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Electronics and Electrical Engineering
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Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
106
Citations
53478
World Ranking
42
National Ranking
23

Electronics and Electrical Engineering

D-Index
106
Citations
52985
World Ranking
127
National Ranking
63

Research.com Recognitions

  • 2026 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 1995 - Fellow of the American Society of Mechanical Engineers
  • 1992 - IEEE Fellow For contributions to and leadership in electronics packaging and reliability.

Overview

Michael Pecht is affiliated with the University of Maryland, College Park in the United States. Their research primarily focuses on engineering with a total of 469 publications in this field. Within engineering, their work spans several subfields including Electrical and Electronic Engineering, Automotive Engineering, Control and Systems Engineering, Mechanical Engineering, and Safety, Risk, Reliability and Quality.

The main topics of Michael Pecht's research encompass advanced battery technologies and materials, machine fault diagnosis, and reliability and maintenance optimization. Specifically, their work covers:

  • Advanced Battery Technologies Research
  • Advancements in Battery Materials
  • Machine Fault Diagnosis Techniques
  • Reliability and Maintenance Optimization
  • Advanced Battery Materials and Technologies
  • Fault Detection and Control Systems
  • Electric Vehicles and Infrastructure

Michael Pecht has published extensively in notable venues, frequently contributing to:

  • IEEE Access
  • Journal of Energy Storage
  • SSRN Electronic Journal
  • Renewable and Sustainable Energy Reviews
  • Journal of Power Sources

Some of their recent publications include:

  • Battery Lifetime Prognostics, 2020, Joule
  • Machine Learning Pipeline for Battery State-of-Health Estimation, 2021, Nature Machine Intelligence
  • Advanced Battery Management Strategies for a Sustainable Energy Future: Multilayer Design Concepts and Research Trends, 2020, Renewable and Sustainable Energy Reviews
  • Mitigation Strategies for Li-ion Battery Thermal Runaway: A Review, 2021, Renewable and Sustainable Energy Reviews
  • Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis, 2020, IEEE Transactions on Industrial Electronics

The scientist has collaborated regularly with several co-authors, including Michael H. Azarian, Jiefei Gu, Lingxi Kong, Lei Su, and Daoming She, reflecting a collaborative approach across their research projects.

Michael Pecht holds distinctions such as being named a Fellow of the American Society of Mechanical Engineers in 1995 and an IEEE Fellow in 1992 for contributions to and leadership in electronics packaging and reliability.

Best Publications

  • Deep Residual Shrinkage Networks for Fault Diagnosis

    Minghang Zhao;Shisheng Zhong;Xuyun Fu;Baoping Tang

  • Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

    Yongzhi Zhang;Rui Xiong;Hongwen He;Michael G. Pecht

  • Battery Lifetime Prognostics

    Xiaosong Hu;Le Xu;Xianke Lin;Michael Pecht

  • Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method

    Wei He;Nicholas Williard;Michael Osterman;Michael Pecht

  • State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures

    Yinjiao Xing;Wei He;Michael Pecht;Kwok Leung Tsui

  • Prognostics and Health Management of Electronics

    Michael G. Pecht

  • Light emitting diodes reliability review

    Moon-Hwan Chang;Diganta Das;Prabhakar V. Varde;Prabhakar V. Varde;Michael G. Pecht;Michael G. Pecht

  • Prognostics and health management of electronics

    N.M. Vichare;M.G. Pecht

  • Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process

    Xiao-Sheng Si;Wenbin Wang;Chang-Hua Hu;Dong-Hua Zhou

  • An ensemble model for predicting the remaining useful performance of lithium-ion batteries

    Yinjiao Xing;Eden W. M. Ma;Kwok-Leung Tsui;Michael G. Pecht

  • Effect of Temperature on the Aging rate of Li Ion Battery Operating above Room Temperature

    Feng Leng;Cher Ming Tan;Michael Pecht

  • Battery Management Systems in Electric and Hybrid Vehicles

    Yinjiao Xing;Eden W. M. Ma;Kwok L. Tsui;Michael Pecht

  • State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

    Wei He;Nicholas Williard;Chaochao Chen;Michael Pecht

  • Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

    Fangdan Zheng;Fangdan Zheng;Yinjiao Xing;Jiuchun Jiang;Bingxiang Sun

  • A review of lead-free solders for electronics applications

    Shunfeng Cheng;Chien-Ming Huang;Michael G. Pecht

  • Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

    Qiang Miao;Lei Xie;Hengjuan Cui;Wei Liang

  • Machine learning pipeline for battery state-of-health estimation

    Darius Roman;Saurabh Saxena;Saurabh Saxena;Valentin Robu;Valentin Robu;Michael G. Pecht

  • Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K -Nearest Neighbor Distance Analysis

    Jing Tian;Carlos Morillo;Michael H. Azarian;Michael Pecht

  • A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

    Changfu Zou;Lei Zhang;Xiaosong Hu;Zhenpo Wang

  • Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression

    Datong Liu;Jingyue Pang;Jianbao Zhou;Yu Peng

  • Physics-of-Failure: An Approach to Reliable Product Development

    Michael Pecht;Abhijit Dasgupta

  • Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis

    Xiaohang Jin;Mingbo Zhao;Tommy W. S. Chow;Michael Pecht

  • A Prognostics and Health Management Roadmap for Information and Electronics-Rich Systems

    Michael G. Pecht

  • State of charge estimation for electric vehicle batteries using unscented kalman filtering

    Wei He;Nicholas Williard;Chaochao Chen;Michael G. Pecht

Frequent Co-Authors

Abhijit Dasgupta
Abhijit Dasgupta University of Maryland, College Park
Kam-Chuen Yung
Kam-Chuen Yung Hong Kong Polytechnic University
Wenbin Wang
Wenbin Wang University of Science and Technology Beijing
Gang Niu
Gang Niu Xi'an Jiaotong University
Kwok-Leung Tsui
Kwok-Leung Tsui Virginia Tech
Rui Xiong
Rui Xiong Beijing Institute of Technology
Guoqi Zhang
Guoqi Zhang Delft University of Technology
Hongwen He
Hongwen He Beijing Institute of Technology
Zhaowei Zhong
Zhaowei Zhong Nanyang Technological University
Jiuchun Jiang
Jiuchun Jiang Hubei University of Technology

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