The scientific cooperation agreement signed with INRIA in 2015 covers two fields: the “digital and IT performance of simulators” and the “control and optimization of complex systems”. A thesis carried out in the latter field (1) contributes to the optimized use of the energy on board hybrid vehicles, in such a way as to take full advantage of the potential offered by this technology in terms of reducing CO2 emissions.
Striking the right balance between the Internal Combustion (IC) engine and the electric motor is dependent on an algorithm, known as the energy management system (EMS). However, this does not take into account the impact of traffic conditions, an important factor when it comes to consumption. As a result, the balance between the two sources is not optimized.
In the proposed approach, vehicle dynamics are modeled as a random process, sensitive to various topological road characteristics and different traffic conditions(2). This modeling is made possible thanks to massive mobility and traffic data flows, fed back from vehiclesa. As a result, the new EMS will be able to implement control strategies based on stochastic dynamic optimization techniques. It will thus request a shift in the balance between IC and electric as a function of traffic conditions, as illustrated in the figure below.
The results obtained from real data, gathered in three traffic scenarios over a few kilometers of highway, demonstrate a benefit in terms of overall energy
consumption. The aim now is to validate this new control strategy over a wider geographic area.
a - This research used data provided by the Geco air smartphone app.
(1) A. Le Rhun, A stochastic optimal control for the energy management of a hybrid electric vehicle under traffic constraints, due to be defended in
(2) A. Le Rhun, F. Bonnans, G. De Nunzio, T. Leroy, and P. Martinon, IEEE Transactions on Intelligent Transportation Systems, pp. 1–10, 2019.
DOI : 10.1109/TITS.2019.2923292
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