Secondary battery management system

Patent metadata

Field	Value
Publication number	US-10263447-B2
Application number	US-201615011148-A
Country	US
Kind code	B2
Filing date	Jan 29, 2016
Priority date	Jan 29, 2016
Publication date	Apr 16, 2019
Grant date	Apr 16, 2019

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.

First claim

What is claimed is:

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 mo

Assignees

• Bosch Gmbh Robert

Inventors

• MirTabatabaei Anahita
• Klein Reinhardt
• Krupadanam Ashish
• Ravi Nikhil
• Christensen John F

Classifications

• H02J7/92

  with prioritisation of loads or sources · CPC title

• H02J7/84

  Control of state of health [SOH] · CPC title

• H02J7/82

  Control of state of charge [SOC] · CPC title

• H02J7/50

  acting upon multiple batteries simultaneously or sequentially · CPC title

• H01M10/4257 Primary

  Smart batteries, e.g. electronic circuits inside the housing of the cells or batteries · CPC title