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Game-theoretic Energy Management with Velocity Prediction in Hybrid Electric Vehicle

This paper studies a game-theoretic energy management for hybrid electric vehicle (HEV), combining of engine-generator, battery and ultracapacitor (UC). Each energy source has different utility function with different preference. Here, we use game-theoretic strategy to dispatch power because it can represent different preferences for different components. In addition, a game-theoretic strategy with velocity prediction (VP) is proposed to improve the basic game-theoretic strategy, ie., game-theoretic management without velocity prediction. Here, a recurrent neural network long short term memory (RNN-LSTM) structure is implemented to predict future velocities. Since the dataset of driving cycles has only one feature, velocity, two different feature engineering methods are proposed to improve the accuracy of VP. Next, Nash equilibrium of game-theoretic management has been realized through the best response functions and the performance of game-theoretic management has been carried out. Finally, four criteria are used to quantify the performance of the proposed energy management and that of the game-theoretic management without VP. A comparative analysis in simulation results validates that the proposed method gives a better performance with less battery power variation, more battery usage power, less engine-generator energy and more UC average energy difference.