Sandia National Laboratories has conducted a sequence of studies on the performance of lithium ion and other types of electrochemical cells using inductive models. The objectives of some of these investigations are: (1) to develop procedures to rapidly determine performance degradation rates while these cells undergo life tests; (2) to model cell voltage and capacity in order to simulate cell output under variable load and temperature conditions; (3) to model rechargeable battery degradation under conditions of cyclic charge/discharge, and many others. Among the uses for the models are: (1) to enable efficient predictions of battery life; (2) to characterize system behavior. Inductive models seek to characterize system behavior using experimentally or analytically obtained data in an efficient and robust framework that does not require phenomenological development. There are certain advantages to this. Among these advantages is the ability to avoid making measurements of hard to determine physical parameters or having to understand cell processes sufficiently to write mathematical functions describing their behavior. We have used artificial neural networks (ANNs) for inductive modeling, along with ancillary mathematical tools to improve their accuracy. This paper summarizes efforts to use inductive tools for cell and battery modeling. Examples of numerical results are presented.