Data-driven model for lithium-ion battery capacity fade and lifetime prediction

Patent metadata

Field	Value
Publication number	US-11226374-B2
Application number	US-201816161852-A
Country	US
Kind code	B2
Filing date	Oct 16, 2018
Priority date	Oct 17, 2017
Publication date	Jan 18, 2022
Grant date	Jan 18, 2022

Abstract

A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ΔQ(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

First claim

What is claimed:

1. A method of collecting battery data suitable for use in data-driven predictive modeling to predict battery cell performance, the method comprising:
a) collecting a dataset by cycling, using a battery cycling instrument in a laboratory, a plurality of battery cells between a voltage V1 and a voltage V2;
b) continuously measuring one or more battery cell physical properties during said cycling;
c) measuring a charge-voltage curve Q(V) for each said battery cell during said cycling;
d) calculating, using data from said charge-voltage curves, a cycle-to-cycle evolution curve ΔQ(V) of a battery cell charge, wherein ΔQ(V) is a difference between charge-voltage curves Q(V) at two different cycles;
e) automatically calculating one or more summary statistics of said ΔQ(V); and
f) outputting the summary statistics.

2. The method according to claim 1 , wherein said one or more battery cell physical properties are selected from the group consisting of: a battery cell voltage, a battery cell current, a battery cell can temperature, and a battery cell internal resistance.

3. The method according to claim 1 , wherein said continuously measuring further comprises impedance spectroscopy and strain measurement.

4. A method of predicting one or more battery cell operation characteristics from battery cell cycling data, said method comprising:
performing the method of claim 1 to collect said summary statistics of said ΔQ(V);
training a machine learning model to relate one or more battery cell operation characteristics to said summary statistics of said ΔQ(V); and
using the machine learning model, after said training, to predict one or more battery cell operation characteristics including battery lifetime from battery cell cycling data.

5. The method according to claim 4 , wherein said battery cell operation characteristics are selected from the group consisting of: a battery lifetime, a logarithm of said battery lifetime, and a Boolean classification of battery performance.

6. The method according to claim 1 , wherein said one or more battery cell physical properties include a battery cell internal resistance of each said battery cell.

7. The method of claim 1 , wherein said one or more summary statistics are selected from the group consisting of: minimum, mean, variance, skewness and kurtosis.

Assignees

• Univ Leland Stanford Junior
• Massachusetts Inst Technology

Inventors

• Severson Kristen Ann
• Braatz Richard Dean
• Chueh William C
• Attia Peter M
• Jin Norman
• Harris Stephen J
• Perkins Nicholas

Classifications

• H01M10/48

  Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte (constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569) · CPC title

• G01R31/396

  Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery · CPC title

• G01R31/392 Primary

  Determining battery ageing or deterioration, e.g. state of health · CPC title

• G01R31/389

  Measuring internal impedance, internal conductance or related variables · CPC title

• G01R31/3842 Primary

  combining voltage and current measurements · CPC title