Advances in computational algorithms and the significant increase in computational resources currently available has led to the new and exciting field of Artificial Intelligence, and Machine Learning in particular. This has reduced the need to perform a large number of experiments in order to obtain the optimized process parameters. For example, consider a scenario where the manufacturing and assembly of a battery component is dependent on more than a dozen parameters. Using Machine Learning (ML) algorithms, such as Gaussian Processes, the situation can be expressed as:
ŷ = ƒ(x1,x2,.…….,xn)
Where, ŷ is the characteristic of the battery or battery component we need to optimize (e.g., specific energy density) and xi are different composition and process parameters (e.g., type and amount of precursor for the cathode material, heat treatment temperature, thickness of the cathode tape, charge/discharge rates), and ƒ is a function of xi. The ML algorithms can determine the shape of ƒ with a limited number of experiments (called training set) so that ƒ can then be used to predict the value of the desired characteristic ŷ with given inputs. Machine Learning can also be used to determine which parameters are more important (and need to be observed more closely during manufacturing) and which parameters are less important.
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The Machine Learning algorithm is applicable to both battery materials and assembled cells. The technology can also be applied to Coating Optimization. Contact us today using the form below to explore how the ML approach can be adapted for your specific need in a cost effective manner.