Secondary battery management

• US 10,686,321 B2
• Filed: 01/29/2016
• Issued: 06/16/2020
• Est. Priority Date: 01/29/2016
• 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., Kojic, Aleksandar, Stewart, Sarah, Kim, Sun Ung, Johnston, Christina
• Primary Examiner(s): Isla, Richard
• Assistant Examiner(s): Torres Ruiz, Johali A
• Application Number:
  US15/010,873
• Publication Number:
  US 20170222447A1
• Time in Patent Office: 1,600 Days
• Field of Search: 320134
• CPC Class Codes: H01M 10/052 Li-accumulators
• 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 10/4257 Smart batteries, e.g. electronic circuits inside the housing of the cells or batteries
• CPC Class Codes: H01M 10/44 Methods for charging or discharging circuits for charging H02J7/00
• CPC Class Codes: H01M 10/48 Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569
• 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 50/569 Constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals battery terminal connectors with integrated measuring arrangements G01R31/364
• CPC Class Codes: H02J 7/0013 acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• CPC Class Codes: H02J 7/0025 Sequential battery discharge in systems with a plurality of batteries
• 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]
• CPC Class Codes: H02J 7/0071 with a programmable schedule
• CPC Class Codes: H02J 7/007182 in response to battery voltage
• CPC Class Codes: Y02E 60/10 Energy storage using batteries
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

A method of managing a battery system, the battery system including at least one battery cell, at least one sensor configured to measure at least one characteristic of the battery cell, and a battery management system including a microprocessor and a memory, the method comprising receiving by the battery management system, from the at least one sensor at least one measured characteristic of the battery cell at a first time and at least one measured characteristic of the battery cell at a second time. The battery management system estimating, at least one state of the battery cell by applying a physics-based battery model, the physics based battery model being based on differential algebraic equations; and regulating by the battery management system, at least one of charging or discharging of the battery cell based on the at least one estimated state.

Claims

1. A method of managing a battery system, the battery system including at least one battery cell, at least one sensor configured to measure at least one characteristic of the battery cell, and a battery management system including a microprocessor and a memory, the method comprising

estimating, by the battery management system, a state of charge of the at least one battery cell at a first time by applying an electrochemical battery model,

wherein applying the electrochemical battery model includes using differential algebraic equations to estimate the state of charge of the at least one battery cell at the first time based at least in part on a previously determined state of charge and a previously determined value of a slowly varying parameter indicative of physical changes to a chemical composition of battery cell materials of the at least one battery cell

wherein applying the electrochemical battery model provides an estimated value of the slowly varying parameter at the first time;

receiving by the battery management system from the at least one sensor, a measured value of at least one measured characteristic of the battery at the first time;

updating, by the battery management system, the state of charge of the at least one battery cell at the first time based on the at least one measured characteristic of the battery at the first time; and

regulating, by the battery management system, at least one of charging or discharging of the battery based on the updated state of charge of the at least one battery cell.

2. The method of claim 1, wherein estimating the state of charge of the at least one battery cell includes applying an extended Kalman filter.

3. The method of claim 2, wherein updating the state of charge of the at least one battery cell further includes

determining a noise covariance;

and

adjusting a gain of the extended Kalman filter based on the determined noise covariance.

4. The method of claim 3, further comprising:

estimating a value of the at least one measured characteristic of the battery at the first time using the electrochemical battery model; and

calculating a measurement error based on the measured value of the at least one measured characteristic of the battery at the first time and the estimated value of the at least one measured characteristic of the battery at the first time, and

wherein updating the state of charge of the at least one battery cell further includes updating the state of charge of the at least one battery cell based on the adjusted gain of the extended Kalman filter and the calculated measurement error.

5. The method of claim 4, wherein adjusting the gain of the extended Kalman filter includes calculating an adjusted gain based on the equation:

6. The method of claim 2, wherein applying the extended Kalman filter includes using a single extended Kalman filter to jointly estimate both the state of charge of the at least one battery cell and the slowly varying parameter of the at least one battery cell.

