Jun Xu

Prof. Jun Xu

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
orcid
Professor and Ph.D. supervisor with School of Mechanical Eng...

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

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

Representative Publications

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1 September 2020 in Applied Thermal Engineering
Verified
Journal Article
Closed access
HWHaitao Wang
Haitao Wang
TTTao Tao
Tao Tao
JXJun Xu
Jun Xu
XMXuesong Mei
Xuesong Mei
XLXiaoyan Liu
Xiaoyan Liu
PGPiao Gou
Piao Gou

25 October 2013 in IEEE Transactions on Vehicular Technology
Verified
Journal Article
Closed access
JXJun Xu
Jun Xu
Chris MiChris Mi
Chris Mi
BCBinggang Cao
Binggang Cao
JDJunjun Deng
Junjun Deng
ZCZheng Chen
Zheng Chen
SLSiqi Li
Siqi Li
With the development of electric drive vehicles (EDVs), the state-of-charge (SOC) estimation for lithium-ion (Li-ion) batteries has become increasingly more important. Based on the analysis of some of the most popular model-based SOC estimation methods, the proportional-integral (PI) observer is proposed to estimate the SOC of lithium-ion batteries in EDVs. The structure of the proposed PI observer is analyzed, and the convergence of the estimation method with model errors is verified. To demonstrate the superiority and compensation properties of the proposed PI observer, the simple-structure RC battery model is utilized to model the Li-ion battery. To validate the results of the proposed PI-based SOC estimation method, the experimental battery test bench is established. In the validation, the urban dynamometer driving schedule (UDDS) drive cycle is utilized, and the PI-based SOC estimation results are found to agree with the reference SOC, generally within the 2% error band for both the known and unknown initial SOC cases.

5 November 2013 in IEEE Transactions on Vehicular Technology
Verified
Journal Article
Closed access
ZCZheng Chen
Zheng Chen
Chris MiChris Mi
Chris Mi
JXJun Xu
Jun Xu
XGXianzhi Gong
Xianzhi Gong
CYChenwen You
Chenwen You
This paper focuses on building an efficient, online, and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on a detailed powertrain analysis, the battery current can be optimized to improve the fuel economy using dynamic programming (DP). Three types of drive cycles, i.e., highway, urban, and urban (congested), are classified, and six typical drive cycles are analyzed and simulated to study all the driving conditions. The online intelligent energy management controller is built, which consists of two neural network (NN) modules that are trained based on the optimized results obtained by DP methods, considering the trip length and duration. Based on whether the trip length and duration are known or unknown, the controller will choose the corresponding NN module to output the effective battery current commands to realize the energy management. Numerical simulation shows that the proposed controller can improve the fuel economy of the vehicle.

1 May 2019 in Energy Storage Materials
Verified
Journal Article
Closed access
AJAjay Jagadale
Ajay Jagadale
XZXuan Zhou
Xuan Zhou
RXRui Xiong
Rui Xiong
Deepak DubalDeepak Dubal
Deepak Dubal
JXJun Xu
Jun Xu
SYSen Yang
Sen Yang

1 July 2013 in Journal of Power Sources
Verified
Journal Article
Closed access
JXJun Xu
Jun Xu
Chris MiChris Mi
Chris Mi
BCBinggang Cao
Binggang Cao
Junyi CaoJunyi Cao
Junyi Cao

1 October 2013 in Journal of Power Sources
Verified
Journal Article
Closed access
ZCZheng Chen
Zheng Chen
Chris MiChris Mi
Chris Mi
YFYuhong Fu
Yuhong Fu
JXJun Xu
Jun Xu
XGXianzhi Gong
Xianzhi Gong