Due to its versatility, ease of use, and performance, machine learning is being applied to almost all fields of science, including electrical engineering. A particular sub-field of electrical engineering which has seen enormous growth due to the use of machine learning is battery degradation - the analysis of how batteries decrease in effectiveness over time. However, while machine learning tools appear appropriate, as a battery exhibits seemingly non-linear degradation and a complex dependence on external factors, these tools are being applied without much thought, to data which is often not very representative of real operation. In this talk, we will take a look at some of the pitfalls in the data and machine learning tools applied in this field, as well as some simpler or more coherent alternatives.