Next Article in Journal
Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids
Next Article in Special Issue
Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture
Previous Article in Journal
Geometrical Synthesis of Sparse Antenna Arrays Using Compressive Sensing for 5G IoT Applications
Open AccessArticle

Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles

by 1,2,*, 2 and 2
1
BMW Group, Petuelring 130, 80788 Munich, Germany
2
Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 351; https://doi.org/10.3390/s20020351
Received: 3 December 2019 / Revised: 24 December 2019 / Accepted: 26 December 2019 / Published: 8 January 2020
Deviations between High Voltage (HV) current measurements and the corresponding real values provoke serious problems in the power trains of Electric Vehicle (EVs). Examples for these problems have inaccurate performance coordinations and unnecessary power limitations during driving or charging. The main reason for the deviations are time delays. By correcting these delays with accurate Time Delay Estimation (TDE), our data shows that we can reduce the measurement deviations from 25% of the maximum current to below 5%. In this paper, we present three different approaches for TDE. We evaluate all approaches with real data from power trains of EVs. To enable an execution on automotive Electronic Control Unit (ECUs), the focus of our evaluation lies not only on the accuracy of the TDE, but also on the computational efficiency. The proposed Linear Regression (LR) 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 (VM) approach. It is not only more noise-resistant but also very efficient after the first execution. Another interesting approach are Adaptive Filter (AFs), introduced by Emadzadeh et al. Unfortunately, AFs do not reach the accuracy and efficiency of VM in our experiments. Thus, we recommend VM for TDE of HV current signals in the power train of EVs and present an additional optimization to enable its execution on ECUs. View Full-Text
Keywords: automotive; current; electric power train; electric vehicle; embedded systems; delay; detection; distributed systems; measurements; power train; sensor; signals; time delay estimation automotive; current; electric power train; electric vehicle; embedded systems; delay; detection; distributed systems; measurements; power train; sensor; signals; time delay estimation
Show Figures

Figure 1

MDPI and ACS Style

Pfeiffer, J.; Wu, X.; Ayadi, A. Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles. Sensors 2020, 20, 351. https://doi.org/10.3390/s20020351

AMA Style

Pfeiffer J, Wu X, Ayadi A. Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles. Sensors. 2020; 20(2):351. https://doi.org/10.3390/s20020351

Chicago/Turabian Style

Pfeiffer, Jakob; Wu, Xuyi; Ayadi, Ahmed. 2020. "Evaluation of Three Different Approaches for Automated Time Delay Estimation for Distributed Sensor Systems of Electric Vehicles" Sensors 20, no. 2: 351. https://doi.org/10.3390/s20020351

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop