Secondary battery management system

• US 10,263,447 B2
• Filed: 01/29/2016
• Issued: 04/16/2019
• Est. Priority Date: 01/29/2016
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: MirTabatabaei, Anahita, Klein, Reinhardt, Krupadanam, Ashish, Ravi, Nikhil, Christensen, John F.
• Primary Examiner(s): Berhanu, Samuel
• Assistant Examiner(s): Kebede, Tessema
• Application Number:
  US15/011,148
• Publication Number:
  US 20170222449A1
• Time in Patent Office: 1,173 Days
• Field of Search: 320134
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• 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: H02J 2207/10 Control circuit supply, e.g. means for supplying power to the control circuit
• CPC Class Codes: H02J 7/0013 acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• 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: Y02E 60/10 Energy storage using batteries
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

A method and system for managing a battery system. The method including receiving at least one measured characteristic of the battery over a pre-defined time horizon from the at least one sensor, receiving at least one estimated characteristic of the battery from a electrochemical-based battery model based on differential algebraic equations, determining a cost function of a Moving Horizon Estimation based on the at least one measured characteristic and the at least one estimated characteristic, updating the electrochemical-based battery model based on the cost function, estimating at least one state of the at least one battery cell by applying the electrochemical-based battery model, and regulating at least one of charging or discharging of the battery based on the estimation of the at least one state of the at least one battery cell.

Claims

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

receiving, by the battery management system, at least one measured characteristic of the battery cell over a pre-defined time horizon from the at least one sensor;

receiving, by the battery management system, at least one estimated characteristic of the battery cell from a electrochemical-based battery model based on differential algebraic equations;

determining, by the battery management system, a cost function of a Moving Horizon Estimation method based on the at least one measured characteristic and the at least one estimated characteristic by determining an error between the at least one measured characteristic and the at least one estimated characteristic; and

determining an arrival cost by comparing the error to a predetermined threshold, the arrival cost having a weight based on previously estimated states and parameters;

updating, by the battery management system, the electrochemical-based battery model based on the cost function of the Moving Horizon Estimation;

estimating, by the battery management system, at least one state of the at least one battery cell by applying the electrochemical-based battery model that applies differential algebraic equations to account for physical parameters of a chemical composition of the at least one battery cell; and

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

2. The method of claim 1, wherein determining the arrival cost includes determining the arrival cost using a Kalman Filter when the battery system is a linear unconstrained system.

3. The method of claim 1, wherein determining the arrival cost includes

linearizing the battery system when the battery system is a non-linear constrained system;

and

determining a time varying arrival cost gain for each parameter based on a estimation robustness of each parameter using a modified extended Kalman Filter.

4. The method of claim 1, wherein the at least one state of the at least one battery cell includes at least one of a state-of-charge or a state-of-health of the at least one battery cell.

5. The method of claim 4, wherein estimating the at least one state of the at least one battery cell by applying the electrochemical-based battery model includes synchronously estimating the state-of-charge and the state-of-health of the at least one battery cell.

6. The method of claim 4, wherein estimating the at least one state of the at least one battery cell by applying the electrochemical-based battery model includes separately estimating the state-of-charge and the state-of-health of the at least one battery cell, wherein the state-of-charge of the at least one battery cell is estimated continuously, and wherein the state-of-health of the at least one battery cell is estimated at a pre-defined time.

7. The method of claim 6, wherein the pre-defined time is one hundred drive cycles.

8. The method of claim 1, further comprising suspending the estimation of the at least one state of the at least one battery cell under a low input persistency of excitation or under a low gradient of output.

9. The method of claim 1, wherein the electrochemical-based battery model is a Reduced-Order-Model of a Newman model.

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

receive at least one measured characteristic of at least one battery cell over a pre-defined time horizon from at least one sensor, wherein the at least one battery cell and the at least one sensor are part of a battery system;

receive at least one estimated characteristic of the at least one battery cell from an electrochemical-based battery model based on differential algebraic equations;

determine a cost function of a Moving Horizon Estimation based on the at least one measured characteristic and the at least one estimated characteristic by determining an error between the at least one measured characteristic and the at least one estimated characteristic; and

determining an arrival cost by comparing the error to a predetermined threshold, the arrival cost having a weight based on previously estimated states and parameters;

update the electrochemical-based battery model based on the cost function of the Moving Horizon Estimation;

estimate at least one state of the at least one battery cell by applying the electrochemical-based battery model that applies differential algebraic equations to account for physical parameters of a chemical composition of the at least one battery cell; and

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

11. The battery management system of claim 10, wherein determine the arrival cost includes instructions that, when executed by the processor, cause the battery management system to determine the arrival cost using a Kalman Filter when the battery system is a linear unconstrained system.

12. The battery management system of claim 10, wherein determine the arrival cost includes instructions that, when executed by the processor, cause the battery management system to

linearize the battery system when the battery system is a non-linear constrained system;

and

determine a time varying arrival cost gain for each parameter based on a estimation robustness of each parameter using a modified extended Kalman Filter.

13. The battery management system of claim 10, wherein the at least one state of the at least one battery cell includes at least one of a state-of-charge or a state-of-health of the at least one battery cell.

14. The battery management system of claim 13, wherein estimate the at least one state of the at least one battery cell by applying the electrochemical-based battery model includes instructions that, when executed by the processor, cause the battery management system to synchronously estimate the state-of-charge and the state-of-health of the at least one battery cell.

15. The battery management system of claim 13, wherein estimate the at least one state of the at least one battery cell by applying the electrochemical-based battery model includes instructions that, when executed by the processor, cause the battery management system to separately estimate the state-of-charge and the state-of-health of the at least one battery cell, wherein the state-of-charge of the at least one battery cell is estimated continuously, and wherein the state-of-health of the at least one battery cell is estimated at a pre-defined time.

16. The battery management system of claim 15, wherein the pre-defined time is one hundred drive cycles of a vehicle.

17. The battery management system of claim 10, further comprising instructions that, when executed by the processor, cause the battery management system to suspend the estimation of the at least one state of the at least one battery cell under a low input persistency of excitation or under a low gradient of output.

18. The battery management system of claim 10, wherein the electrochemical-based battery model is a Reduced-Order-Model of a Newman model.

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

receiving, by the battery management system, at least one measured characteristic of the battery cell over a pre-defined time horizon from the at least one sensor;

receiving, by the battery management system, at least one estimated characteristic of the battery cell from an electrochemical-based battery model based on differential algebraic equations;

determining, by the battery management system, a cost function of a Moving Horizon Estimation method based on the at least one measured characteristic and the at least one estimated characteristic by

determining an error between the at least one measured characteristic and the at least one estimated characteristic; and

determining an arrival cost that has a weight based on previously estimated states and parameters;

when the error is less than a predetermined threshold, updating by the battery management system, the electrochemical-based battery model based on the cost function of the Moving Horizon Estimation;

when the error is greater than the predetermined threshold, modifying states and parameters of the battery model using an estimate modifier and reevaluating until the error is less than the predetermined threshold indicating that the battery model is accurate;

estimating, by the battery management system, at least one state of the at least one battery cell by applying the electrochemical-based battery model that applies differential algebraic equations to account for physical parameters of a chemical composition of the at least one battery cell; and

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

20. The method of claim 19, including a noise covariance matrix has an inverse relation with the arrival cost gains.