Zhongliang Han

Dr. Zhongliang Han

China Automotive Technology and Research Center, Tianjin, China
orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

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

Representative Publications

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1 March 2018 in Applied Energy
Verified
Journal Article
Closed access
Zhongliang HanZhongliang Han
Zhongliang Han
NXNan Xu
Nan Xu
虹陈虹 陈
虹 陈
YHYanjun Huang
Yanjun Huang
BZBin Zhao
Bin Zhao

13 February 2026 in Actuators
Verified
Journal Article
Open access
PDF file
JYJiansen Yang
Jiansen Yang
Zhongliang HanZhongliang Han
Zhongliang Han
ZZZhiguo Zhang
Zhiguo Zhang
XWXuewei Wang
Xuewei Wang
FBFan Bai
Fan Bai
Yan WangYan Wang
Yan Wang
Distributed drive vehicles provide enhanced actuation flexibility, making longitudinal–lateral coordinated stability control essential for improving vehicle handling and safety under complex driving conditions. Nevertheless, the existing coordinated control strategies commonly employ stability reference models with fixed tire–road friction coefficients, which restrict their adaptability to time-varying adhesion environments. In addition, conventional sliding mode-based lateral stability controllers may exhibit limited performance when confronted with strong nonlinear coupling and external disturbances. To address these issues, this paper proposes an integrated longitudinal–lateral coordinated stability control framework for distributed drive vehicles. A dual unscented Kalman filter-based estimator is developed to identify the tire–road friction coefficients and construct a friction-adaptive reference model for yaw rate and sideslip angle. An adaptive fractional power speed controller with resistance compensation is designed to generate the total longitudinal driving torque, while an adaptive neural sliding mode controller produces the corrective yaw moment for lateral stability enhancement. Furthermore, a pseudoinverse-based torque distribution strategy is employed to allocate the longitudinal torque and yaw moment to individual wheels. Simulation results demonstrate that the proposed framework significantly improves vehicle stability and tracking accuracy compared with conventional control methods under varying road conditions.