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
In this paper,an adaptive composite anti-disturbance control of heavy haul trains(HHTs)is proposed.First,the mechanical principle and characteristics of couplers are analysed and the longitudinal multi-particles nonli...In this paper,an adaptive composite anti-disturbance control of heavy haul trains(HHTs)is proposed.First,the mechanical principle and characteristics of couplers are analysed and the longitudinal multi-particles nonlinear dynamic model of HHTs is established,which can satisfy that the forces of vehicles in different positions are different.Subsequently,a radial basis function network(RBFNN)is employed to approximate the uncertainties of HHTs,and a nonlinear disturbance observer(NDO)is constructed to estimate the approximation error and external disturbances.To indicate and improve the approximation accuracy,a serial-parallel identification model of HHTs is constructed to generate a prediction error,and an adaptive composite anti-disturbance control scheme is developed,where the prediction error and tracking error are employed to update RBFNN weights and an auxiliary variable of NDO.Finally,the feasibility and effectiveness of the proposed control scheme are demonstrated through the Lyapunov theory and simulation experiments.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.
基金This research was supported by the National Natural Science Foundation of China(Grants No.U2034211 and 61963029)the Jiangxi Provincial Natural Science Foundation(Grants No.20232ACE01013 and 20232ACB202007)。
文摘In this paper,an adaptive composite anti-disturbance control of heavy haul trains(HHTs)is proposed.First,the mechanical principle and characteristics of couplers are analysed and the longitudinal multi-particles nonlinear dynamic model of HHTs is established,which can satisfy that the forces of vehicles in different positions are different.Subsequently,a radial basis function network(RBFNN)is employed to approximate the uncertainties of HHTs,and a nonlinear disturbance observer(NDO)is constructed to estimate the approximation error and external disturbances.To indicate and improve the approximation accuracy,a serial-parallel identification model of HHTs is constructed to generate a prediction error,and an adaptive composite anti-disturbance control scheme is developed,where the prediction error and tracking error are employed to update RBFNN weights and an auxiliary variable of NDO.Finally,the feasibility and effectiveness of the proposed control scheme are demonstrated through the Lyapunov theory and simulation experiments.
