Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles
Jakob Pfeiffer, Xuyi Wu · 2019
Deviations between electric current measurements and reality can cause severe problems in the power train of electric vehicles (EVs). Among others, these are unnecessary power limitations and inaccurate performance coordination during driving or charging. One reason for these deviations are time delays. In this paper, we present three different approaches for time delay estimation (TDE) and evaluate these with real data from power trains of EVs. Besides the accuracy of the TDE, the focus of our evaluation lies on the computational efficiency to enable an execution on automotive electronic control units (ECUs). The Linear Regression approach suffers even from small noise and offsets in the measurement data and is unsuited for our purpose. A better alternative is the Variance Minimization approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filters presented by Emadzadeh et al. Unfortunately, Adaptive Filters do not reach the accuracy and efficiency of Variance Minimization in our experiments. Thus, we recommend Variance Minimization for TDE of current signals in the power train of EVs.