ORCID Profile
0000-0002-1763-0397
Current Organisation
Beijing Institute of Technology
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Publisher: MDPI AG
Date: 15-05-2014
DOI: 10.3390/EN7053204
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 11-05-2021
DOI: 10.1186/S10033-021-00555-6
Abstract: An X-by-wire chassis can improve the kinematic characteristics of human-vehicle closed-loop system and thus active safety especially under emergency scenarios via enabling chassis coordinated control. This paper aims to provide a complete and systematic survey on chassis coordinated control methods for full X-by-wire vehicles, with the primary goal of summarizing recent reserch advancements and stimulating innovative thoughts. Driving condition identification including driver's operation intention, critical vehicle states and road adhesion condition and integrated control of X-by-wire chassis subsystems constitute the main framework of a chassis coordinated control scheme. Under steering and braking maneuvers, different driving condition identification methods are described in this paper. These are the trigger conditions and the basis for the implementation of chassis coordinated control. For the vehicles equipped with steering-by-wire, braking-by-wire and/or wire-controlled-suspension systems, state-of-the-art chassis coordinated control methods are reviewed including the coordination of any two or three chassis subsystems. Finally, the development trends are discussed.
Publisher: Elsevier BV
Date: 10-2016
Publisher: IEEE
Date: 08-2014
Publisher: Elsevier BV
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Elsevier BV
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Elsevier BV
Date: 10-2019
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Elsevier BV
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2023
Publisher: IEEE
Date: 06-11-2020
Publisher: Chinese Journal of Mechanical Engineering
Date: 2020
Publisher: Elsevier BV
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 04-2021
Publisher: IEEE
Date: 11-10-2020
Publisher: IEEE
Date: 11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Elsevier BV
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: MDPI AG
Date: 05-07-2018
DOI: 10.3390/EN11071768
Publisher: Elsevier BV
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Elsevier BV
Date: 06-2018
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 05-2020
Publisher: IEEE
Date: 27-11-2020
Publisher: Elsevier BV
Date: 2014
Publisher: MDPI AG
Date: 25-02-2023
DOI: 10.3390/SU15054168
Abstract: In-wheel-motor-drive electric vehicles have attracted enormous attention due to its potentials of improving vehicle performance and safety. Road surface roughness results in forced vibration of in-wheel-motor (IWM) and thus aggravates the unbalanced electric magnetic force (UEMF) between its rotor and stator. This can further compromise vertical and longitudinal vehicle dynamics. This paper presents a comprehensive study to reveal the coupled vertical–longitudinal effect on suspension-in-wheel-motor systems (SIWMS) along with a viable optimization procedure to improve ride comfort and handling performance. First, a UEMF model is established to analyze the mechanical–electrical–magnetic coupling relationship inside an IWM. Then a road–tire–ring force (RTR) model that can capture the transient tire–road contact patch and tire belt deformation is established to accurately describe the road–tire and tire–rotor forces. The UEMF and the RTRF model are incorporated into the quarter-SIWMS model to investigate the coupled vertical–longitudinal vehicle dynamics. Through simulation studies, a comprehensive evaluation system is put forward to quantitatively assess the effects during braking maneuvers under various road conditions. The key parameters of the SIWMS are optimized via a multi-optimization method to reduce the adverse impact of UEMF. Finally, the multi-optimization method is validated in a virtual prototype which contains a high-fidelity multi-body model. The results show that the longitudinal acceleration fluctuation rate and the slip ratio signal-to-noise ratio are reduced by 5.07% and 6.13%, respectively, while the UEMF in the vertical and longitudinal directions varies from 22.2% to 34.7%, respectively, and is reduced after optimization. Thus, the negative coupling effects of UEMF are minimized while improving the ride comfort and handling performance.
Publisher: SAGE Publications
Date: 24-04-2018
Abstract: This paper presents a vehicle sideslip angle estimation scheme against noises and outliers in sensor measurements for a four-wheel-independent-drive electric vehicle. The proposed scheme combines a robust unscented Kalman filter estimator based on the 3-DOF vehicle dynamics model and an extended Kalman filter estimator based on the kinematic model to form a hybrid estimator through a weighting factor. The weighting factor can be dynamically adjusted in real time to optimize the overall estimation performance under different driving conditions. The main contributions of this study to the related literature lie in two aspects. Firstly, a robust unscented Kalman filter estimator was incorporated to improve the robustness of dynamics-based estimation to sensor measurement outliers. Secondly, a novel moving polynomial Kalman smoother was included to filter out the noises in sensor measurements. Co-simulations of Matlab/Simulink and Carsim software were conducted under typical vehicle maneuvers and show that the proposed vehicle sideslip angle estimation scheme can obtain satisfied estimation results, with the moving polynomial Kalman smoother exhibiting better phase characteristics and filtering performance relative to commonly-used finite impulse response filters, and the robust unscented Kalman filter estimator being robust to sensor measurement outliers.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Chinese Journal of Mechanical Engineering
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Elsevier BV
Date: 06-2017
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 08-2015
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/959065
Abstract: In order to adapt the matching and planning requirements of charging station in the electric vehicle (EV) marketization application, with related layout theories of the gas stations, a location model of charging stations is established based on electricity consumption along the roads among cities. And a quantitative model of charging stations is presented based on the conversion of oil sales in a certain area. Both are combining the principle based on energy consuming equivalence substitution in process of replacing traditional vehicles with EVs. Defined data are adopted in the ex le analysis of two numerical case models and analyze the influence on charging station layout and quantity from the factors like the proportion of vehicle types and the EV energy consumption at the same time. The results show that the quantitative model of charging stations is reasonable and feasible. The number of EVs and the energy consumption of EVs bring more significant impact on the number of charging stations than that of vehicle type proportion, which provides a basis for decision making for charging stations construction layout in reality.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/EN16062619
Abstract: Electric vehicles are becoming dominant in the global automobile market due to their better environmental friendliness compared to internal combustion vehicles. An adequate network of public charging stations is required to fulfil the fast charging demands of EV users. Knowing the shape and litude of their power curves is essential for power purchase planning and grid capacity sizing. Based on a large-scale empirical and representative dataset, this paper creates standard load profiles for various power levels, station sizes, and operating environments. It is found that the average power per charge point increases with rated station power, particularly for a rated power above 100 kW, and decreases with the number of charge points per station for AC chargers. For AC chargers, it is revealed how the shape of the power curve largely depends on the environment of a station, with urban settings experiencing the highest average power of 0.71 kW on average leading to an annual energy sale of 6.2 MWh. These findings show that the rated grid capacity can be well below the sum of the rated power of each charge point.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: MDPI AG
Date: 08-07-2017
DOI: 10.3390/EN10070947
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: AME Publishing Company
Date: 12-2017
Publisher: Elsevier BV
Date: 10-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 30-05-2018
Publisher: IEEE
Date: 06-11-2020
Publisher: Elsevier BV
Date: 05-2016
Publisher: Elsevier BV
Date: 05-2017
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 29-11-2020
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Start Date: 2019
End Date: 2021
Funder: National Natural Science Foundation of China
View Funded ActivityStart Date: 2020
End Date: 2023
Funder: Beijing Municipal Science and Technology Commission
View Funded Activity