Toyota Research Institute, Inc.

Massachusetts Institute of Technology

Leland Stanford Junior University

Chirranjeevi Balaji Gopal

Patrick Kenichi Herring

Abraham S Anapolsky

Muratahan Aykol

William C Chueh

Richard Dean Braatz

William E Gent

Benben Jiang

Vivek Nath Lam

Xiao Cui

Patrick A Asinger

Bruis Van Vlijmen

Gabriel B Crane

Linnette Teo

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).

System and method for forecasting battery state with imperfect data

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.

Systems and methods for predicting battery life using data from a diagnostic cycle

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.

Vehicle battery analysis system

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.

System and method for controlling a battery management system

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San Jose, CA, United States of America

Chirranjeevi Balaji Gopal