Systems and methods for optimizing battery designs

• US 10,445,449 B2
• Filed: 11/14/2016
• Issued: 10/15/2019
• Est. Priority Date: 11/14/2016
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Cox, Kelsea, Arroyo, Sharon Filipowski
• Primary Examiner(s): Fernandez Rivas, Omar F
• Assistant Examiner(s): Cocchi, Michael Edward
• Application Number:
  US15/350,764
• Publication Number:
  US 20180137229A1
• Time in Patent Office: 1,065 Days
• CPC Class Codes: G06F 30/17 Mechanical parametric or variational design
• CPC Class Codes: G06F 30/30 Circuit design
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: H01M 50/529 Intercell connections through partitions, e.g. in a battery casing

Abstract

Systems, computer readable media, and method concern determining operational parameters of an inventory of components to be included in a battery. The method further includes determining an initial working solution for a battery design layout for the battery. The method also includes determining, based on the initial working solution, one or more possible solutions for the battery design layout utilizing a local search algorithm. The local search algorithm iteratively generates the one or more possible solutions from the initial working solution by swapping components from one or more components assigned to locations in the battery design layout with at least one of other components assigned to locations in the battery design layout and other components remaining in the inventory. The swapping of components is limited by a tabu list.

Claims

1. A method, comprising:

determining operational parameters of an inventory of components to be included in a battery, wherein the components include storage components and electrical components;

determining an initial working solution for a battery design layout for the battery, wherein the initial working solution comprises a set of components from the inventory of components assigned to locations in the battery design layout;

determining, based on the initial working solution, two or more possible solutions for the battery design layout utilizing a local search algorithm,

wherein the local search algorithm iteratively generates the two or more possible solutions from the initial working solution by swapping components from one or more components assigned to locations in the battery design layout with at least one of other components assigned to locations in the battery design layout and other components remaining in the inventory,

wherein a tabu list stores identifications of one or more components that are not allowed to swap for a number of iterations of the local search algorithm,

wherein a module comprises one or more virtual cells;

wherein objective function values are calculated for each of the one or more possible solutions based on the operational parameters of components assigned to locations in the two or more possible solutions, wherein one or more terms in the objective function values are calculated for each of the two or more possible solutions by minimizing difference in capacity across all virtual cells in the battery, minimizing difference in impedance across all the modules comprising the virtual cells, or minimizing difference in impedance across all the virtual cells, and

wherein the swapping components is determined at least partially based on the tabu list;

performing a new local search based on a cooling schedule based on a probability of replacing a current solution with an updated solution as determined by a predetermined number of iterations of the local search algorithm that have been performed; and

storing at least one of the two or more possible solutions;

providing, via an interface to a user, a battery build selected from the two or more possible solutions based on the objective function values calculated for the two or more possible solutions; and

testing, by an electrical battery testing equipment, physical battery cells based on the battery design layout under operating conditions.

2. The method of claim 1, the method further comprising:

performing a diversification process for the battery design layout, wherein the diversification process generates one or more additional solutions in parts of a solution space minimally explored.

3. The method of claim 2, wherein the diversification process comprises:

determining a new initial diversify solution to test; and

determining, based on the new initial diversify solution, the one or more additional solutions for the battery design layout to consider for new working solutions.

4. The method of claim 1, the method further comprising:

selecting one or more elite solutions from the two or more possible solutions;

wherein the one or more elite solutions are of higher quality than a previously stored solution of the two or more possible solutions; and

performing an intensification process on the one or more elite solutions.

5. The method of claim 1, wherein the storage components comprise a plurality of one or more of lithium ion (Li-ion) cells, nickel cadmium (NiCad) cells, and nickel metal hydride (NiMh) cells, and wherein the electrical components comprise include a plurality of one or more of fuses, pass transistors, diodes, thermosensors, and an indicator.

15. The method of claim 1, wherein a weight for each objection function values is preset in the battery design layout.

16. The method of claim 1, wherein a weight for each objection function values is provided by the user in the battery design layout.

6. A system, comprising:

one or more memory devices storing instructions; and

one or more processors coupled to the one or more memory devices and configured to execute the instructions to perform a method comprising;

determining operational parameters of an inventory of components to be included in a battery, wherein the components include storage components and electrical components;

determining an initial working solution for a battery design layout for the battery, wherein the initial working solution comprises a set of components from the inventory of components assigned to locations in the battery design layout;

determining, based on the initial working solution, two or more possible solutions for the battery design layout utilizing a local search algorithm,

wherein the local search algorithm iteratively generates the two or more possible solutions from the initial working solution by swapping components from one or more components assigned to locations in the battery design layout with at least one of other components assigned to locations in the battery design layout and other components remaining in the inventory,

