Cedric Plasse

Performance Comparison of Three Storage Systems for Mild HEVs Using PHIL Simulation

Hybrid electric vehicles (HEVs) would contribute to energy saving and greenhouse effect reduction if they were to be massively launched on the market. A notable effort has been done in simulation to optimize energy consumption and component sizing. Power-hardware-in-the-loop (PHIL) simulation could be a further step to obtain more realistic performance and to compare different solutions, including economic aspects. This paper deals with the implementation on a high dynamic test bench of a diesel mild parallel HEV using the PHIL technique. Three configurations, corresponding to different energy-storage systems, have been tested under the same conditions. Power, energy, consumption, and pollutant emission performance, which are measured on the test bench, are compared and discussed.

Hardware In the Loop Simulation of a Diesel Parallel Mild-Hybrid Electric Vehicle

Hybrid vehicles present a real potential to reduce CO2 emission and energy dependency. The simulation of these vehicles is well adapted to highlight the first order influent parameters. However, more realistic components and HEVs performance versus cost could be identified and improved by testing using the HIL concept. This paper deals with the test and validation of a parallel mild-hybrid vehicle with a first HIL configuration presenting a low additional cost. Measured fuel consumption reduction values for the HEV, compared to a given reference conventional vehicle, are presented and discussed.

Temperature Supervision of an Integrated Starter Generator

This paper deals with the use of an extended Kalman filter (EKF) for the temperature observation of an induction starter generator. At first, we remind considerations about temperature supervision. Then we present a classical method based on a parameter observer. Problems encountered with such an observer are presented in the framework of our application. To overcome these problems we propose a second observer extended to temperature variation using a priori knowledge on parameters evolution model. Experimental results are presented and commented in the last section