Modeling a change in battery degradation

• US 10,044,212 B2
• Filed: 05/10/2017
• Issued: 08/07/2018
• Est. Priority Date: 08/20/2015
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group), Kia Corp.
• Original Assignee: International Business Machines Corporation
• Inventors: Katsuki, Takayuki, Morimura, Tetsuro
• Primary Examiner(s): Memula, Suresh
• Application Number:
  US15/591,142
• Publication Number:
  US 20170244256A1
• Time in Patent Office: 454 Days
• Field of Search: 320134
• CPC Class Codes: G01R 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: H02J 7/0068 Battery or charger load switching, e.g. concurrent charging and load supply H02J7/0013 takes precedence
• CPC Class Codes: H02J 7/0069 Charging or discharging for charge maintenance, battery initiation or rejuvenation

Abstract

A battery controller and method for controlling a battery include training parameters for a battery capacity prediction model based usage of similar batteries and capacity information for the respective similar batteries. The model characterizes a capacity decay rate. Future battery capacity is predicted for a battery under control based on the battery capacity prediction model and a present value of the battery capacity. One or more operational parameters of the battery under control is controlled based on the predicted future battery capacity.

Claims

1. A computer-implemented method for controlling a battery, comprising:

training parameters for a battery capacity prediction model based usage of similar batteries and capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate;

predicting future battery capacity for a battery under control based on the battery capacity prediction model and a present value of the battery capacity; and

controlling one or more operational parameters of the battery under control based on the predicted future battery capacity.

2. The method of claim 1, wherein a usage pattern comprises a timestamp, an electric current, a voltage, and a temperature.

3. The method of claim 2, wherein the usage pattern further comprises a state of charge or difference in state of charge.

4. The method of claim 1, wherein the battery capacity prediction model comprises an expression for a current battery capacity y at a time t:

y≡

−

at<sup>b(y,θ

)</sup>+c

wherein a is a gradient of a capacity degradation curve, c is an initial capacity retention rate, and b (y, θ

) is a function that depends on a current battery capacity y and a trained parameter θ

.

5. The method of claim 4, wherein the function b(y, θ

) has functional form b=θ

₀+θ

₁y , where θ

₀ and θ

₁ are learned parameters.

6. The method of claim 4, wherein training parameters comprises learning the parameter a with a trajectory regression.

7. The method of claim 1, wherein the battery capacity prediction model comprises an expression for a current battery capacity y at a time t:

y_t≅

−

at<sup>b(yt−

Δ

t,θ

)</sup>+c

wherein a is a gradient of a capacity degradation curve, c is an initial capacity retention rate, Δ

t is a small elapsed time, and b(y<sub>t−

Δ

t</sub>, θ

) is a function that depends on a previous battery capacity y and a trained parameter θ

.

8. The method of claim 1, wherein the model accounts for an initial increase in battery capacity over time followed by a decline in battery capacity that is proportional to a square root of time elapsed.

9. The method of claim 1, wherein controlling one or more operational parameters of the battery comprises setting a maximum or minimum terminal voltage in accordance with usage patterns and the prediction.

10. The method of claim 1, wherein controlling one or more operational parameters of the battery comprises setting a charging speed of the battery in accordance with usage patterns and the prediction.

11. A computer readable storage medium comprising a computer readable program for controlling a battery, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:

training parameters for a battery capacity prediction model based usage of similar batteries and capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate;

predicting future battery capacity for a battery under control based on the battery capacity prediction model and a present value of the battery capacity; and

controlling one or more operational parameters of the battery under control based on the predicted future battery capacity.

12. A battery controller, comprising:

a processor configured to train parameters for a battery capacity prediction model based on usage of similar batteries and capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate, and further configured to predict future battery capacity for a battery under control based on the battery capacity prediction model and a present value of the battery capacity; and

a battery interface configured to control one or more operational parameters of the battery under control based on the predicted future battery capacity.

13. The battery controller of claim 12, wherein a usage pattern comprises a timestamp, an electric current, a voltage, and a temperature.

14. The battery controller of claim 13, wherein the usage pattern further comprises a state of charge or difference in state of charge.

15. The battery controller of claim 12, wherein the battery capacity prediction model comprises an expression for a current battery capacity y at a time t:

i y≡

−

at<sup>b(y,θ

)</sup>+c

wherein a is a gradient of a capacity degradation curve, c is an initial capacity retention rate, and b(y, θ

) is a function that depends on a previous battery capacity y<sub>t−

Δ

t</sub> and a trained parameter θ

.

16. The battery controller of claim 15, wherein the function b(y, θ

)has functional form b=θ

₀+θ

₁y , where θ

₀ and θ

₁ are learned parameters.

17. The battery controller of claim 12, wherein the battery capacity prediction model comprises an expression for a current battery capacity y at a time t:

y_t≅

−

at<sup>b(yt−

Δ

t,θ

)</sup>+c

wherein a is a gradient of a capacity degradation curve, c is an initial capacity retention rate, Δ

t is a small elapsed time, and b(y<sub>t−

Δ

t</sub>, θ

) is a function that depends on a previous battery capacity y and a trained parameter θ

.

18. The battery controller of claim 12, wherein the model accounts for an initial increase in battery capacity over time followed by a decline in battery capacity that is proportional to a square root of time elapsed.

19. The battery controller of claim 12, wherein the battery interface is configured to set a maximum or minimum terminal voltage in accordance with usage patterns and the prediction.

20. The battery controller of claim 12, wherein the battery interface is configured to set a charging speed of the battery in accordance with usage patterns and the prediction.