Yangjun Zhang

Prof. Dr. Yangjun Zhang


Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 October 2020 in IEEE Transactions on Vehicular Technology
Verified
Journal Article
Closed access
Yi XieYi Xie
Yi Xie
CWChenyang Wang
Chenyang Wang
Xiaosong HuXiaosong Hu
Xiaosong Hu
Xianke LinXianke Lin
Xianke Lin
YZYangjun Zhang
Yangjun Zhang
WLWei Li
Wei Li
In order to keep the lithium-ion battery within the optimal temperature range to achieve excellent battery performance and extend its lifespan, it is necessary to have an effective control strategy for a low-temperature battery thermal management system (BTMS) consisting of electric pump, cooling plate and radiator. In this paper, a control-oriented model for BTMS is established, and an intelligent model predictive control (IMPC) strategy is developed by integrating a neural network-based vehicle speed predictor (VSP) and a target battery temperature adaptor based on Pareto boundaries. The strategy developed based on the BTMS model is applied to plug-in electric vehicles operating in electric vehicle (EV) mode, and the results show its superiority in terms of battery temperature control, battery lifespan extension and energy saving. Under the new European driving cycle, the average difference between the real-time battery temperature under the novel IMPC and its target temperature is 0.26 °C, and the maximum temperature difference among modules is 1.03 °C. Moreover, compared with the on-off controller, model predictive control (MPC), and MPC with VSP, the state of health under IMPC at the end of the driving cycle is 0.016%, 0.012%, and 0.008% higher, respectively. At the end of the driving cycle, the energy consumption of IMPC is 24.5% and 14.1% lower than that of the on-off controller and traditional MPC, respectively.

16 July 2022 in International Journal of Energy Research
Verified
Journal Article
Closed access
YWYueqi Wang
Yueqi Wang
DDDan Dan
Dan Dan
YZYangjun Zhang
Yangjun Zhang
YQYuping Qian
Yuping Qian
Satyam PanchalSatyam Panchal
Satyam Panchal
Yi XieYi Xie
Yi Xie

17 June 2020 in IEEE Transactions on Transportation Electrification
Verified
Journal Article
Closed access
CDChaofan Dong
Chaofan Dong
YQYuping Qian
Yuping Qian
YZYangjun Zhang
Yangjun Zhang
WZWeilin Zhuge
Weilin Zhuge
More electric aircraft is a promising solution to the problems of fuel consumption, emissions, and noise faced by the aviation industry. Electrical machines with high power density are essential to improve the overall performance of more electric aircraft. The huge difference between the maximum and the rated output of electrical machines shows that the performance does not reach the present limit of the electromagnetic design. Improving thermal designs is an important and effective way to increase power density at the present stage, as the temperature rise limit of materials is the main obstacle to restricting the performance of electrical machines. In this article, a variety of thermal designs are comprehensively reviewed and critically discussed according to different cooling positions. Next, an analysis of suitable thermal designs for different applications of electrical machines in more electric aircraft is presented. Subsequently, cooling capacity and additional mass of the thermal designs, which are the two key determinants of improving power density, are estimated and compared. Moreover, the reliability and cost of the thermal designs are discussed in depth. This work contributes to a thorough understanding of thermal designs in improving the power density of electrical machines and guides their future designs and applications in more electric aircraft.

24 August 2022 in IEEE Transactions on Industrial Electronics
Verified
Journal Article
Closed access
Yi XieYi Xie
Yi Xie
WLWei Li
Wei Li
Xiaosong HuXiaosong Hu
Xiaosong Hu
Manh-Kien TranManh-Kien Tran
Manh-Kien Tran
Satyam PanchalSatyam Panchal
Satyam Panchal
KAILONG LIUKAILONG LIU
KAILONG LIU
Energy storage system based on batteries is a key to achieve a green industrial economy and the online estimation of its status is critical for the battery management system. Therefore, this article proposed a distributed spatial–temporal online correction algorithm for the state of charge (SOC) three-dimensional (3-D) state of temperature (SOT) coestimation of battery. First, the internal resistance is identified, and SOC is estimated based on the adaptive Kalman filter. Then, to improve the fidelity of electrical status estimation under the dynamic operation condition, the SOC estimation is coupled with an online restoration algorithm of distributed temperature. An improved fractal growth process is used to achieve the self-organization and convergence during the restoration of 3-D temperature distribution. Finally, to validate the fidelity of online coestimation algorithm for electrical and thermal parameters, dynamic current profiles are used. The coestimation method raises the fidelity of SOC estimation by 1.5% at most, compared with the SOC estimation algorithm without the SOT estimation. It also keeps the mean relative error of SOT estimation within 8%. Additionally, the robustness of the spatial–temporal online correction method with dual adaptive Kalman filters is validated. The result shows that the coestimation algorithm still has a good convergence performance with disturbance added.

27 June 2023 in IEEE Transactions on Transportation Electrification
Verified
Journal Article
Closed access
RYRui Yang
Rui Yang
Yi XieYi Xie
Yi Xie
KLKuining Li Li
Kuining Li Li
Manh-Kien TranManh-Kien Tran
Manh-Kien Tran
MFMichael Fowler
Michael Fowler
YZYangjun Zhang
Yangjun Zhang
Battery temperature greatly affects its electrical performance and safety. In this work, the thermal characteristics of a hybrid solid-liquid battery (referred to as solid-state battery) were systematically studied for the development of future battery thermal management systems (BTMS). The battery resistance characteristics were investigated by performing electrochemical impedance spectroscopy (EIS) measurements. Then, the extracted outcomes were connected with the heat generation and used to explain the trends of the battery thermal characteristics at different temperatures and current rates. Moreover, the resistance and thermal characteristics of the solid-state battery were compared to those of the traditional LiNiMnCoO2 (NMC) and LiFePO4 (LFP) batteries with similar capacity. According to the results, the solid-state battery has a bigger polarization resistance than the traditional batteries, because of the larger charge transfer impedance and impedance across the film evoked by the solid electrolyte. The higher resistance makes the solid-state battery generate more heat and achieve a higher temperature rise, and a BTMS with stronger cooling performance is required. Moreover, the battery temperature rise plateau of the solid-state battery is mainly affected by the reversible heat, and the polarization heat is the biggest contributor to the total heat generation in the solid-state battery.

11 March 2020 in IEEE Transactions on Transportation Electrification
Verified
Journal Article
Closed access
Yi XieYi Xie
Yi Xie
WLWei Li
Wei Li
Xiaosong HuXiaosong Hu
Xiaosong Hu
Xianke LinXianke Lin
Xianke Lin
YZYangjun Zhang
Yangjun Zhang
KLKexin Li
Kexin Li
This study presents an enhanced internal temperature estimation method for lithium-ion batteries using a one-dimensional model and a dual Kalman filter. The cylindrical battery cell is modeled by a one-dimensional thermal model with three nodes. This model provides a more accurate representation of the temperature distribution, resulting in more details of the temperature field. With the newly developed one-dimensional model, an enhanced temperature estimation method is developed by including the internal resistance identification and SOC estimation in the temperature estimation process. Experiments and simulations are conducted to evaluate the robustness and accuracy of the temperature estimation. The estimated temperature using the one-dimensional model with random initial values is compared with the surface temperature from experiments, which shows excellent robustness against random initial values. High estimation accuracy is demonstrated by the comparison between the estimated temperature field and the simulated temperature field from a high-fidelity three-dimensional model. Experimental results show that the dual Kalman filter method provides better stability than the single Kalman filter, and the accuracy of the internal temperature estimation is improved by the equivalent thermal conductivity identification which considers the anisotropy of thermal conductivity in different directions.