Autonomous screening and optimization of battery formation and cycling procedures

Patent metadata

Field	Value
Publication number	US-10992156-B2
Application number	US-201816161790-A
Country	US
Kind code	B2
Filing date	Oct 16, 2018
Priority date	Oct 17, 2017
Publication date	Apr 27, 2021
Grant date	Apr 27, 2021

Abstract

A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final recommended policies.

First claim

What is claimed:

1. A method of optimizing charging policies for battery cell formation or cycling, the method comprising:
a) defining a parameter space for a plurality of battery cells being optimized for lifetime;
b) specifying hyperparameters, wherein said hyperparameters comprise resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters;
c) selecting a subset of said charging policies, including repetitions of policies, wherein said charging policies include one or more charging rates;
d) testing said subset of said charging policies on the plurality of battery cells being optimized, using a battery cycling instrument, until a number of cycles required for accurate lifetime prediction is achieved;
e) employing an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test;
f) iteratively seeking a lifetime-optimized charging policy according to said hyperparameters by running said recommended tests by repeating c)-e) above one or more times using closed-loop lab testing; and
g) providing the lifetime-optimized charging policy as an output.

2. The method according to claim 1 , wherein said parameter space comprises a number of cycling steps, a cycling time, a state-of-charge (SOC) range, and a boundary on a minimum and maximum current, voltage, resistance and temperature, or temperature, per said cycling step.

3. The method according to claim 1 , wherein said parameter space comprises a multi-step parameter space to optimize formation cycling or charging rate in a series of defined ranges of said SOC within a specified amount of time, wherein each said step controls a percentage of each said SOC range, wherein each said SOC range is independent from the other said SOC ranges, wherein a final said SOC range is a summation of all said SOC ranges prior to said final SOC range.

4. The method according to claim 1 , wherein said resource hyperparameters comprise a number of available testing channels, and a number of batches.

5. The method according to claim 1 , wherein said parameter space hyperparameters comprise a mean and standard deviation of a lifetime across all said policies, and a standard deviation of a single said policy tested multiple times.

6. The method according to claim 1 , wherein said algorithm hyperparameters comprise a degree of similarity between neighboring said policies in said parameter space, an exploration constant to control a balance of exploration versus exploitation, and a decay constant of said exploitation constant per round.

7. The method according to claim 1 , wherein a preliminary set of cells are configured to generate data to develop an early prediction model, quantify a mean, a standard deviation, and a range of lifetime over said parameter space, and quantify an intrinsic cell-to-cell variation for nominally identical cells cycled with nominally identical cycling conditions.

8. The method according to claim 7 wherein the early prediction model is a dynamic early prediction model, wherein said dynamic early prediction model comprises decreasing a size of said preliminary set of cells as more data is collected if a prediction confidence increases.

9. A multi-phase optimal experimental design (OED) method, wherein said multi-phase OED method comprises a first round of closed-loop testing and a second round of closed-loop testing, wherein said first round of closed-loop testing comprises performing a preliminary classification of policies into a low-lifetime policy group or a high-lifetime policy group, wherein quantitative prediction is not required, and wherein said second round of closed-loop testing comprises the method of claim 1 .

10. The method according to claim 1 further comprising multi-cell sampling per charging policy within a test policy round, wherein said multi-cell sampling is directed to one or more cells of interest within said test policy round.

11. The method of claim 1 , wherein the charging policies relate to fast charging.

12. The method of claim 1 , wherein the charging policies relate to battery formation.

13. The method of claim 1 , further comprising validating the lifetime-optimized charging policy by testing multiple batteries to failure according to the lifetime-optimized charging policy.

Assignees

• Univ Leland Stanford Junior

Inventors

• Ermon Stefano
• Chueh William C
• Grover Aditya
• Markov Todor Mihaylov
• Perkins Nicholas
• Attia Peter M

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• H02J7/975

  in response to temperature · CPC title

• H02J7/963

  in response to battery voltage gradient · CPC title

• H02J7/953

  in response to charge current gradient · CPC title

• H02J7/90 Primary

  Regulation of charging or discharging current or voltage · CPC title