Adaptive control for hybrid electric vehicles Online publication date: Sun, 28-Aug-2011
by Sanjaka G. Wirasingha, Srdjan M. Lukic, Fernando Rodriguez, Antonis I. Antoniou, Ali Emadi
International Journal of Electric and Hybrid Vehicles (IJEHV), Vol. 3, No. 2, 2011
Abstract: Existing controllers of hybrid electric vehicles (HEV) are mostly tuned to achieve higher fuel economy for specific driving conditions. In this paper, a brief review of control strategies is presented followed by new approaches to implementing an optimisation based controller. First, a control strategy based on stochastic dynamic programming that can be used for real time applications is presented. This strategy achieves about 10% increase in fuel efficiency in most drive cycles over a rule-based control strategy. The second analysis uses an artificial neural network (ANN) to develop an optimised control strategy. Several existing hybrid control strategies are used to model a base for city and highway driving. Elite simulation data was extracted to form a training set for the ANN. The training set was then used to develop a new ANN control strategy that was superior to all the existing strategies used to develop the training set.
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