Intelligent Networked Hybrid Electric Vehicle Optimization Using a Linearized Ecms Control Algorithm

Abstract

The purpose is to promote the development of the automotive industry, develop energysaving, safe, comfortable, and environment-friendly HEVs (Hybrid Electric Vehicle). Here, an HEV fleet is researched through the intelligent network connection to improve the fuel economy, traffic fluency, comfort, and safety of the vehicle. The HEV based on linearized ECMS (Equivalent Fuel Consumption Minimization Strategy) control algorithm is partially optimized. Based on the control algorithm of ECMS, cA-ECMS [Equivalent Factor Adaptive Control Strategy Based on Continuous SOC (System on Chip) Feedback] and dA-ECMS (Equivalent Factor Adaptive Control Strategy Based on Discrete SOC Feedback) are proposed. Based on SOC feedback, an online identification A-ECMS (Adaptive ECMS) algorithm is established. The results show that the control algorithm gives full play to the advantages of the adaptive control algorithm and improves the adaptability of the equivalent factor of the HEV. Besides, the fuel consumption, motor total energy consumption, and SOC end value deviation under different SOC initial values are compared. The results show that with the increase of SOC initial value, the end state SOC deviations of cA-ECMS and dA-ECMS control algorithm are about 0.6% and -1.6%, respectively, but the fuel consumption is increasing. Meanwhile, the cA-ECMS control algorithm tends to use fewer batteries. From the perspective of adaptability of equivalent factor, the equivalent factor of the dA-ECMS control algorithm changes less and has better adaptability. All of these lay a solid theoretical foundation for the establishment of an energy management control algorithm for real-time control of the real vehicle.