On-line estimation method of battery state of health in wide temperature range based on “standardized temperature”

Patent metadata

Field	Value
Publication number	US-11442113-B2
Application number	US-202117603599-A
Country	US
Kind code	B2
Filing date	Jan 5, 2021
Priority date	May 28, 2020
Publication date	Sep 13, 2022
Grant date	Sep 13, 2022

Abstract

An on-line State of Health estimation method of a battery in a wide temperature range based on “standardized temperature” includes: calculating battery Incremental Capacity curve of a battery, establishing a quantitative relationship between the voltage shift of the temperature-sensitive feature point and the temperature of a standard battery, standardized transformation of Incremental Capacity curves at different temperatures, establishing a quantitative relationship between the transformed height of the capacity-sensitive feature point and the State of Health based on a BOX-COX transformation. The BOX-COX transformation is expressed asyk(λ)={ykλ-1λλ≠0ln⁢⁢ykλ=0.An maximum likelihood function is used to calculate the optimal λ, and the transformed height of the capacity-sensitive feature point y can be acquired. The quantitative relationship between transformed height of the capacity-sensitive feature point and the State of Health is established to obtain the State of Health.

First claim

What is claimed is:

1. An on-line State of Health estimation method for a battery in a wide temperature range comprising the following steps:
S1. generating a plurality of incremental capacity curves for a battery based on a plurality of charging curves for the battery, wherein each of the charging curves for the battery is obtained by holding the battery for 2 hours at fixed ambient temperatures of −5° C. 0° C. 5° C. 10° C. 15° C. 20° C. 25° C. 30° C. 35° C. 40° C. 45° C. 50° C. 55° C. and then charging each battery with 0.1 C to obtain the charging curve at each of the fixed ambient temperatures for the battery;
S2. selecting a temperature-sensitive feature point for each of the incremental capacity curves of the battery at different fixed ambient temperatures, wherein the temperature-sensitive feature point is a second inflection point of the incremental capacity curve, and determining a quantitative relationship between g voltage shift of the temperature-sensitive feature point and each fixed ambient temperature other than 25° C. based on the temperature of the battery at 25° C. by subtracting a voltage at the temperature-sensitive feature point for the battery at 25° C. from a voltage of the temperature-sensitive feature point of the battery at a temperature other than 25° C. and applying an Arrhenius fitting function to determine the quantitative relationship from (1):

y
=

a
⁢

⁢

exp
⁡

(

b
T

)

+
c

(
1
)

wherein a, b, c are fitting parameters, T is a temperature, and y represents a voltage shift of temperature-sensitive feature point;
S3. performing a standardized transformation of the incremental capacity curves at different temperatures; and
S4. establishing a quantitative relationship between a transformed height of a capacity-sensitive feature point and a State of Health of the battery based on a BOX-COX transformation.

2. The on-line State of Health estimation method of the battery in the wide temperature range according to claim 1 , wherein the standardized transformation of the incremental capacity curves at different temperatures in S3 comprises obtaining a charging Q-V curve for each battery at the fixed ambient temperature greater than 30° C., shifting the charging Q-V curve for each battery at the fixed ambient temperature greater than 30° C. by a voltage shift to obtain a normalized charging Q-V curve for each battery at the fixed ambient temperature greater than 30° C. and obtaining a normalized incremental capacity curve based on the fixed ambient temperature greater than 25° C. through a numerical differentiation method.

3. The on-line State of Health estimation method of the battery in the wide temperature range according to claim 2 , wherein a corresponding voltage shift of the charging Q-V curve is achieved by bringing the temperature into the Arrhenius fitting function.

4. The on-line State of Health estimation method of the battery in the wide temperature range according to claim 1 , wherein the selection of the capacity-sensitive feature point in S4 is based on
the height of the second inflection point.

5. The on-line State of Health estimation method of the battery in the wide temperature range according to claim 4 , wherein based on the capacity-sensitive feature point,
the Box-Cox transformation increases linearity between the height of the second inflection point as the capacity-sensitive feature point and the State of Health
using a linear regression equation is expressed as (2):

Y
=

X
⁢

⁢
β

+
ɛ

(
2
)

wherein Y is a dependent variable, X is an independent variable, β is a coefficient matrix, and ε a represents a fitting error;
the Box-Cox transformation is represented as:

y
k

(
λ
)

=

{

y
k
λ

-
1

λ

λ
≠
0

ln
⁢

⁢

y
k

λ
=
0

(
3
)

wherein y in the right side of the equation is an original variable, and the subscript k corresponding toy represents a k-th variable, λ is a conversion parameter, y k (λ) in the left side of the equation is a k-th conversion variable.

Assignees

• Univ Jiangsu

Inventors

• Wang Limei
• Lu Dong
• Zhao Xiuliang
• Qiao Sibing
• Xu Ying
• Pan Chaofeng

Classifications

• G01R31/3648

  comprising digital calculation means, e.g. for performing an algorithm · CPC title

• G01R31/392 Primary

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

• G01R31/367 Primary

  Software therefor, e.g. for battery testing using modelling or look-up tables · CPC title