The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 04, 2025

Filed:

Nov. 24, 2020
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Christoph Kroener, Rosstal, DE;

Felix Kleinheinz, Blaufelden, DE;

Marco Stroebel, Stuttgart, DE;

Peter Birke, Wallhausen, DE;

Assignee:

Robert Bosch GmbH, Stuttgart, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01R 31/382 (2019.01); G01R 31/367 (2019.01); G01R 31/374 (2019.01); G01R 31/389 (2019.01);
U.S. Cl.
CPC ...
G01R 31/382 (2019.01); G01R 31/367 (2019.01); G01R 31/374 (2019.01); G01R 31/389 (2019.01);
Abstract

The invention relates to a method for estimating the state of an energy store comprising at least one electrochemical battery cell () using a battery management system (BMS) which comprises an impedance spectroscopy chip, having at least the following steps: a) determining the frequency-dependent impedance of the at least one electrochemical battery cell () using a data set recording taken in real-time, b) training an artificial neural network () with temperature-based training spectra as the input and a specification for temperature values belonging to each training spectrum as the output, c) taking into consideration a battery cell-to-battery cell variance () between the electrochemical battery cells () when testing the artificial neural network () using weighting functions ascertained during step b) and test spectra and estimating the temperature values belonging to the test spectra according to the weighting functions ascertained in step b), and d) estimating at least one internal state (SoC, SoH, T) of the at least one electrochemical battery cell () of the energy store using the trained artificial neural network ().


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