R
Richard D. Braatz
Researcher at Massachusetts Institute of Technology
Publications - 585
Citations - 25753
Richard D. Braatz is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Model predictive control & Robust control. The author has an hindex of 71, co-authored 551 publications receiving 21561 citations. Previous affiliations of Richard D. Braatz include California Institute of Technology & University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Fault Detection and Diagnosis in Industrial Systems
TL;DR: The appearance of this book is quite timely as it provides a much needed state-of-the-art exposition on fault detection and diagnosis, a topic of much interest to industrialists.
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On the "Identification and control of dynamical systems using neural networks"
E. Rios-Patron,Richard D. Braatz +1 more
TL;DR: Referring to the above said paper by Narendra-Parthasarathy (ibid.
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Data-driven prediction of battery cycle life before capacity degradation
Kristen A. Severson,Peter M. Attia,Norman Jin,Nicholas Perkins,Benben Jiang,Zi Yang,Michael H. Chen,Muratahan Aykol,Patrick Herring,Dimitrios Fraggedakis,Martin Z. Bazant,Stephen J. Harris,Stephen J. Harris,William C. Chueh,Richard D. Braatz +14 more
TL;DR: In this article, a machine learning method was used to predict battery lifetime before the onset of capacity degradation with high accuracy. But, the prediction often cannot be made unless a battery has already degraded significantly.
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A tutorial on linear and bilinear matrix inequalities
TL;DR: In this article, a tutorial on the mathematical theory and process control applications of linear matrix inequalities and bilinear matrix inequalities (BMIs) is presented, and a software for solving LMI and BMI problems is reviewed.
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Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective
Venkatasailanathan Ramadesigan,Paul W. C. Northrop,Sumitava De,Shriram Santhanagopalan,Richard D. Braatz,Venkat R. Subramanian +5 more
TL;DR: In this paper, a review of the modeling and simulation of lithium-ion batteries and their use in the design of better batteries is presented and likely future directions in battery modeling and design including promising research opportunities are outlined.