Electric vehicles such as trains must match their electric power supply and demand,such as by using a composite energy storage system composed of lithium batteries and supercapacitors.In this paper,a predictive contro...Electric vehicles such as trains must match their electric power supply and demand,such as by using a composite energy storage system composed of lithium batteries and supercapacitors.In this paper,a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train.The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain.Real-time online control of power allocation in the composite energy storage system can be achieved.Using standard train operating conditions for simulation,we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running.Compared with traditional predictive control systems,energy efficiency 10.5%higher.This system provides good stability and robustness,satisfactory speed tracking performance and control comfort,and significant suppression of disturbances,making it feasible for practical applications.展开更多
基金This work was supported by the Youth Backbone Teacher Training Program of Henan Colleges and Universities under grant no.2016ggjs-287the Project of Science and Technology of Henan Province under grant nos.172102210124 and 20210221026the Key Scientific Research Project in Colleges and Universities in Henan,grant no.18B460003.
文摘Electric vehicles such as trains must match their electric power supply and demand,such as by using a composite energy storage system composed of lithium batteries and supercapacitors.In this paper,a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train.The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain.Real-time online control of power allocation in the composite energy storage system can be achieved.Using standard train operating conditions for simulation,we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running.Compared with traditional predictive control systems,energy efficiency 10.5%higher.This system provides good stability and robustness,satisfactory speed tracking performance and control comfort,and significant suppression of disturbances,making it feasible for practical applications.