State-of-charge balancing in battery management systems for si/li batteries

Patent metadata

Field	Value
Publication number	US-11901748-B2
Application number	US-202217591334-A
Country	US
Kind code	B2
Filing date	Feb 2, 2022
Priority date	Feb 2, 2022
Publication date	Feb 13, 2024
Grant date	Feb 13, 2024

Abstract

Systems and methods are provided for state-of-charge balancing in battery management systems for Si/Li batteries. State-of-charge (SOC) of one or more lithium-ion cells may be assessed, and based on the assessing of the SOC, the one or more lithium-ion cells may be controlled. The controlling may include setting or modifying one or more operating parameters of at least one lithium-ion cell, and the controlling may be configured to equilibrate the SOC of the one or more lithium-ion cells or to modify an SOC of at least one lithium-ion cell so that the one or more lithium-ion cells have a balanced SOC.

First claim

What is claimed is:

1. A method for managing a battery pack comprising a plurality of lithium-ion cells, the method comprising:
assessing state-of-charge (SOC) of the plurality of lithium-ion cells; and
controlling, based on the assessing of state-of-charge (SOC), the plurality of lithium-ion cells;
wherein one or more of the plurality of lithium-ion cells are silicon/lithium (Si/Li) cells;
wherein assessing the state-of-charge (SOC) comprises calculating the state-of-charge (SOC) using one or more state-of-charge (SOC) models that account for one or more characteristics that are associated with silicon/lithium (Si/Li) cells;
wherein the one or more characteristics comprise one or both of open circuit voltage (OCV) hysteresis and nonlinear time-dependent transition in the OCV during transitions between charge to discharge states; and
wherein the controlling is configured to equilibrate the state-of-charge (SOC) of the plurality of lithium-ion cells, or to modify a state-of-charge (SOC) of an individual lithium-ion cell or groups of lithium-ion cells so that the plurality of lithium-ion cells as a whole has a balanced state-of-charge (SOC).

2. The method of claim 1 , wherein each of the plurality of lithium-ion cells comprises a silicon-dominant cell comprising a silicon-dominant anode with silicon >50% of active material of the anode.

3. The method of claim 1 , further comprising configuring at least one state-of-charge (SOC) model based on a physics-based model associated with at least one lithium-ion cell, and wherein the physics-based model comprises information relating to modeling of one or more physical phenomena as factors that affect the SOC.

4. The method of claim 1 , further comprising configuring at least one state-of-charge (SOC) model based on a machine-learning (ML) model.

5. The method of claim 4 , further comprising training the machine-learning (ML) model using one or more machine-learning (ML) algorithms.

6. The method of claim 1 , further comprising training at least one state-of-charge (SOC) model.

7. The method of claim 6 , further comprising training the at least one state-of-charge (SOC) model using training data.

8. The method of claim 6 , further comprising training the at least one state-of-charge (SOC) model using an Adam optimizer.

9. The method of claim 1 , further comprising configuring at least one state-of-charge (SOC) model using data related to or acquired during formation of at least one lithium-ion cell or fabrication of one or more components of at least one lithium-ion cell.

10. The method of claim 1 , further comprising configuring at least one state-of-charge (SOC) model using data related to or acquired during operation of at least one lithium-ion cell.

11. The method of claim 1 , wherein at least one state-of-charge (SOC) model comprises a multilayer perceptron (MLP) model.

12. The method of claim 1 , further comprising training at least one state-of-charge (SOC) model until it achieves a mean absolute error (MAE) meeting one or more predefined thresholds.

13. The method of claim 1 , further comprising training at least one state-of-charge (SOC) model until it achieves a root mean square error (RMSE) and/or an r-squared value meeting one or more predefined thresholds.

14. The method of claim 1 , further comprising controlling the plurality of lithium-ion cells to maintain one or more lithium-ion cells of the plurality of lithium-ion cells within a predefined range of a tracked value at any given point in a life of the battery pack.

