The research group Electromobility and Learning Systems (ELS) celebrated a success at this year's IEEE Instrumentation & Measurement Society conference: The conference paper "Adaptive Variance Estimation of Sensor Noise within a Sensor Data Fusion Framework" submitted by Dominik Schneider, Bernhard Liebhart and Prof. Christian Endisch was presented at the IEEE International Instrumentation and Measurement Technology Conference (I²MTC) 2021, where it won the "Best Paper (1st Place)" award. The conference occurred virtually this year due to corona.
The paper describes a method to adaptively estimate the variance of the sensor noise of current sensors. It makes use of the model-based sensor data fusion developed in the project Learning Systems in BMS to obtain a reference value for the variance estimation. The determined characteristics of the sensors serve as input to the sensor data fusion for weighting the individual sensor readings. The method was also compared to conventional methods for determining sensor noise as part of the work.
The adaptive variance estimation will now be applied in the battery management systems for smart battery systems of the ELS research group.