Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data

2022 ◽  
Vol 48 ◽  
pp. 103856
Author(s):  
Felix Heinrich ◽  
Marco Pruckner
Keyword(s):  
Neural Networks ◽  
State Of Health ◽  
Virtual Experiments ◽  
Vehicle Data

scholarly journals Batteries State of Health Estimation via Efficient Neural Networks with Multiple Channel Charging Profiles

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Noman Khan ◽  
Fath U Min Ullah ◽  
Afnan ◽  
Amin Ullah ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Of Health ◽  
Multiple Channel

State of Health Estimation of Zinc Air Batteries Using Neural Networks

2017 ◽  
pp. 641-647 ◽  
Author(s):  
Andre Loechte ◽  
Daniel Heming ◽  
Klaus T. Kallis ◽  
Peter Gloesekoetter
Keyword(s):  
Neural Networks ◽  
State Of Health ◽  
Zinc Air Batteries

Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks

2012 ◽  
Vol 42 (1) ◽  
pp. 487-494 ◽  
Author(s):  
Akram Eddahech ◽  
Olivier Briat ◽  
Nicolas Bertrand ◽  
Jean-Yves Delétage ◽  
Jean-Michel Vinassa
Keyword(s):  
Neural Networks ◽  
Impedance Spectroscopy ◽  
Health Monitoring ◽  
Recurrent Neural Networks ◽  
State Of Health ◽  
Li Ion Batteries ◽  
Li Ion

scholarly journals Cyberattack detection in vehicles using characteristic functions, artificial neural networks, and visual analysis

2021 ◽  
Vol 20 (4) ◽  
pp. 845-868
Author(s):  
Yannick Chevalier ◽  
Florian Fenzl ◽  
Maxim Kolomeets ◽  
Roland Rieke ◽  
Andrey Chechulin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Autonomous Vehicles ◽  
Visual Analysis ◽  
Characteristic Functions ◽  
Vehicle Data ◽  
Artificial Neural ◽  
Attack Surfaces ◽  
Vehicle Network

The connectivity of autonomous vehicles induces new attack surfaces and thusthe demand for sophisticated cybersecurity management. Thus, it is important to ensure thatin-vehicle network monitoring includes the ability to accurately detect intrusive behavior andanalyze cyberattacks from vehicle data and vehicle logs in a privacy-friendly manner. For thispurpose, we describe and evaluate a method that utilizes characteristic functions and compareit with an approach based on artificial neural networks. Visual analysis of the respective eventstreams complements the evaluation. Although the characteristic functions method is an order ofmagnitude faster, the accuracy of the results obtained is at least comparable to those obtainedwith the artificial neural network. Thus, this method is an interesting option for implementation inin-vehicle embedded systems. An important aspect for the usage of the analysis methods within acybersecurity framework is the explainability of the detection results.

Sign in / Sign up

Export Citation Format

Share Document