Chunjie Zhai - Academia.edu (original) (raw)

Papers by Chunjie Zhai

2017 Chinese Automation Congress (CAC), 2017

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical... more The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. In order to play the ARIMA model with good linear fitting ability and artificial neural network model with strong nonlinear relation mapping ability, this study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Radial Basis Function Artificial Neural Networks (RBF-ANN) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the RBF-ANN model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for five minutes time-aggregations. The validations of the proposed hybrid methodology were performed by using traffic data from Shinan Avenue in Nansha District, Guangzhou, China. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model as well as RBF-ANN model. The combination method played the advantages of the two models is an effective method for short-term traffic flow forecasting.

IEEE Access, 2021

In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric ve... more In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric vehicles (EVs), and improve the energy economy, an ADHDP-based economic adaptive cruise control (Eco-ACC) strategy for EVs in car-following scenarios is proposed in this paper. First, the longitudinal dynamics of EVs is modeled, and the control objectives are presented; then, the actor-critic structure of ADHDP is introduced, and the policy iteration formulas of the critic and actor networks in the ADHDP framework are given; finally, after the state variables, control variables, unity function and value function are determined, the ADHDP-based Eco-ACC strategy for EVs is designed. Extensive simulation results under different driving cycles show that the proposed Eco-ACC strategy can not only ensure vehicle safety, improve riding comfort and reduce energy consumption, but also significantly reduce the battery capacity loss and extend the battery life compared with the benchmark algorithm. In addition, the proposed Eco-ACC strategy is model-free and real-time, and can be robust in different car-following scenarios.

IEEE Transactions on Intelligent Transportation Systems, 2021

2019 Chinese Control Conference (CCC)

2017 Chinese Automation Congress (CAC)

IEEE Transactions on Vehicular Technology

IEEE Access

To deal with energy crisis and environmental issues, higher fuel economy standards and more strin... more To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this article proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon. INDEX TERMS Cooperative control, heterogeneous platoon, string stability, model predictive control, fuel economy.

IEEE Transactions on Intelligent Transportation Systems

Proceedings of the Institution of Civil Engineers - Transport

IEEE Transactions on Intelligent Transportation Systems

IEEE Access

The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicle... more The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicles into a platoon at a short inter-vehicular distance. Additionally, the eco-driving technology has the potential to further increase fuel efficiency by optimizing the speed trajectories of vehicles. However, little research has been done into the eco-driving of a vehicle platoon. This paper proposes a cooperative look-ahead control strategy for maximizing the fuel efficiency of vehicle platoon travelling on a road with varying slopes. In this paper, the aerodynamic drag, nonlinear engine fuel consumption model, discrete gear ratios, and three engine operating modes are considered in the control strategy; under system constraints, a cooperative optimal fuel consumption problem of vehicle platoon based on distributed model predictive control is formulated; to obtain the optimal solution of the formulated nonlinear optimization problem, after the minimum fuel consumption table and the corresponding optimal control variable tables are obtained, the nonlinear optimization problem with discrete control variables is transformed into a 0-1 binary mixed linear programming problem. Simulation results show that, compared with benchmarks, the proposed control strategy can significantly improve fuel efficiency under different passenger comfort requirements, allowed speed ranges, and predictive control horizons. INDEX TERMS Road vehicles, cooperative systems, fuel economy, distributed control, integer linear programming.

IEEE Transactions on Vehicular Technology

IET Intelligent Transport Systems

2017 Chinese Automation Congress (CAC), 2017

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical... more The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. In order to play the ARIMA model with good linear fitting ability and artificial neural network model with strong nonlinear relation mapping ability, this study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Radial Basis Function Artificial Neural Networks (RBF-ANN) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the RBF-ANN model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for five minutes time-aggregations. The validations of the proposed hybrid methodology were performed by using traffic data from Shinan Avenue in Nansha District, Guangzhou, China. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model as well as RBF-ANN model. The combination method played the advantages of the two models is an effective method for short-term traffic flow forecasting.

IEEE Access, 2021

In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric ve... more In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric vehicles (EVs), and improve the energy economy, an ADHDP-based economic adaptive cruise control (Eco-ACC) strategy for EVs in car-following scenarios is proposed in this paper. First, the longitudinal dynamics of EVs is modeled, and the control objectives are presented; then, the actor-critic structure of ADHDP is introduced, and the policy iteration formulas of the critic and actor networks in the ADHDP framework are given; finally, after the state variables, control variables, unity function and value function are determined, the ADHDP-based Eco-ACC strategy for EVs is designed. Extensive simulation results under different driving cycles show that the proposed Eco-ACC strategy can not only ensure vehicle safety, improve riding comfort and reduce energy consumption, but also significantly reduce the battery capacity loss and extend the battery life compared with the benchmark algorithm. In addition, the proposed Eco-ACC strategy is model-free and real-time, and can be robust in different car-following scenarios.

IEEE Transactions on Intelligent Transportation Systems, 2021

2019 Chinese Control Conference (CCC)

2017 Chinese Automation Congress (CAC)

IEEE Transactions on Vehicular Technology

IEEE Access

To deal with energy crisis and environmental issues, higher fuel economy standards and more strin... more To deal with energy crisis and environmental issues, higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made. Ecological cooperative adaptive cruise control (Eco-CACC) has been considered as an effective solution to decrease the fuel consumption and greenhouse gas emissions of a platoon of vehicles, and this article proposes an Eco-CACC strategy for a heterogeneous platoon of heavy-duty vehicles with time delays. The proposed Eco-CACC strategy consists of distributed control protocols for the following vehicles and a new model predictive controller for the leading one. Firstly, after the distributed control protocols are designed based on the neighboring vehicle information, the sufficient conditions that guarantee the internal stability and string stability are derived, and the upper bound of the time delays under controller parameters is also obtained. Secondly, assuming the vehicle platoon as a rigid body with followers staying at desired positions, the new model predictive controller is designed in order to further improve the overall fuel economy of vehicle platoon. Simulations are conducted to validate the sufficient conditions of internal stability and string stability, and to explore the fuel saving performance of the proposed control strategy. The simulation results demonstrate that, compared with the benchmark, the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon. INDEX TERMS Cooperative control, heterogeneous platoon, string stability, model predictive control, fuel economy.

IEEE Transactions on Intelligent Transportation Systems

Proceedings of the Institution of Civil Engineers - Transport

IEEE Transactions on Intelligent Transportation Systems

IEEE Access

The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicle... more The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicles into a platoon at a short inter-vehicular distance. Additionally, the eco-driving technology has the potential to further increase fuel efficiency by optimizing the speed trajectories of vehicles. However, little research has been done into the eco-driving of a vehicle platoon. This paper proposes a cooperative look-ahead control strategy for maximizing the fuel efficiency of vehicle platoon travelling on a road with varying slopes. In this paper, the aerodynamic drag, nonlinear engine fuel consumption model, discrete gear ratios, and three engine operating modes are considered in the control strategy; under system constraints, a cooperative optimal fuel consumption problem of vehicle platoon based on distributed model predictive control is formulated; to obtain the optimal solution of the formulated nonlinear optimization problem, after the minimum fuel consumption table and the corresponding optimal control variable tables are obtained, the nonlinear optimization problem with discrete control variables is transformed into a 0-1 binary mixed linear programming problem. Simulation results show that, compared with benchmarks, the proposed control strategy can significantly improve fuel efficiency under different passenger comfort requirements, allowed speed ranges, and predictive control horizons. INDEX TERMS Road vehicles, cooperative systems, fuel economy, distributed control, integer linear programming.

IEEE Transactions on Vehicular Technology

IET Intelligent Transport Systems