Method for optimizing material properties of components of a battery, manufacturing a fiber network, an electrode and a battery

Patent metadata

Field	Value
Publication number	US-2024222641-A1
Application number	US-202118289132-A
Country	US
Kind code	A1
Filing date	May 19, 2021
Priority date	May 19, 2021
Publication date	Jul 4, 2024
Grant date	—

Abstract

The present invention relates to a method for optimizing material properties of components of a battery comprising the following steps: Inputting material parameter data, with said material parameter data relating to properties of constituents of the components of the battery; simulating one or more components and/or constituents of components of the battery using a simulation model which takes the material parameter data as input to generate simulation result data as output, with the simulation result data comprising at least one of the following data: data on microscopic geometric features of the component, data on a conductivity of the component, data on a current collector, data on a binder phase, data on a diffusivity of the electrolyte and data on a charging and discharging potential of the component; training an AI model with the material parameter data as input and the simulation result data as output; evaluating a final accuracy of the AI model with respect to the simulation model using extended material parameter data; using the AI model to output material properties of the constituents of the components of the battery. The invention further relates to a method for manufacturing a fiber network, to an electrode and to a battery.

First claim

1 - 48 . (canceled)

49 . A method for optimizing material properties of components of a battery, comprising the following steps:
(1) Inputting material parameter data, with said material parameter data relating to properties of constituents of the components of the battery, (2) simulating one or more components and/or constituents of components of the battery using a simulation model which takes the material parameter data as input to generate simulation result data as output, with the simulation result data comprising at least one of the following data: data on microscopic geometric features of the component, data on a conductivity of the component, data on a current collector, data on a binder phase, data on a diffusivity of the electrolyte and data on a charging and discharging potential of the component; (3) training an AI model with the material parameter data as input and the simulation result data as output; (4) evaluating a final accuracy of the AI model with respect to the simulation model using extended material parameter data; (5) using the AI model to output material properties of the constituents of the components of the battery.

50 . The method of claim 49 ,
wherein the battery is an electrochemical energy storage device.

51 . The method of claim 50 ,
wherein the electrochemical energy storage device is a multivalent-ion or monovalent-ion battery.

52 . The method of claim 49 ,
wherein the components of the battery are selected from a group of members consisting of one or more electrodes, a current collector, a positive electrode, a negative electrode, a separator, an electrolyte, a binder, carbon black and combinations of the foregoing.

53 . The method of claim 49 ,
wherein the constituents of the components of the battery are selected from a group of members consisting of a fiber network, an active material (AM), a binder, a conductive additive and an electrolyte and combinations of the foregoing.

54 . The method of claim 53 ,
wherein the fiber network comprises a plurality of fibers and a material of the plurality of fibers comprises metal or carbon.

55 . The method of claim 53 ,
wherein the active material is selected from a group of members consisting of graphite, silicon, silicon/carbon composite, silicon-dioxide/carbon composite, tin, tin-oxide, lithium metal of a lithium metal composite or Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) in any kind of stoichiometry, Lithium Iron phosphate (LF(M,N,C)P), Spinel type manganese oxide (Mn2O4).

56 . The method of claim 53 ,
wherein the binder is selected from a group of members consisting of polyvinylidene fluoride or styrene-butadiene copolymer, carboxymethylcellulose, polyvinylidene fluoride hexafluoropropylene, alginates and polyvinylalcohole.

57 . The method of claim 53 ,
wherein the conductive additive is selected from a group of members consisting of carbon black, Super P in any kind of size, Carbon Nanotubes, graphene and metal nanowires.

58 . The method of claim 53 ,
wherein the material parameter data comprises data on the fiber network properties and/or data on the AM properties and/or data on the electrolyte properties.

59 . The method of claim 53 ,
wherein the data on the fiber network properties are selected from a group of members consisting of a fiber density, a fiber length, a fiber curvature, a fiber cross section geometry, a fiber diameter, a fiber distribution orientation, a fiber conductivity or combinations of the foregoing.

60 . The method of claim 53 ,
wherein the data on the AM properties are selected from a group of members consisting of an AM fraction, AM particle size, AM particle shape, AM conductivity, AM diffusivity, AM equilibrium open circuit potential, AM reaction rate or combinations of the foregoing.

61 . The method of claim 53 ,
wherein the data on the electrolyte properties are selected from a group of members consisting of an initial concentration, a transference number, an electrolyte diffusivity or combinations of the foregoing.

62 . The method of claim 49 ,
wherein the simulation model is based on a microstructure simulation of the constituents of the component of the battery.

63 . The method of claim 53 ,
wherein the simulation model comprises a simulation of the fiber network.

64 . The method of claim 53 ,
wherein the simulation model comprises a simulation of the active material and/or the binder and/or the conductive additives.

65 . The method of claim 49 ,
wherein the extended material parameter data is generated by extrapolating the material parameter data.

66 . The method of claim 65 ,
wherein the extended material parameter data is set manually and/or by using an extrapolation strategy.

67 . The method of claim 66 ,
wherein the extrapolation strategy is based on a fixed step extrapolation, a random extrapolation or an extrapolation function.

68 . The method of any of claim 49 ,
wherein step (4) comprises: (4.1.) inputting the extended material parameter data into the simulation model which outputs extended simulation result data; (4.2.) inputting the extended material parameter data into the pretrained AI model which outputs predicted result data; (4.3.) determining an uncertainty factor value based on a difference between the extended simulation result data and the predicted result data; and (4.4.) finishing the training of the AI model, if the uncertainty factor value is smaller than a predefined uncertainty factor threshold value, and repeating the previous steps (3) to (4), wherein the extended material parameter data is added to the material parameter data and the extended simulation result data is added to the simulation result data, otherwise.

69 . The method of claim 68 ,
wherein the difference between the extended simulation result data and the predicted result data comprises an error matrix.

70 . The method of claim 69 ,
wherein the uncertainty factor value comprises the highest value in the error matrix or an average of all values in the error matrix.

71 . The method of claim 49 ,
wherein the accuracy of the AI model is predicted by another integrated AI model.

72 . A method for manufacturing a fiber network, comprising:
(1) inputting material parameter data, with said material parameter data relating to properties of constituents of components of the battery, (2) simulating one or more components and/or constituents of the components of the battery using a simulation model which uses the material parameter data to generate simulation result data, with the simulation result data comprising at least one of the following data: data on a porosity of the component, data on a conductivity of the component, data on a current collector, data on a binder phase and data on a diffusivity of the electrolyte; (3) fitting an AI model to the material parameter data and to the simulation result data; (4) evaluating a final accuracy of the AI model with respect to the simulation model using extended material parameter data; (5) using the AI model to output the material properties of the constituents of the components of the battery; (6) manufacturing a fiber network based on the determined material properties of the constituents of the components of the battery, wherein the manufacturing of the fiber network comprises:
step a) providing a plurality of fibers and placing the fibers in a hot press and
step b)

Assignees

• Max Planck Gesellschaft

Inventors

• Jahnke Timotheus
• Wang Yuanzhen
• Farley Erik
• Spatz Joachim

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0475

  Generative networks · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title