Research on Adaptive Control Strategy of Plug-in Hybrid Electric Vehicle Based on Internet of Vehicles Information.

In order to better improve the fuel economy of plug-in hybrid electric vehicle (PHEV), an adaptive control strategy is proposed with the application of traffic information obtained from internet of vehicles technology. Firstly, the P2-configuration PHEV simulation model is developed based on MATLAB/Simulink. Secondly, a virtual scenario based on SUMO is built to simulate internet of vehicles technology to obtain traffic information. Through the experimental vehicle speed compared with average Baidu API to extract the traffic speed, verify the validity of the virtual scene. Based on the extracted average traffic flow speed, approximate global driving condition is generated by the exponential weighted moving average method. Then, the SOC reference trajectory is generated by the dynamic programming (DP) algorithm based on the acquired approximate global driving condition information. PI control is employed to follow the SOC reference trajectory, enabling adjustment of the equivalent factor adaptively. Finally, the SUMO-MATLAB co-simulation platform is built to validate the effectiveness. It demonstrates that the adaptive equivalent fuel consumption minimization strategy (A-ECMS) with information of internet of vehicles saves 3.6% of fuel consumption compared with ECMS strategy without information of Internet of Vehicles (IoV). To verify the possibility of applying the proposed strategy to a vehicle, a Linux board that can acquire real-time road condition information is developed, applying real-time traffic information to the strategy. The experiment outcomes demonstrate that, in comparison to the ECMS strategy without IoV information, the proposed approach improves fuel efficiency by 3.8%. [ABSTRACT FROM AUTHOR]