attery cells are extremely sensitive, requiring extensive,
time-consuming tests during development and
production to ensure safety and performance. In
addition, various cell types, test chambers, temperatures,
and test scenarios must be considered. Many test labs
still work manually, leading to errors and delays. There are
tLree eJJective ways to coYnteract %Ytomated testfield
management, simulation-driven reduction of testing time, and
AI-based data analysis. These strategies increase traceability
and transparency, reduce costs, and shorten time to market.
1. Digitalization and Automation in the Lab
Using digital test orders and automated test procedures
reduces manual tasks, avoids errors, and improves the
quality of results. A comprehensive digital order processing
system with automated planning of the entire battery lab
assigns up to 1000 units under test to the correct test
chambers. Performance monitoring and KPI-driven control
of testbeds also reduces energy consumption. The AVL Lab
Management™ software solution seamlessly integrates
hardware and software, thus optimizing utilization, creating
transparency and traceability.
2. Simulation-Supported Test Optimization
Battery tests are very time-consuming due to slow
electrochemical processes. Our algorithms, developed over
years of collaboration with pilot customers and our own
propulsion development, analyze test data and optimize test
Tougher global competition for battery-electric vehicles requires
a shorter time-to-market for new models. Therefore, as a core
component, batteries must be developed and tested faster while
maintaining higher quality.
TrocedYres. -n Fattery cell aging tests, YT to Ż oJ time can
be saved, as not every test needs to be completed to the end
and can be supplemented by simulation. AI models learn the
dynamic behavior of batteries – which varies with temperature
and state of charge – and thus eliminate unnecessary test
cycles. Real-time simulation on the battery testbed enables
validation and optimization at system level under realistic
driving conditions in the lab (‘road-to-lab’). This reduces the
risk of late, costly changes just before start of production.
3. AI-Driven Evaluation of Test Results
Accurate prediction of the state of charge and the lifetime of
batteries requires in-depth understanding of electrochemical
processes. Fleet data is used to train data-driven models
based on neural networks to predict battery aging. Combined
with the data from cell tests, this can improve the quality of
battery models and further reduce development time. This
requires huge volumes of data to be processed from various
development areas and test environments. Our leading-edge
data analytics platform pools this data and thus serves as a
central source of knowledge.
%:0 Tlaces great emTLasis on JYtYreTroofing, reƽected in oYr
portfolio. Our solutions in the areas of virtual testing, data
analytics and testfield management are scalaFle and ƽe\iFle,
allowing them to adapt to technological changes and pave the
way to a safe and sustainable future.
Strategies for More
Efficient Battery Testing
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