Patents by Inventor Chirranjeevi Balaji Gopal
Chirranjeevi Balaji Gopal has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11768249Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.Type: GrantFiled: March 31, 2021Date of Patent: September 26, 2023Assignees: Toyota Research Institute, Inc., Massachusetts Institute of Technology, The Board of Trustees of the Leland Stanford Junior UniversityInventors: William C. Chueh, Bruis van Vlijmen, William E. Gent, Vivek Lam, Patrick K. Herring, Chirranjeevi Balaji Gopal, Patrick A. Asinger, Benben Jiang, Richard Dean Braatz, Xiao Cui, Gabriel B. Crane
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Publication number: 20230261188Abstract: A method includes accessing one or more models of battery cathode synthesis, battery cell prototyping, battery cell testing, or a combination thereof. The method also includes applying the one or more models for controlling one or more steps in a battery production workflow.Type: ApplicationFiled: February 15, 2023Publication date: August 17, 2023Applicant: Mitra Future Technologies, Inc.Inventors: Xiaofei YE, Chirranjeevi Balaji GOPAL, William CHUEH, Prateek MEHTA
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Patent number: 11555859Abstract: In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.Type: GrantFiled: September 10, 2020Date of Patent: January 17, 2023Assignee: Toyota Research Institute, Inc.Inventors: Muratahan Aykol, Chirranjeevi Balaji Gopal, Patrick K. Herring, Abraham S. Anapolsky
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Patent number: 11529887Abstract: A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.Type: GrantFiled: January 24, 2020Date of Patent: December 20, 2022Assignee: Toyota Research Institute, Inc.Inventors: Patrick K. Herring, Chirranjeevi Balaji Gopal, Abraham S. Anapolsky
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Publication number: 20220284747Abstract: An approach to forecasting battery health as a dynamic time-series problem as opposed to a static prediction problem is presented. Systems and methods disclosed herein forecast a trajectory to failure by predicting a path to failure as opposed to only predicting when the battery may fail. A machine-learning model is implemented that extracts unique features taken from time-series data, such as time snippets of charging data. The raw time-series data may include current voltage and temperature with complex transformations and without capturing a full cycle, which permits wider applicability to instances of varying depth of discharge (DoD).Type: ApplicationFiled: March 8, 2021Publication date: September 8, 2022Applicant: TOYOTA RESEARCH INSTITUTE, INC.Inventors: Linnette TEO, Chirranjeevi BALAJI GOPAL
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Publication number: 20220137149Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.Type: ApplicationFiled: March 31, 2021Publication date: May 5, 2022Applicants: Toyota Research Institute, Inc., The Board of Trustees of the Leland Stanford Junior University, Massachusetts Institute of TechnologyInventors: William C. Chueh, Bruis van Vlijmen, William E. Gent, Vivek Lam, Patrick K. Herring, Chirranjeevi Balaji Gopal, Patrick A. Asinger, Benben Jiang, Richard Dean Braatz, Xiao Cui, Gabriel B. Crane
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Publication number: 20220074993Abstract: In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.Type: ApplicationFiled: September 10, 2020Publication date: March 10, 2022Inventors: Muratahan Aykol, Chirranjeevi Balaji Gopal, Patrick K. Herring, Abraham S. Anapolsky
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Publication number: 20210406461Abstract: A method performed by a computing device includes generating a template for receiving data based on a type of a test conducted in a testing environment. The method also includes receiving data input to the computing device based on the template. The method further includes parsing the received data to identify data corresponding to a sample-based provenance and a time-based provenance. The method still further includes updating at least one of the time-based provenance and the sample-based provenance based on the identified data. The method also includes generating an inference at a machine learning model based on at least one of the time-based provenance and the sample-based provenance, and updating the template based on the inference.Type: ApplicationFiled: June 30, 2020Publication date: December 30, 2021Applicant: TOYOTA RESEARCH INSTITUTE, INC.Inventors: Ha-Kyung KWON, Chirranjeevi BALAJI GOPAL
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Publication number: 20210229568Abstract: A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.Type: ApplicationFiled: January 24, 2020Publication date: July 29, 2021Inventors: Patrick K. Herring, Chirranjeevi Balaji Gopal, Abraham S. Anapolsky