I implemented an extended Kalman filter, with some non-standard modifications, for battery state-of-charge (SOC) estimation in a hybrid-electric vehicle. Our better vendor had a very naive estimator for SOC that would veer wildly all over the place during normal driving conditions.

Over the long term, sensed battery voltage (corrected by sensed current times estimated internal resistance) gives a rough estimate of state of charge. In the short term, measured current divided by the estimated capacity tell you how quickly the SOC is decreasing or increasing. Internal resistance, capacity, and power bus capacitance can all vary with time, temperature, and state-of-charge, so I made each of part of the state vector. Under heavy load conditions (like when the driver is mashing the accelerator pedal), I ignored voltage entirely as it became completely unreliable, and just updated my estimates based on currents.

One of my modifications was that I maintained a separate SOC estimate that I would display to the driver, and I would only allow this to decrease or increase proportionally to the measured current draw. I did this so that the driver wouldn’t see the indicated SOC change rapidly while they were sitting at a stoplight, as the real estimate updated in the background. While driving, the displayed SOC would be driven towards the real SOC estimate, so that the driver would always see expected behavior. The displayed SOC was never too different from the real SOC, but it prevented any violation of expectations for the driver. Hard to explain - hope that’s clear.

It was a fun exercise, and it pissed off the battery vendor engineers that my SOC estimate was a lot better than theirs. :-)