Data-driven battery aging model using statistical analysis and artificial intelligence

Patent metadata

Field	Value
Publication number	US-2016239592-A1
Application number	US-201615015377-A
Country	US
Kind code	A1
Filing date	Feb 4, 2016
Priority date	Feb 12, 2015
Publication date	Aug 18, 2016
Grant date	—

Abstract

A method and system are provided. The method includes determining, by a processor, a set of battery aging modeling parameters that include battery capacity for a battery based on a statistical analysis applied to experiment data. The experiment data is obtained from measurements of a set of battery parameters that include battery capacity and that are taken by a hardware-based battery parameter monitoring device during a plurality of experiments which vary another set of battery parameters. The set and the other set have at least some different members. The method further includes generating, by the processor, a battery aging neural network based model for the battery that includes the set of battery aging modeling parameters. The method also includes storing the battery aging neural network based model in a memory device.

First claim

What is claimed is:

1 . A method, comprising:
determining, by a processor, a set of battery aging modeling parameters that include battery capacity for a battery based on a statistical process applied to experiment data, the experiment data obtained from measurements of a set of battery parameters that include battery capacity and that are taken by a hardware-based battery parameter monitoring device during a plurality of experiments which vary another set of battery parameters, the set and the other set having at least some different members; generating, by the processor, a battery aging neural network based model for the battery that includes the set of battery aging modeling parameters; and storing the battery aging neural network based model in a memory device.

2 . The method of claim 1 , wherein the set of battery parameters further include at least one of an internal resistance, a terminal voltage, state of health, and an internal temperature.

3 . The method of claim 1 , wherein the battery aging neural network based model is a battery cycling degradation model, and the other set of battery parameters comprise at least two of an ambient temperature, a charging rate, a discharging rate, a minimum state of charge, a maximum state of charge, previous estimated battery capacity, and an energy storage or extraction throughput.

4 . The method of claim 1 , wherein the battery aging neural network based model is a calendar aging degradation model, and the other set of battery parameters comprise at least two of a state of charge of the battery at the beginning of an idle situation), an ambient temperature, an accumulative shelf time, and a previous estimated battery capacity.

5 . The method of claim 1 , wherein the statistical process uses a statistical analysis applied to the experiment data to determine the set of battery aging modeling parameters based on statistical significance.

6 . The method of claim 5 , wherein the statistical significance is based on different interactions between the battery parameters in at least one of the set and the other set.

7 . The method of claim 1 , wherein the statistical process comprises applying single and multiple regressions with a least squares process to the experiment data.

8 . The method of claim 7 , wherein Ridge and Lasso regressions are used to verify results obtained from the least squares process.

9 . The method of claim 7 , wherein the statistical process uses k-fold cross-validation to determine a test error and select the set of battery aging model parameters.

10 . The method of claim 1 , further comprising setting or changing a charging/discharging profile for the battery based on the battery aging neural network based model.

11 . The method of claim 1 , further comprising initiating a switching, using one or more hardware based switches, from the battery to another battery based on a battery aging related prediction derived from the battery aging neural network based model.

12 . The method of claim 1 , further comprising:
re-sampling the experiment data obtained from each of the plurality of experiments using a fixed interval length to obtain re-sampled experiment data; and training the battery aging neural network based model using the re-sampled experiment data, wherein the fixed interval length is determined as a maximum one of respective minimum intervals for the plurality of experiments.

13 . The method of claim 1 , further comprising:
performing a unification process on the experiment data using a fixed end of data to obtain unified experiment data; and training the battery aging neural network based model using the unified experiment data, wherein the fixed end of data is determined as a minimum one of respective maximum data throughputs for the plurality of experiments.

14 . The method of claim 1 , further comprising:
performing a data division operation on the experiment data to divide the experiment data into a training category, a validation category, and a testing category; and training the battery aging neural network based model using the experiment data categorized into each of the training category, the validation category, and the testing category, wherein the data division operation categories more of the experiment data into the training category than the validation category and the testing category.

15 . The method of claim 1 , further comprising:
performing an sensitivity analysis on the battery aging neural network based model using different numbers of layers and different numbers of neurons; and adjusting the battery aging neural network based model based on results of the sensitivity analysis.

16 . A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim 1 .

17 . A battery management system, comprising:
a processor for determining a set of battery aging modeling parameters that include battery capacity for a battery based on a statistical process applied to experiment data, and generating a battery aging neural network based model for the battery that includes the set of battery aging modeling parameters; and a memory for storing the set of battery aging modeling parameters, wherein the experiment data is obtained from measurements of a set of battery parameters that include battery capacity and that are taken by a hardware-based battery parameter monitoring device during a plurality of experiments which vary another set of battery parameters, the set and the other set having at least some different members.

18 . The battery management system of claim 17 , wherein the statistical process uses a statistical analysis applied to the experiment data to determine the set of battery aging modeling parameters based on statistical significance.

19 . The battery management system of claim 18 , wherein the statistical significance is based on different interactions between the battery parameters in at least one of the set and the other set.

20 . The battery management system of claim 17 , further comprising setting or changing a charging/discharging profile for the battery based on the battery aging neural network based model.

Assignees

• Nec Lab America Inc

Inventors

• Pourmousavi Kani Seyyed Ali

Classifications

• B60L58/16 Primary

  responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH] · CPC title

• G06N3/02

  Neural networks · CPC title

• B60L2240/545

  Temperature · CPC title

• B60L58/12

  responding to state of charge [SoC] · CPC title

• H01M2010/4278

  Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller · CPC title