Abstract: Process for making a composite oxide according to the formula x·Li2Ni1-y1-y2Mny1M1y2O3·(1?x)·LiNi1-zM2zO2 wherein x is in the range of from 0.01 to 0.5, z is in the range of from zero to 0.5, M1 is selected from Ti, Zr, Sn, Ge, Ta, Nb, Sb, W, and Mo, and combinations of at least two of the foregoing, M2 is at least one of Co, Al, Mg, Fe, or Mn, or a combination of at least two of the foregoing, 0.1?y1?0.75, zero?y2?0.05, said process comprising the following steps: (a) providing a particulate hydroxide, oxide or oxyhydroxide of TM where TM has the general formula x·Ni1-y1-y2Mny1M1y·(1?x)Ni1-zM2z, or the respective species without M1 and/or M2, (b) adding a source of lithium, (c) treating the mixture obtained from step (b) thermally under an atmosphere comprising oxygen in two steps: (c) heating the mixture obtained from step (b) to 680 to 800° C. in an atmosphere containing in the range of from 10 to 100 vol-% oxygen, and, (e) heating the intermediate from step (c) to 450 to 580° C.

Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

Abstract: Process for making a mixed oxide according to the formula Li1+xTM1?xO2 wherein x is in the range of from 0.1 to 0.2 and TM is a combination of elements according to general formula (I) (NiaCobMnc)1-dM1d (I) wherein a is in the range of from 0.30 to 0.38, b being in the range of from zero to 0.05, c being in the range of from 0.60 to 0.70, and d being in the range of from zero to 0.05, M1 is selected from Al, Ti, Zr, W, Mo, Nb, Ta, Mg and combinations of at least two of the forego-ing, a+b+c=1, said process comprising the following steps: (a) providing a particulate hydroxide, oxide or oxyhydroxide of manganese, nickel, and, optionally, at least one of Co and M1, (b) adding a source of lithium, (c) calcining the mixture obtained from step (b) thermally under an atmosphere comprising 0.05 to 5 vol.-% of oxygen at a maximum temperature the range of from 650 to 1000° C.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.