Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters

The time-varying nature of consumption patterns is critical in the development of reliable electric vehicles and real-time schemes for assessing energy autonomy. Most of these schemes use battery voltage observations as a primary source of information and neglect variables external to the vehicle that affect its autonomy and help to characterise the behaviour of the battery as main energy storage device. Using an electric bicycle as case study, we show that the incorporation of external variables (e.g., altitude measurements) improves predictions associated with evolution of the battery voltage in time. We achieve this by proposing a novel kernel adaptive filter for multiple inputs and with a data-dependent dictionary construction. This allows us to model the dependency between battery voltage and altitude variations in a sequential manner. The proposed methodology combines automatic discovery of the relationship between voltage and altitude from data, and a kernel-based voltage predictor to address an important issue in reliability of electric vehicles. The proposed method is validated against a standard kernel adaptive filter, fixed linear filters and adaptive linear filters as baselines on the short- and long-term prediction of real-world battery voltage data.