15. The method of claim 1 , wherein the assessing of the state-of-charge (SOC) comprises determining state-of-charge (SOC) prediction for at least one lithium-ion cell of the plurality of lithium-ion cells; and wherein the controlling comprising determining at least one action based on the SOC prediction.

16. The method of claim 15 , further comprising determining the state-of-charge (SOC) prediction based on or using one or more of: deviation between a most recent state-of-charge (SOC) calculation and state-of-charge (SOC) measurement, changes to predicted useful life for the at least one lithium-ion cell, and reinforcement learning based modeling.

17. The method of claim 1 , wherein the controlling comprises setting or modifying one or more operating parameters of an individual lithium-ion cell or groups of lithium-ion cells within the plurality of lithium-ion cells.

18. The method of claim 17 , wherein the one or more operating parameters comprise current applied to at least one lithium-ion cell, and wherein the controlling comprising setting or adjusting the current based on calculated SOC value associated with the individual lithium-ion cell or the groups of lithium-ion cells, and/or to balance the SOC values of the plurality of lithium-ion cells.

19. A system comprising:
a plurality of lithium-ion cells; and
one or more circuits configured to:
assess state-of-charge (SOC) of the plurality of lithium-ion cells; and
control, based on the assessing of state-of-charge (SOC), the plurality of lithium-ion cells;
wherein one or more of the plurality of lithium-ion cells are silicon/lithium (Si/Li) cells;
wherein assessing the state-of-charge (SOC) comprises calculating the state-of-charge (SOC) using one or more state-of-charge (SOC) models that account for one or more characteristics that are associated with silicon/lithium (Si/Li) cells;
wherein the one or more characteristics comprise one or both of open circuit voltage (OCV) hysteresis and nonlinear time-dependent transition in the OCV during transitions between charge to discharge states; and
wherein the controlling is configured to equilibrate the state-of-charge (SOC) of the plurality of lithium-ion cells or to modify a state-of-charge (SOC) of an individual lithium-ion cell or groups of lithium-ion cells so that the plurality of lithium-ion cells as a whole has a balanced state-of-charge (SOC).

20. The system of claim 19 , wherein each of the plurality of lithium-ion cells comprises a silicon-dominant cell comprising a silicon-dominant anode with silicon >50% of active material of the anode.

21. The system of claim 19 , wherein the one or more circuits are configured to train at least one state-of-charge (SOC) model.

22. The system of claim 21 , wherein the one or more circuits are configured to train the at least one state-of-charge (SOC) model using training data.

23. The system of claim 21 , wherein the one or more circuits are configured to train the at least one state-of-charge (SOC) model using an Adam optimizer.

24. The system of claim 19 , wherein the one or more circuits are configured to configure at least one state-of-charge (SOC) model using data related to or acquired during formation of at least one lithium-ion cell or fabrication of one or more components of at least one lithium-ion cell.

25. The system of claim 19 , wherein the one or more circuits are configured to configure at least one state-of-charge (SOC) model using data related to or acquired during operation of at least one lithium-ion cell.

26. The system of claim 19 , wherein the one or more circuits are configured to control the plurality of lithium-ion cells to maintain one or more lithium-ion cells of the plurality of lithium-ion cells within a predefined range of a tracked value at any given point in a life of a battery pack comprising the plurality of lithium-ion cells.

Assignees

• Enevate Corp

Inventors

• Keene Samuel
• Park Benjamin Yong
• Saab Samer

Classifications

• H02J7/82

  Control of state of charge [SOC] · CPC title

• H02J7/52 Primary

  for charge balancing, e.g. equalisation of charge between batteries · CPC title

• H02J7/0014 Primary

  Electricity · mapped topic

• H01M4/386

  Silicon or alloys based on silicon · CPC title

• H01M10/0525 Primary

  Rocking-chair batteries, i.e. batteries with lithium insertion or intercalation in both electrodes; Lithium-ion batteries · CPC title