Xiyuan Niu
Xiyuan Niu has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Jianjun Hu
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China
4 shared publications Guoqiang Zu
Shool of Automotive EngineeringChongqing University Chongqing China
2 shared publicationsChunyun Fu
School of Automotive Engineering, Chongqing University, Chongqing 400044, China
1 shared publicationFeng Xiao
Chassis Department, GAC R&D Center, Guangzhou, Guangdong 510000, China
1 shared publicationJunlong Tao
School of Automotive Engineering, Chongqing University, Chongqing 400044, China
1 shared publicationMeixia Jia
1 shared publication
Latest Publications
- Open Access
- 30 September 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 141826-141837
An Optimal Torque Distribution Control Strategy for Four-Wheel Independent Drive Electric Vehicles Considering Energy Economy
To further improve the energy economy of a four-wheel independent drive electric vehicle (FWIDEV) in the process of vehicle stability control, in this paper, the influence of different wheel torque distributions on vehicle stability and energy economy during vehicle steering is analyzed in depth. Then the wheel torque distribution scheme when the vehicle steering is established. Combined with the economic-based torque distribution strategy applied in the straight running condition, an optimal wheel torque distribution strategy is proposed for FWIDEV to adapt different driving conditions. And the controller designed in this paper adopts hierarchical control structure. The upper controller calculate the corrective yaw moment based on the sliding mode control. The lower controller implements wheel torque distribution according to the proposed strategy. Finally, the simulation results under different driving scenarios indicate that the proposed control strategy can achieve the same effect as the conventional control strategy in terms of vehicle stability, but the energy economy is improved by about 2.4%.
- 30 September 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 7, 141826-141837
Open AccessAn Optimal Torque Distribution Control Strategy for Four-Wheel Independent Drive Electric Vehicles Considering Energy Economy
To further improve the energy economy of a four-wheel independent drive electric vehicle (FWIDEV) in the process of vehicle stability control, in this paper, the influence of different wheel torque distributions on vehicle stability and energy economy during vehicle steering is analyzed in depth. Then the wheel torque distribution scheme when the vehicle steering is established. Combined with the economic-based torque distribution strategy applied in the straight running condition, an optimal wheel torque distribution strategy is proposed for FWIDEV to adapt different driving conditions. And the controller designed in this paper adopts hierarchical control structure. The upper controller calculate the corrective yaw moment based on the sliding mode control. The lower controller implements wheel torque distribution according to the proposed strategy. Finally, the simulation results under different driving scenarios indicate that the proposed control strategy can achieve the same effect as the conventional control strategy in terms of vehicle stability, but the energy economy is improved by about 2.4%.
- 12 March 2019
- Published by Wiley in International Journal of Energy Research
- Vol. 43 (8) , 3346-3364
Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle
- 12 March 2019
- Published by Wiley in International Journal of Energy Research
- Vol. 43 (8) , 3346-3364
Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle
- 1 February 2019
- Published by Elsevier in Mechanical Systems and Signal Processing
- Vol. 116, 113-128
Parameter matching and optimal energy management for a novel dual-motor multi-modes powertrain system
- 1 February 2019
- Published by Elsevier in Mechanical Systems and Signal Processing
- Vol. 116, 113-128
Parameter matching and optimal energy management for a novel dual-motor multi-modes powertrain system
- Open Access
- 14 September 2018
- Published by SAGE Publications in Advances in Mechanical Engineering
- Vol. 10 (9)
Research on energy management strategy considering battery life for plug-in hybrid electric vehicle
To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.
- 14 September 2018
- Published by SAGE Publications in Advances in Mechanical Engineering
- Vol. 10 (9)
Open AccessResearch on energy management strategy considering battery life for plug-in hybrid electric vehicle
To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.