This notebook explores the State of Charge (SoC) estimation of a battery using a state observer algorithm, the Kalman filter, or more precisely its nonlinear extension: the extended Kalman filter (EKF). The notebook provides three Python implementations of the Kalman filter: 1. a step-by-step literate programming version of the filter, using a sequence of notebook cells, to implement one step of the filter, 2. a generic implementation (all the above steps wrapped in a single function) and 3. a compact implementation specialized for SoC estimation with baked-in battery model. Source notebook is available as supplementary material of this record or as an interactive version at https://github.com/pierre-haessig/pierre-notebooks.