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Traffic Information-based Energy Optimization Algorithm for Parallel Hybrid Electric Vehicles

The rapid development of the cloud computing technology and the internet of things (IoT) brings unprecedented opportunities and challenges to automotive industry. The more traffic information enables the advanced control algorithm be adopted in powertrain system to improve the fuel efficiency. This paper proposes a hierarchical control strategy based on historical driving data and real-time traffic information for the powertrain system of hybrid electric vehicle. In the upper layer, the target vehicle obtains the optimal reference SoC trajectory offline through dynamic programming according to the historical data in the cloud; in the lower layer, the Gaussian process regression prediction is adopted to estimate the future speed of the vehicle ahead; and then the optimal power distribution is obtained by solving the energy optimal control problem in the prediction time domain through the sequential quadratic programming algorithm (SQP). Then simulation validations have been implemented to verify the effectiveness of the proposed control strategy.