wherein a tabu list stores identifications of one or more components that are not allowed to swap for a number of iterations of the local search algorithm,

wherein a module comprises one or more virtual cells;

wherein objective function values are calculated for each of the one or more possible solutions based on the operational parameters of components assigned to locations in the two or more possible solutions, wherein one or more terms in the objective function values are calculated for each of the two or more possible solutions by minimizing difference in capacity across all virtual cells in the battery, minimizing difference in impedance across all the modules comprising the virtual cells, or minimizing difference in impedance across all the virtual cells, and

wherein the swapping components is determined at least partially based on the tabu list;

performing a new local search based on a cooling schedule based on a probability of replacing a current solution with an updated solution as determined by a predetermined number of iterations of the local search algorithm that have been performed; and

storing at least one of the two or more possible solutions;

providing, via an interface to a user, a battery build selected from the two or more possible solutions based on the objective function values calculated for the two or more possible solutions; and

testing, by an electrical battery testing equipment, physical battery cells based on the battery design layout under operating conditions.

7. The system of claim 6, the method further comprising:

performing a diversification process for the battery design layout, wherein the diversification process generates one or more additional solutions in parts of a solution space minimally explored.

8. The system of claim 7, wherein the diversification process comprises:

determining a new initial diversify solution to test; and

determining, based on the new initial diversify solution, the one or more additional solutions for the battery design layout to consider for new working solutions.

9. The system of claim 6, the method further comprising:

selecting one or more elite solutions from the two or more possible solutions;

wherein the one or more elite solutions are of higher quality than a previously stored solution of the two or more possible solutions; and

performing an intensification process on the one or more elite solutions.

10. The system of claim 6, wherein the storage components comprise a plurality of one or more of lithium ion (Li-ion) cells, nickel cadmium (NiCad) cells, and nickel metal hydride (NiMh) cells, and wherein the electrical components comprise include a plurality of one or more of fuses, pass transistors, diodes, thermosensors, and an indicator.

17. The system of claim 6, wherein a weight for each objection function values is preset in the battery design layout.

18. The system of claim 6, wherein a weight for each objection function values is provided by the user in the battery design layout.

11. A non-transitory computer readable medium storing instructions for causing one or more processors to perform a method, the method comprising:

determining operational parameters of an inventory of components to be included in a battery, wherein the components include storage components and electrical components;

determining an initial working solution for a battery design layout for the battery, wherein the initial working solution comprises a set of components from the inventory of components assigned to locations in the battery design layout;

determining, based on the initial working solution, two or more possible solutions for the battery design layout utilizing a local search algorithm,

wherein the local search algorithm iteratively generates the two or more possible solutions from the initial working solution by swapping components from one or more components assigned to locations in the battery design layout with at least one of other components assigned to locations in the battery design layout and other components remaining in the inventory,

wherein a tabu list stores identifications of one or more components that are not allowed to swap for a number of iterations of the local search algorithm,

wherein a module comprises one or more virtual cells;

wherein objective function values are calculated for each of the one or more possible solutions based on the operational parameters of components assigned to locations in the two or more possible solutions, wherein one or more terms in the objective function values are calculated for each of the two or more possible solutions by minimizing difference in capacity across all virtual cells in the battery, minimizing difference in impedance across all the modules comprising the virtual cells, or minimizing difference in impedance across all the virtual cells, and

wherein the swapping components is determined at least partially based on the tabu list;

performing a new local search based on a cooling schedule based on a probability of replacing a current solution with an updated solution as determined by a predetermined number of iterations of the local search algorithm that have been performed; and

storing at least one of the two or more possible solutions;

providing, via an interface to a user, a battery build selected from the two or more possible solutions based on the objective function values calculated for the two or more possible solutions; and

testing, by an electrical battery testing equipment, physical battery cells based on the battery design layout under operating conditions.

12. The non-transitory computer readable medium of claim 11, the method further comprising:

performing a diversification process for the battery design layout, wherein the diversification process generates one or more additional solutions in parts of a solution space minimally explored.

13. The non-transitory computer readable medium of claim 12, wherein the diversification process comprises:

determining a new initial diversify solution to test; and

determining, based on the new initial diversify solution, the one or more additional solutions for the battery design layout to consider for new working solutions.

14. The non-transitory computer readable medium of claim 11, the method further comprising:

selecting one or more elite solutions from the two or more possible solutions;

wherein the one or more elite solutions are of higher quality than a previously stored solution of the two or more possible solutions; and

performing an intensification process on the one or more elite solutions.

19. The non-transitory computer readable medium of claim 11, wherein a weight for each objection function values is preset in the battery design layout.

20. The non-transitory computer readable medium of claim 11, wherein a weight for each objection function values is provided by the user in the battery design layout.