A Hybrid Energy Management Strategy based on Line Prediction and Condition Analysis for the Hybrid Energy Storage System of Tram

摘要

This article focuses on the optimization of energy management strategy (EMS) for the tram equipped with on-board battery-supercapacitor hybrid energy storage system. The purposes of the optimization are to prolong the battery life, improve the system efficiency, and realize real-time control. Therefore, based on the analysis of a large number of historical operation data, this article proposes a rolling optimization strategy (ROS) based on wavelet neural network prediction and dynamic programming (DP). Furthermore, this article makes condition construction by analyzing the operation characteristics of the tram, and proposes a hybrid EMS based on line prediction and condition analysis, and the strategy makes a cross use of ROS and rule-based (RB) for simplification, and the parameters in the strategy are optimized. Through the analysis of practical cases, three substrategies are verified respectively, and they show obvious optimization effect on the objective, and compared with the whole process DP, the proposed strategy obtains real-time optimization. At the same time, it proves the rationality of using RB strategy to replace ROS strategy entirely or partially in some scenarios.