles is implemented. The application on several kinds of mission natures is then proposed and discussed so as to show the mission impact on the energy consumption according to the propulsion chain sizing.

Optimization of the Connection Topology of an Offshore Wind Farm Network

In this paper, an approach based on a genetic algorithm is presented in order to optimize the connection topology of an offshore wind farm network. The main objective is to introduce a technique of coding a network topology to a binary string. The first advantage is that the optimal connections of both middle- and high-voltage alternating-current grids are considered, i.e., the radial clustering of wind turbines, the number and locations of the offshore electrical substations, and the number of high-voltage cables. The second improvement consists of removing infeasible network configurations as designs with crossing cables and thereby reduces the search space of solutions.

Optimization and Reliability Evaluation of an Offshore Wind Farm Architecture

This paper presents an original approach aiming to obtain the optimum configuration of an offshore wind farm (OWF). Thanks to cost models, we take into account the costs off all the parts of the electrical network. The optimization platform, based on a genetic algorithm, also allows us to evaluate the reliability of an OWF. This approach is used to compare the topologies obtained in different cases on a real OWF, the “Banc de Guerande.” The selected design of the OWF is compared to the results obtained using different methods: cost optimization, or both cost and reliability optimization. The optimization results show that the ring topologies give interesting results if the total cost including the expected energy not supplied during the lifetime of the OWF are taken into account. The number of offshore substations and their positions are also considered in the optimization: we show that the introduction of more substations in order to obtain better performances is possible at a reasonable cost. The presented optimization tool can help to design such farms taking into account several constraints.

Investigation of Correlations between Driving Patterns and Power Demand of Auxiliary Devices Aboard Military Vehicles

With the broad introduction of ever more electric equipment and devices aboard military vehicles, demands in energy supply is being increased to fit the mission requirements. The original aspect of this work is to propose a comprehensive review of the correlations between the energy consumption of the auxiliary equipment and the driving patterns in military areas. The tendency to serial correlation of the auxiliary power is also examined and argued. The longer term purpose is to determine the best suited model of the auxiliary power in order to properly size the electrical sources such as storage devices and generators.

Prévision des consommations de puissance selon une approche stochastique: Application aux réseaux îlotés et embarqués

Isolated and on board power systems have to be designed differently compared to utility networks, due to their size and their number of loads. Such power systems require in the early stage of design a right knowledge of the load power in order to define the main electrical equipment suitably. However, the increasing number of loads and their varying operating modes, make the power assessment more difficult whereas the economical and technical constraints impose an optimal electrical sizing. In order to tackle the problem, a load forecasting method taking into account the random load behaviour is proposed. An example of on board power system is presented afterwards to illustrate the benefits of the stochastic approach.