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Energies 2013, 6(12), 6455-6475; doi:10.3390/en6126455

A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
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Received: 11 October 2013 / Revised: 4 December 2013 / Accepted: 4 December 2013 / Published: 13 December 2013
(This article belongs to the Special Issue Advances in Hybrid Vehicles)
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Abstract

Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO). To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD) is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective. View Full-Text
Keywords: electric vehicles; cooperative braking; combined model; collaborative optimization algorithm; predictive control strategy electric vehicles; cooperative braking; combined model; collaborative optimization algorithm; predictive control strategy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Guo, H.; He, H.; Sun, F. A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle. Energies 2013, 6, 6455-6475.

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