Dr. Peng Mei
Dempartment of Mechanical Engineering, Politecnico di Milano, Milan, Lombardy, Italy
+1
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6Years (2021-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications11 April 2023 in Engineering Applications of Artificial Intelligence
Journal Article
Peng MeiHKHamid Reza Karimi
Hamid Reza Karimi
HXHehui Xie
Hehui Xie
FCFei Chen
Fei Chen
CHCong Huang
Cong Huang
SYShichun Yang
Shichun Yang
22 November 2021 in International Journal of Adaptive Control and Signal Processing
Journal Article
Peng MeiHKHamid Reza Karimi
Hamid Reza Karimi
SYShichun Yang
Shichun YangBXBin Xu
Bin Xu
CHCong Huang
Cong Huang
Summary: This article proposes a novel fuzzy sliding‐mode control scheme under an adaptive control strategy for energy management mechanism in electric vehicles that are subject to a regenerative braking system. The effectiveness of the fuzzy logic controller is to adjust the sliding mode parameters according to the slip ratio tracking error between the optimal slip ratio and the actual slip ratio. Specifically, the proposed torque distribution strategy can integrate the best battery condition and energy recovery efficiency under the practical constraints through this control method by fixing the pneumatic braking torque and motor torque. Finally, an electric vehicle model is established in the Simulink environment to verify the applicability of the proposed control algorithm.
30 April 2023 in IET Control Theory & Applications
Journal Article
Peng Mei
Hamid Reza KarimiCHCong Huang
Cong HuangFCFei Chen
Fei ChenSYShichun Yang
Shichun YangRemaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.
28 January 2025 in ISA Transactions
Journal Article
Peng MeiHamid Reza KarimiLOLei Ou
Lei Ou
HXHehui Xie
Hehui Xie
CZChong Zhan
Chong Zhan
SYShichun Yang
Shichun Yang +1
Advancements in intelligent vehicle technology have spurred extensive research into the impact of driving style (DS) on intelligent transportation systems (ITS), aiming to enhance vehicle safety, comfort, and energy efficiency. Accurate DS identification is pivotal for accelerating ITS adoption, especially in regions where its implementation is still in its infancy. This paper investigates the role of DS recognition methods, particularly clustering and classification techniques, in influencing connected vehicle control and optimizing speed planning within ITS. While traditional speed planning approaches focus on general traffic models, this study emphasizes the critical role of DS in shaping personalized and adaptive speed planning. The paper highlights three primary DS recognition approaches: rule-based, model-based, and learning-based methods, and introduces a framework for integrating DS recognition with speed planning, addressing aspects such as data collection, preprocessing, and classification techniques. This focus provides a novel perspective on leveraging DS recognition to enhance ITS adaptability.
17 November 2021 in IEEE Transactions on Vehicular Technology
Journal Article
CHCong Huang
Cong HuangSCSerdar Coskun
Serdar Coskun
JWJun Wang
Jun Wang
Peng MeiQSQuan Shi
Quan Shi
This paper is concerned with the problem of dynamic output feedback control design for the cooperative adaptive cruise control (CACC) system with randomly occurring sensor saturations (ROSSs) subject to randomly occurring deception attacks (RODAs). The dynamics of the vehicle equipped with CACC system behave like a linear model in which the deviation of the position and velocity are chosen as the state variables. Sensor saturations and deception attacks are simultaneously taken into account, and two sets of Bernoulli random variables are utilized to characterize their nature of random occurrence. The main objective of this article is to develop a robust dynamic output feedback controller (RDOFC) such that, for all possible parameter uncertainties, sensor saturations as well as deception attacks, all the states can still be exponentially mean square stable and the $H_{\infty }$ performance index is guaranteed. In light of the Lyapunov stability theory, a sufficient condition of the desired controller is firstly constructed, then the controller parameters are obtained relying on the solutions to a set of linear matrix inequalities (LMIs). Finally, the three-car platoon system is evaluated to validate the usefulness of the developed algorithm.
27 October 2025 in Reliability Engineering & System Safety
Journal Article
YYYue Yu
Yue YuHamid Reza Karimi
Len GelmanJTJinghui Tian
Jinghui Tian
Peng MeiAbout Me
Peng Mei currently holds a joint postdoctoral position at Politecnico di Milano and the University of Genoa. He earned his PhD in New Energy Vehicle Engineering from Beihang University in January 2024. From 2019 to 2021, he served as a Visiting Scholar in the Department of Mechanical Engineering at Politecnico di Milano in Milan, Italy. He has published around 20 papers in refereed international journals. His accolades include receiving the Best Paper Award at the 13th EAI International Conference on Sensor Systems and Software, the Excellent Oral Presentation Award at the 2022 2nd International Conference on Computers and Automation, and the Excellent Oral Presentation Award at the 2022 8th International Conference on Control, Automation and Robotics. He was also recognized as an Outstanding Reviewer by IET Control Theory & Applications in 2023. Dr. Mei contributes as a Youth Editorial Board Member for several international journals, such as Exploration, CHAIN, Journal of Geoenergy, and Journal of Chang'an University, etc. He also served as the Publicity Chair for the 4th International Conference on Computers and Automation. His research focuses on various aspects of energy management, vehicle control strategies, reinforcement learning, and battery modeling.
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