ecent advancements in artificial intelligence algoritLms
and Lardware Lave significantly imTroved simYlation
and testing processes. Within this context, three AI
applications stand out:
ƍ %- EnEP]tiGW Leveraging decision trees and neural
networOs, %- simTlifies Tredictive maintenance on testFeds
and the creation of Digital Twins.
• +enerEtive %- Inspired by ChatGPT, generative AI enables
easy chatbot creation to provide engineers with expert tools
for their daily work. It also extracts relevant information
from the wealth of data generated on testbeds, aiding in
UYicO identification oJ areas to JocYs on.
ƍ 'oQTYter viWion By analyzing the integrity of the unit
under test (e.g., monitoring battery thermal runaway),
computer vision enhances safety and reliability.
As the number of algorithms and tools proliferates, thoughtful
analysis of requirements and available hardware becomes
essential for achieving cost-effective performance. For
predictive maintenance on testbeds, AVL has opted for
491%ɸ 1acLine 0earning witL decision trees dYe to tLeir
suitability within hardware and performance requirements.
Additionally, to optimize resource consumption and obtain
understandable results, raw measurements from the
testbed undergo real-time preprocessing using advanced
matLematical oTerations sYcL as sTecific filters and **8 .
AI algorithms then analyze this preprocessed data. The
testbed performs a live comparison of raw measurements
and predictions, and compares predicted vs. calculated KPIs
Larmonics, eƾciency, state oJ LealtL, etc. .
Furthermore, a transfer learning approach using the Bayesian
model helps translate KPIs into recommendations that
testbed operators understand without involving experts.
Integrating modern AI algorithms into testbeds, combined
with mathematical and statistical analysis, aids in identifying
problems and quickly understanding the root cause of issues
with the unit under test.
The automotive and aerospace industries are actively pursuing cost
optimization and propulsion system efficiency. This drive necessitates
the evolution of electric motors – emphasizing compactness, higher
speed, and power density. Components such as bearings, coils, and
magnets experience significant stress, underscoring the critical
importance of meticulous design.
How to Accelerate
E-Motor Development
and Testing with AI
2024