IP Library › Granted Patent US 11,408,942
Granted Patent B2
US 11,408,942 · App. 17/546,988 · Granted Aug 9, 2022

Method for predicting service life of retired power battery

Inventor: Zhiqiang Liu (Hunan, CN)
Assignee: CHANGSHA UNIVERSITY OF SCIENCE & TECHNOLOGY
G01R31/392G01R31/367G01R31/374G06F30/20H02J7/005
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Quick Facts
Patent No.
US 11,408,942
App. No.
17/546,988
Granted
Aug 9, 2022
Kind
B2
Abstract

This invention discloses a method for predicting the service life of a retired power battery. The service life of a retired power battery may be predicted by its power battery life attenuation curve. The power battery life attenuation curve is obtained by establishing a power battery life model and a charge and discharge characteristic curve of the power battery by utilizing the temperature T, the discharge rate C and the discharge depth DOD in the charging and discharging process of the power battery. This invention establishes a three-dimensional relation graph with a cycle life with respect to the capacity loss rate and the functional relationship ω=ƒ(T,C) by using the power battery life attenuation curve. The three-dimensional relation graph is applied to the same type of battery. And the attenuation of the battery in the full life cycle may be predicted.

Claims (23)

1. A method for predicting a life of a retired power battery, comprising steps of:

Step 1, arranging power batteries as an experimental group, performing charge and discharge cycles at different discharge rates and different temperatures respectively, recording a life cycle experiment value and remaining capacity of a power battery after the charge and discharge cycles, and drawing a discharge characteristic curve under each working condition of the power battery;

Step 2, calculating capacity loss rate of the power battery in the experimental group under the life cycle and the working conditions, based on the remaining capacity of the power battery;

Step 3, generating a life attenuation curve under each working condition based on the life cycle experiment value recorded in Step 1 and the capacity loss rate obtained in Step 2;

Step 4, establishing a battery life prediction model based on factors influencing a service life of the power battery, and fitting the battery life prediction model with the life attenuation curve;

Step 5, acquiring the life cycle corresponding to the capacity loss rate under each working condition based on the fitted curve in Step 4;

Step 6, generating a three-dimensional graph based on a product of the discharge rate and a temperature, the capacity loss rate and the life cycle;

Step 7, determining a remaining service life of a same-type power battery as follows:

calculating the capacity loss rate and the product of the discharge rate and the temperature,

substituting the product into the three-dimensional graph to obtain the life cycle, and then solving a difference between the life cycle and a nominal life to obtain the remaining life,

determining a timeline to utilize the retired power battery while still satisfying battery performance and further determining the timeline to replace the retired power battery with another power battery once the remaining life of the retired power battery ends, thereby improving efficiency of utilizing the retired power battery and enhancing battery performance with the another power battery for new energy vehicles,

wherein the process for establishing the battery life prediction model in Step 4 is as follows:

S1, considering factors that affect the life cycle of the response power battery, including the temperature T, the discharge rate C and a discharge depth DOD;

S2, determining that the discharge depth DOD is 100%, and establishing the battery life prediction model: Q loss =ƒ(T,C,t);

S3, based on characteristics of a power battery life cycle attenuation rule conforming to a power function, further establishing the battery life prediction model: Q loss =x μ +d, wherein the d is a constant of temperature and x is the power function;

S4, based on the influence of temperature, further establishing the battery life prediction model: Q loss =B•e (K a K T K C +b) •Q α +d, wherein the B is a coefficient, K a is a function related to an activation energy, K T K C is the function Q loss which is affected by the temperature T and the discharge rate C, α is a power function factor, Q represents the discharge capacity Q=n•C where n is a number of discharge cycles represents the temperature and b is a constant;

S5, based on a value of the power function factor α and the activation energy a, α=0.287, a=3116+170•C, and determining the battery life prediction model:

Q loss =B•e (−lg(3116−170•C))•T•C (n•C) 0.287 +c, wherein the c is the constant.

2. The method of claim 1 , wherein the solving formula of capacity loss rate in Step 2 is Q loss real =ΔQ/Q τ ×100%, Q loss real is the capacity loss rate, ΔQ is the capacity attenuation amount of the battery, and Q τ is rated capacity of the power battery; and the capacity attenuation amount of the battery is obtained by the formula: ΔQ=Q τ −Q n , and Q n is the remaining capacity of the battery after the cycle of n times.

3. The method of claim 1 , wherein the establishing of the three-dimensional graph in Step 6 comprising the steps of:

drawing a three-dimensional scatter diagram, the three-dimensional scatter diagram is about the product of the discharge rate and the temperature, the capacity loss rate and the life cycle; and

fitting the three-dimensional scatter diagram into the three-dimensional graph by using interpolation.

4. The method of claim 1 , wherein the calculation formula of the remaining life in Step 7 is as follows: N remain =N nominal −N, the N nominal is the nominal life, and the N represents the life cycle.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 9, 2021
From: LIU, ZHIQIANG
To: CHANGSHA UNIVERSITY OF SCIENCE & TECHNOLOGY
Reel/Frame 058352/0705 →
Priority Claims (1)
CN 201910914866.6 · Sep 26, 2019 · national
Continuity (2)
Continuation PCTCN2020115519 · Sep 16, 2020
Related Publication 20220099753A1 · Mar 31, 2022
Cited By (1)
US 12,565,119