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Dr. Ying Tian
Beijing Jiaotong University

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0 fault diagnosis
0 calculation model
0 Fuel cell vehicle
0 Hydrogen Consumption
0 Temperature and Pressure Method

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Journal article
Published: 08 June 2021 in World Electric Vehicle Journal
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In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition.

ACS Style

Ying Tian; Jiaqi Liu; Qiangqiang Yao; Kai Liu. Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming. World Electric Vehicle Journal 2021, 12, 85 .

AMA Style

Ying Tian, Jiaqi Liu, Qiangqiang Yao, Kai Liu. Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming. World Electric Vehicle Journal. 2021; 12 (2):85.

Chicago/Turabian Style

Ying Tian; Jiaqi Liu; Qiangqiang Yao; Kai Liu. 2021. "Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming." World Electric Vehicle Journal 12, no. 2: 85.

Journal article
Published: 30 March 2021 in Energies
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Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.

ACS Style

Ying Tian; Qiang Zou; Jin Han. Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification. Energies 2021, 14, 1918 .

AMA Style

Ying Tian, Qiang Zou, Jin Han. Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification. Energies. 2021; 14 (7):1918.

Chicago/Turabian Style

Ying Tian; Qiang Zou; Jin Han. 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification." Energies 14, no. 7: 1918.

Journal article
Published: 14 December 2020 in IEEE Access
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Proton exchange membrane fuel cell vehicles using hydrogen as fuel has become one of the key development directions in the field of new energy vehicles. However, hydrogen is flammable, it is easy to trigger serious safety accidents once hydrogen leak occurs. Therefore, the application of hydrogen leakage diagnosis methods is essential for the safe operation of proton exchange membrane fuel cell system. This paper reviews the existing methods of diagnosing hydrogen leakage for proton exchange membrane fuel cell system. The principles, research status and application scopes of different methods are elaborated, and these methods are analyzed based on three aspects of on-line diagnosis, real-time performance and calculation complexity. Furthermore, suggestions are provided for the selection of on-line hydrogen leakage diagnosis methods for fuel cell vehicles. Based on this review, readers can easily understand the current status of hydrogen leakage diagnosis methods for proton exchange membrane fuel cell system, and it can provide references for the application selection of the hydrogen leakage diagnosis methods for fuel cell vehicles.

ACS Style

Ying Tian; Qiang Zou; Zezhao Lin. Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles. IEEE Access 2020, 8, 224895 -224910.

AMA Style

Ying Tian, Qiang Zou, Zezhao Lin. Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles. IEEE Access. 2020; 8 (99):224895-224910.

Chicago/Turabian Style

Ying Tian; Qiang Zou; Zezhao Lin. 2020. "Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles." IEEE Access 8, no. 99: 224895-224910.

Journal article
Published: 28 August 2020 in IEEE Access
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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

ACS Style

Qiangqiang Yao; Ying Tian; Quan Wang; Shengyuan Wang. Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges. IEEE Access 2020, 8, 161211 -161222.

AMA Style

Qiangqiang Yao, Ying Tian, Quan Wang, Shengyuan Wang. Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges. IEEE Access. 2020; 8 (99):161211-161222.

Chicago/Turabian Style

Qiangqiang Yao; Ying Tian; Quan Wang; Shengyuan Wang. 2020. "Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges." IEEE Access 8, no. 99: 161211-161222.

Journal article
Published: 16 May 2020 in Energies
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In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor C and kernel function parameter g. Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.

ACS Style

Feng Han; Ying Tian; Qiang Zou; Xin Zhang. Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System. Energies 2020, 13, 2531 .

AMA Style

Feng Han, Ying Tian, Qiang Zou, Xin Zhang. Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System. Energies. 2020; 13 (10):2531.

Chicago/Turabian Style

Feng Han; Ying Tian; Qiang Zou; Xin Zhang. 2020. "Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System." Energies 13, no. 10: 2531.

Journal article
Published: 06 November 2019 in Applied Sciences
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Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

ACS Style

Qiangqiang Yao; Ying Tian. A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking. Applied Sciences 2019, 9, 4739 .

AMA Style

Qiangqiang Yao, Ying Tian. A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking. Applied Sciences. 2019; 9 (22):4739.

Chicago/Turabian Style

Qiangqiang Yao; Ying Tian. 2019. "A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking." Applied Sciences 9, no. 22: 4739.

Journal article
Published: 04 June 2018 in Energies
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This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire’s lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

ACS Style

Chuanyang Sun; Xin Zhang; Lihe Xi; Ying Tian. Design of a Path-Tracking Steering Controller for Autonomous Vehicles. Energies 2018, 11, 1451 .

AMA Style

Chuanyang Sun, Xin Zhang, Lihe Xi, Ying Tian. Design of a Path-Tracking Steering Controller for Autonomous Vehicles. Energies. 2018; 11 (6):1451.

Chicago/Turabian Style

Chuanyang Sun; Xin Zhang; Lihe Xi; Ying Tian. 2018. "Design of a Path-Tracking Steering Controller for Autonomous Vehicles." Energies 11, no. 6: 1451.