Research On Operation Control Strategy Of Hybrid Train

With the continuous development of new energy technology and hybrid technology,the application of on-board energy storage system to the field of rail transit has become a trend of future development.Diesel-electric hybrid locomotives use power batteries and diesel generators as power sources.With this feature,diesel-battery hybrid locomotives are more green and environmentally friendly compared with traditional diesel locomotive.For traditional diesel locomotive,the operating fuel consumption of the train is mainly affected by the train speed curve,while for hybrid power train,the operating fuel consumption of the train is not only related to the train speed curve,but also related to the energy management strategy.The traditional optimization method does not consider the coupling relationship between the speed curve and the energy management strategy.The optimization method is generally sequential optimization,that is,the train speed curve is optimized first,and then the energy management strategy is optimized.Aiming at this problem,this paper adopts a method of simultaneous optimization of speed curve and energy management strategy to optimize the operation and driving of hybrid electric trains.This thesis mainly takes the diesel-battery hybrid train as the research object,firstly analyzes the energy flow characteristics of the hybrid power system,and establishes the whole vehicle model of the hybrid power train,which mainly includes the train dynamics model,the diesel generator fuel consumption model,and the power battery model.After that,a train operation evaluation model was established,which provided the model basis for subsequent research.In this thesis,aiming at an actual line condition,the genetic algorithm is used to optimize the train running fuel consumption,and the train speed curve and energy management strategy are simultaneously optimized.The simulation of sequential optimization is carried out,and different optimization methods are compared and analyzed.The simulation results show that simultaneous optimization saves 4.8% of fuel compared with sequential optimizationIn order to further optimize the fuel consumption of train operation,this thesis adopts the multi-objective genetic algorithm with elite retention strategy to optimize the train speed curve and energy management strategy at the same time with the train running time and running fuel consumption as the goal,and obtains the multi-group operation under the same station.Then,a sensitivity analysis was carried out on the train running time and operating fuel consumption,and it was found that excessively extending the train running time would not necessarily lead to better operating fuel consumption.The analysis can be applied to the re-planning of the full-line timetable of hybrid electric trains.Finally,in order to apply the optimized speed curve to engineering,a fuzzy PID speed tracker based on genetic algorithm parameter optimization is designed in this thesis.The speed tracker considers the train’s running line in advance,and the line resistance is added to the fuzzy PID speed tracker as a feedforward control.In order to shorten the debugging time of the initial parameters of the speed tracker,a genetic algorithm is used to optimize the initial PID parameters of the fuzzy PID speed tracker with the minimum train cumulative speed tracking error as the optimization goal.The PID speed tracker is simulated and compared.The simulation results show that the tracking effect of the fuzzy PID speed tracker is better.