Energy Demand Prediction: A Partial Information Game Approach

International audienceThis article proposes an original approach to predict the electric vehicles (EVs)' energy demand in a charge station using a regret minimization learning approach. The problem is modelled as a two players game involving: on the one hand the EV drivers, whose demand is unknown and, on the other hand, the service provider who owns the charge station and wants to make the best predictions in order to minimize his regret. The information in the game is partial. Indeed, the service provider never observes the EV drivers' energy demand. The only information he has access to is contained in a feedback function which depends on his predictions accuracy and on the EV drivers' consumption level. The local/expanded accuracy and the ability for uncertainty handling of the regret minimization learning approach is evaluated by comparison with three well-known learning approaches: (i) Neural Network, (ii) Support Vector Machine, (iii) AutoRegressive Integrated Moving Average process, using as benchmarks two data bases: an artificial one generated using a bayesian network and real domestic household electricity consumption data in southern California. We observe that over real data, regret minimization algorithms clearly outperform the other learning approaches. The efficiency of these methods open the door to a wide class of game theory applications dealing with collaborative learning, information sharing and manipulation

Hybrid genetic approach for 1-D bin packing problem

International audienc

Smart and green adaptive cruise control for an electric vehicle: First results

International audienceThis paper concerns the development of the Smart and Green Adaptive Cruise Control aiming at controlling the ego vehicle speed taking into account the road data in order to optimize the energy consumption while improving safety (no collision, under the legal speed, up to the safe headway). A graph search algorithm of Dijkstra is used to build up a velocity pattern offering the best compromise between energy consumed and covered time

Speed profile optimization for electric vehicle with regenerative capacity

International audienc

Smart and Green ACC, adaptation of the ACC strategy for electric vehicle with regenerative capacity

This paper presents an optimization of a conventional Adaptive Cruise Control system (ACC) for the specific use of electric vehicles with regenerative capacity, namely the Smart and Green ACC (SAGA). Longitudinal control strategies, that are developed for the driving assistances, mainly aim at optimizing the safety and the comfort of the vehicle occupants. Electric vehicles have the possibility, depending on the architecture, the speed and the braking demand, to regenerate a part of the electric energy during the braking. Moreover, the electric vehicle range is currently limited. The opportunity to adapt the braking of an ACC system to extend slightly the range must not be avoided. When the ACC is active, the vehicle speed is controlled automatically either to maintain a given clearance to a forward vehicle, or to maintain the driver desired speed, whichever is lower. We define how we can optimize both mode and what is the impact, in term of safety and strategy, including the knowledge of the future of the road, integrating a navigation system