Modeling a change in battery degradation

• US 9,800,073 B2
• Filed: 08/20/2015
• Issued: 10/24/2017
• 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:
  US14/831,378
• Publication Number:
  US 20170054317A1
• Time in Patent Office: 796 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 on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries. The model characterizes a capacity decay rate in accordance with a present value of the battery capacity. 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 are controlled based on the predicted future battery capacity to extend the battery'"'"'s usable lifetime.

Claims

1. A method for controlling a battery, comprising:

training parameters for a battery capacity prediction model based on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate in accordance with a present value of the battery capacity;

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 to extend the battery'"'"'s usable lifetime.

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 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.

5. 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.

6. 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.

7. A non-transitory 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 on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate in accordance with a present value of the battery capacity;

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 to extend the battery'"'"'s usable lifetime.

8. A battery controller, comprising:

a processor configured to train parameters for a battery capacity prediction model based on usage pattern information that correlates usage of similar batteries with capacity information for the respective similar batteries, wherein the model characterizes a capacity decay rate in accordance with a present value of the battery capacity, 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 to extend the battery'"'"'s usable lifetime.

9. The battery controller of claim 8, wherein a usage pattern comprises a timestamp, an electric current, a voltage, and a temperature.

10. The battery controller of claim 9, wherein the usage pattern further comprises a state of charge or difference in state of charge.

11. The battery controller of claim 8, 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.

12. The battery controller of claim 8, wherein the battery interface is configured to set a maximum or minimum terminal voltage in accordance with usage patterns and the prediction.

13. The battery controller of claim 8, wherein the battery interface is configured to set a charging speed of the battery in accordance with usage patterns and the prediction.