Abstract
Plug-in hybrid electric vehicles (PHEV) have become vital for oil consumption reduction. They have not achieved their maximum potential due to control strategy limitations. Existing controllers are often tuned to achieve the best fuel economy for specific conditions. It is impractical to optimise a controller for every scenario. A control strategy for PHEVs using artificial neural networks (ANN) is presented. The advantages of implementing a controller using ANN include independence from drive cycle or user, precision and robustness, and updatable training set. Existing PHEV control strategies are used to model a base for city and highway driving. Simulation data was extracted to form an ANN training set, which was used to develop a new strategy that was better than existing ones. The controller was validated using different drive cycles. Furthermore, the sensitivity of ANN controllers is presented. The controller is also used to validate the charge depleting mode of PHEVs.
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