Energy management in hybrid electric vehicles : benefit of prediction


== Abstract ==

Hybrid vehicles require a supervisory algorithm, often referred to as energy management strategy, which governs the drivetrain components. In general the energy management strategy objective is to minimize the fuel consumption subject to constraints on the components, vehicle performance and driver comfort. Typically, we have to deal with two difficulties in the design of an energy management strategy. Firstly, the nonlinear behavior of the components results in a nonconvex cost function, complicating the use of optimization methods. Different approaches to deal with the nonconvexity are discussed. Secondly, the future power and velocity trajectories are unknown. Prediction of the future trajectories, based upon either past or predicted vehicle velocity and road grade trajectories, could help in obtaining a solution close to optimal. The benefit of prediction, compared to a heuristic and an optimal control strategy that uses only actual vehicle data, is shown with an example of a hybrid truck at a highway trajectory in a hilly environment. Results indicate that prediction has benefits only when the slopes have sufficient grade and length, such that the battery state-of-charge boundaries are reached. © 2010 IFAC.


== Original document ==

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* [http://pdfs.semanticscholar.org/7999/77cb225910ca40e7d838e54e9d0ff67c8a64.pdf http://pdfs.semanticscholar.org/7999/77cb225910ca40e7d838e54e9d0ff67c8a64.pdf]

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: [http://dx.doi.org/10.3182/20100712-3-de-2013.00027 http://dx.doi.org/10.3182/20100712-3-de-2013.00027] under the license https://www.elsevier.com/tdm/userlicense/1.0/

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: [https://academic.microsoft.com/#/detail/2169845658 https://academic.microsoft.com/#/detail/2169845658]