7. The method of claim 2, wherein applying the extended Kalman filter includes using a first extended Kalman filter to estimate the state of charge of the at least one battery cell and using a second extended Kalman filter to separately estimate the slowly varying parameter of the at least one battery cell.

8. The method of claim 1, wherein regulating the at least one of charging or discharging of the battery based on the state of charge includes regulating a rate of charge or discharge.

17. The method of claim 1, wherein the slowly varying parameter of the at least one battery cell includes at least one selected from a group consisting of cathode particle radius, anode particle radius, ion diffusion rates, intercalation current and transference number, solution conductivity, cell porosity, and equilibrium potential of the anode and cathode.

18. The method of claim 1, wherein the slowly varying parameter of the at least one battery cell includes at least one selected from a group consisting of

a volume fraction of active material in an anode of the at least one battery cell,

a volume fraction of active material in a cathode of the at least one battery cell,

a total cyclable lithium in the at least one battery cell, and

a radius of particles in one or more electrodes in the at least one battery cell.

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

estimate a state of charge of the at least one battery cell at a first time by applying an electrochemical battery model,

wherein applying the electrochemical battery model includes using differential algebraic equations to estimate the state of charge of the at least one battery cell at the first time based at least in part on a previously determined state of charge and a previously determined value of a slowly varying parameter indicative of a physical change in a chemical composition of battery cell materials of the at least one battery cell,

wherein applying the electrochemical battery model provides an estimated value of the slowly varying parameter at the first time;

receive a measured value of at least one measured characteristic of the battery from a sensor associated with the at least one battery cell at the first time;

update the state of charge of the at least one battery cell at the first time based on the at least one measured characteristic of the battery at the first time; and

regulate at least one of charging or discharging of the battery based on the updated state of charge of the at least one battery cell.

10. The battery management system of claim 9, wherein the memory stores instructions that, when executed by the processor, cause the battery management system to estimate the state of charge of the at least one battery cell at the first time by applying an extended Kalman filter.

11. The battery management system of claim 10, wherein the memory stores instructions that, when executed by the processor, cause the battery management system to update the state of charge of the at least one battery cell by

determining a noise covariance;

and

adjusting a gain of the extended Kalman filter based on the determined noise covariance.

12. The battery management system of claim 11, wherein the memory stores instructions that, when executed by the processor, further cause the battery management system to:

estimate a value of the at least one measured characteristic of the battery at the first time using the electrochemical battery model; and

calculate a measurement error based on the measured value of the at least one measured characteristic of the battery at the first time and the estimated value of the at least one measured characteristic of the battery at the first time, and

wherein the instructions, when executed by the processor, cause the battery system to update the state of charge of the at least one battery cell by updating the state of charge of the at least one battery cell based on the adjusted gain of the extended Kalman filter and the calculated measurement error.

13. The battery management system of claim 12, wherein the memory stores instructions that, when executed by the processor, cause the battery management system to adjust the gain of the extended Kalman filter by calculating an adjusted gain based on the equation:

14. The battery management system of claim 10, wherein the memory stores instructions that, when executed by the processor, cause the battery management system to apply the extended Kalman filter by using a single extended Kalman filter to jointly estimate both the state of charge of the at least one battery cell and the slowly varying parameter of the at least one battery cell.

15. The battery management system of claim 10, wherein the memory stores instructions that, when executed by the processor, further cause the battery management system to apply the extended Kalman filter by using a first extended Kalman filter to estimate the state of charge of the at least one battery cell and using a second extended Kalman filter to separately estimate the slowly varying parameter of the at least one battery cell.

16. The battery management system of claim 9, wherein the memory stores instructions that, when executed by the processor, cause the battery management system to regulate the at least one of charging or discharging of the battery based on the state of charge includes regulating a rate of charge or discharge.