Secondary battery management system with remote parameter estimation

• US 10,447,046 B2
• Filed: 09/22/2016
• Issued: 10/15/2019
• Est. Priority Date: 09/22/2016
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Ravi, Nikhil, MirTabatabaei, Anahita, Klein, Reinhardt, Krupadanam, Ashish, Christensen, John F.
• Primary Examiner(s): Parihar, Suchin
• Application Number:
  US15/273,040
• Publication Number:
  US 20180083461A1
• Time in Patent Office: 1,118 Days
• Field of Search: 320107
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G06F 1/28 Supervision thereof, e.g. detecting power-supply failure by out of limits supervision
• CPC Class Codes: G06F 1/30 Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations for resetting only G06F1/24
• CPC Class Codes: H01M 10/425 Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing printed circuits H05K1/00
• CPC Class Codes: H01M 2010/4271 Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
• CPC Class Codes: H01M 2010/4278 Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller
• CPC Class Codes: H02J 7/00036 Charger exchanging data with battery
• CPC Class Codes: H02J 7/00047 with provisions for charging different types of batteries
• CPC Class Codes: H02J 7/0047 with monitoring or indicating devices or circuits
• CPC Class Codes: H02J 7/0048 Detection of remaining charge capacity or state of charge [SOC]
• CPC Class Codes: H02J 7/005 Detection of state of health [SOH]

Abstract

A battery system, having a battery management system configured to determine the state of charge and state of health of a secondary battery. The battery management system may export data to and receive inputs from a remote computer which calculates at least a portion of the state of health of the battery.

Claims

1. A battery system comprising a processor and a memory storing instructions that, when executed by the processor, cause the battery system to:

determine, based on an output of a sensor, at least one measured characteristic of a battery;

transmit the at least one measured characteristic from the battery system to a remote processing system;

receive by the battery system from the remote processing system, at least one parameter value calculated by the remote processing system based on the at least one measured characteristic and using a physics-based battery model, the at least one parameter value being indicative of a physical condition of the battery;

calculate by the battery system, a value indicative of a state of the battery based on the at least one parameter received from the remote processing system; and

regulate operation of the battery based on the calculated state of the battery cell.

2. The battery system of claim 1, wherein the instructions, when executed by the processor, further cause the battery system to calculate a first set of parameters based on the at least one measured characteristic, wherein the at least one parameter value calculated by the remote processing system includes a second set of parameters.

3. The battery system of claim 2, wherein the first set of parameters includes parameters expected to change more rapidly than the parameters of the second set of parameters.

4. The battery system of claim 2, wherein the first set of parameters includes parameters which are less computationally intensive than the parameters of the second set of parameters.

5. The battery system of claim 1, wherein the instructions, when executed by the processor, cause the battery system to calculate a value indicative of a state of the battery by applying a physics-based battery model that applies differential algebraic equations to account for physical parameters of a chemical composition of the battery cell.

6. The battery system of claim 5, wherein the instructions, when executed by the processor, cause the battery system to calculate the value indicative of a state of the battery by calculating the value indicative of the state of the battery based on an adjusted gain of an extended Kalman filter and a calculated measurement error.

7. The battery system of claim 1, wherein the instructions, when executed by the processor, cause the battery system to calculate a value indicative of a state of the battery by

determining a sensitivity covariance, and

reducing a number of parameters of a Recursive Least Squares filter.

8. The battery system of claim 1, wherein the instructions, when executed by the processor, cause the battery system to calculate the value indicative of a state of the battery further by

determining a cost function of a Moving Horizon Estimation method based on the at least one measured characteristic, and

updating the physics-based battery model based on the cost function of a Moving Horizon Estimation Method.

9. The battery system of claim 1, wherein the calculation of the at least one parameter value by the remote processing system occurs at a first time, and wherein the instructions, when executed by the processor, further cause the battery system to

calculate a value indicative of a state of the battery at a second time based on the at least one parameter that is calculated by the remote processing system at the first time, and calculate a value indicative of a state of the battery at a third time based on the at least one parameter that is calculated by the remote processing system at the first time,

wherein the second time is not equal to the third time.

10. A method of calculating the states and the parameters of a battery, the method comprising:

determining based on an output of a sensor, at least one measured characteristic of a battery;

transmitting the at least one measured characteristic from the battery system to a remote processing system;

receiving by the battery system from the remote processing system, at least one parameter value calculated by the remote processing system based on the at least one measured characteristic and using a physics-based battery model, the at least one parameter value being indicative of a physical condition of the battery;

calculating by the battery system, a value indicative of a state of the battery based on the at least one parameter received from the remote processing system; and

regulating operation of the battery based on the calculated state of the battery cell.

11. The method of claim 10, further comprising calculating, by the battery system, a first set of parameters based on the at least one measured characteristic, wherein the at least one parameter value calculated by the remote processing system includes a second set of parameters.

12. The method of claim 11, wherein the first set of parameters includes parameters expected to change more rapidly than the parameters of the second set of parameters.

13. The method of claim 11, wherein the first set of parameters includes parameters which are less computationally intensive than the parameters of the second set of parameters.

14. The method of claim 10, wherein calculating, by the battery system, the value indicative of the state of the battery includes applying a physics-based battery model that applies differential algebraic equations to account for physical parameters of a chemical composition of the battery cell.

15. The method of claim 14, wherein calculating, by the battery system, the value indicative of the state of the battery includes calculating the value indicative of the state of the battery based on an adjusted gain of an extended Kalman filter and a calculated measurement error.

16. The method of claim 10, wherein calculating, by the battery system, the value indicative of the state of the battery further includes

determining a sensitivity covariance, and

reducing a number of parameters of a Recursive Least Squares filter.

17. The method of claim 10, wherein calculating, by the battery system, the value indicative of the state of the battery further includes

determining a cost function of a Moving Horizon Estimation method based on the at least one measured characteristic, and

updating the physics-based battery model based on the cost function of a Moving Horizon Estimation Method.

18. The method of claim 10, wherein calculating the at least one parameter value calculated by the remote processing system occurs at a first time, and further comprising:

calculating, by the battery system, a value indicative of a state of the battery at a second time based on the at least one parameter calculated by the remote processing system at the first time; and

calculating, by the battery system, a value indicative of a state of the battery at a third time based on the at least one parameter calculated by the remote processing system at the first time,

wherein the second time is not equal to the third time.