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AI for battery validation

Cut battery test time in half

 

Use data-driven AI models to quickly understand and predict battery performance. Reduce testing time without compromising coverage or safety.  


Test less. Learn more. Get to market faster.

BMW
BAE Systems
Mercedes
Honda
Siemens
Kautex-Textron
Michelin
Aptar
Jota
Accelerate EV battery development with machine learning

Reduce validation testing time and effort. Get to market faster.

Electrification is driving industry disruption in automotive and mobility. Huge investments to develop longer-lasting, faster charging, lower-cost battery technologies are in flight. Traditional simulation cannot accurately model battery performance. OEMs and cell manufacturers are forced to rely on physical testing - increasing costs, schedule delays, and uncertainty. Battery development requires a different approach to stay competitive. Learn how AI and machine learning can reduce battery validation and speed up new product development. Can you afford not to? 

customer service
Optimise complex battery test plans 
root-cause analyiss
Find more errors in your test data faster
PTO
Forecast results to stop long-running tests early 

Today's challenges of battery testing


Test too much and you waste time confirming what you already know. Test too little and you risk missing performance issues. Schedule, quality, and your career depend on finding the balance.

 

  • Run the most important tests and skip the rest
  • Optimise resources spent on costly test rigs and facilities
  • Validate your designs faster with fewer prototype iterations 

Build machine learning models of your battery designs from test data for a simpler, more accurate model than traditional simulation.  Self-learning models built using test data can be more accurate than ECM models and less complex than P2D models.  With the interactive tools in Monolith, you can quickly gain a deep understanding of your design, make predictions for how your battery will react under different conditions, and optimise parameter values for key use cases. 

Battery testing

Understand which design parameters and operating conditions have the greatest impact on battery performance and lifetime.  Apply model evaluation tools in Monolith to visually and quantitatively compare the accuracy of different modelling algorithms to find the best model. Build understanding and confidence in your model with sensitivity analysis and explainable AI functions to guide you on where to focus to improve battery performance.

explainable ai self learning models monolith

Use machine learning models to predict test outcomes for a wide range of conditions ahead of time.  Use prediction tools to exercise your design with different input values and instantaneously see the results.  Use interactively sliders to vary inputs and see how scalar and curve outputs are impacted.  Make more educated decisions on which parameters require more rigorous testing, or which values can be removed from the test plan altogether. 

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Apply active learning techniques to create more efficient test plans. Achieve the same test coverage in fewer steps, or test more conditions in the same amount of time.  Apply Monolith's proprietary test plan optimization tools to explore your battery design space and get recommendations for the most important values for your test plan.  Reorder your test plan or remove unnecessary test steps to find the most efficient test plan without jeopardizing coverage. 

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After an extensive evaluation, we found Monolith to be an excellent option for scaling AI across our R&D.

-Dr. Markus Meiler, VP Research & Development Battery

The intractable physics of EV battery validation requires a new approach to validation – machine learning.

 

Model capacity fade to 99% accuracy with 50% fewer tests.
Reduce the number of ageing tests by 40% while maintaining test coverage.
Identity 75% of cell testing repetitions that are redundant.
Identify optimal charging protocol to maximise battery lifetime in 73% fewer tests.
Find more than 95% of known anomalies in battery test data using automated tools.
 Reduce exhaustive battery DCIR testing protocol to the simplest, minimal test plan.
On-demand webinar library

Learn how you can reduce battery testing efforts with AI tools designed for engineering with Monolith

Forrester Industry Webinar 2024
Industry Panel: AI in EV battery validation
Watch now
About Energy Webinar
Understanding battery degradation with AI and test data
Watch now
Reduce battery test time and cost
Reducing EV battery testing by 70%: How AI solves 3 key obstacles
Watch now
Webinar - Battery testing with AI_ Build a more efficient test plan you can trust
Battery testing with AI: Build a more efficient test plan you can trust
Watch now

Ready to reduce battery testing efforts with AI?

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Speak to our team
Accelerate EV Battery development with ai guided test
White paper: EV battery development

Accelerate EV battery development
With AI-guided test

 

Engineers can circumvent many of the challenges of traditional battery testing to get new products to market faster and with greater confidence, using AI-based self-learning models.

 

 
No-code AI software

By engineers. For engineers.

 

  • Avoid wasted tests due to faulty data
  • Build just the right test plan - no more, no less 
  • Understand what drives product performance and failure
  • Calibrate non-linear systems for any condition 

Ensure battery design quality & safety using AI

 

Let machine learning guide your testing


With the power of AI, you can model battery performance across the design space with a fraction of a traditional test plan. Using the Next Test Recommender, you can apply multiple machine learning algorithms at once to chart your testing path using the fewest steps possible incrementally.
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It is imperative to get to market faster.


You need to get to market faster with revolutionary new EV batteries but you can't rely on your current methods of physical testing and simulation. 

In our 2024 study, 58% say AI is crucial to stay competitive. Here are other highlights:

 

  • 64% believe it's urgent to reduce battery validation  
  • 66% say it's imperative to reduce physical testing
  • 62% indicate virtual tools don't fully ensure battery designs meet validation requirements
*A commissioned study conducted by Forrester Consulting on behalf of Monolith
Download study

EV battery media coverage

ee times
March 13, 2024

Trying out AI-powered EV battery modelling

 

 

 

 

Read now
automotive world pr-1
January 15, 2024

AI advances battery validation

 

 

 

 

Read now
the engineer monolith ai pr-1-2-2
January 3, 2024

The growing challenge of battery testing on time and on cost

 

Read now

Battery testing FAQs

My engineers are busy. Why risk trying Monolith over another AI tool?

Monolith is the only AI platform built by engineers for engineers and trusted by the world’s top engineering teams including BMW, Siemens and BAE. Its advanced capabilities analyse complex engineering data sets quickly and accurately to help you find relationships, new insights and anomalies so you can trust your data fast. With its user-friendly, no-code interface, domain experts can quickly get up to speed and leverage institutional knowledge for future generations.

Learn more

 

 
How is Monolith better than current statistical and simulation methods?

There are five ways Monolith delivers superior performance to statistical and simulation methods. 

  1. Increased accuracy: advanced machine learning algorithms identify complex patterns and relationships in engineering datasets for more accurate predictions and insights
  2. Faster analysis: algorithms are optimised for speed, allowing it to analyse large datasets much more quickly
  3. More comprehensive analysis: analyse both structured and unstructured data
  4. Adaptive learning: continually refine and improve the accuracy of predictions
  5. Ease of use: no code environment built by engineers for engineers

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What if I don't have a lot of data or I'm modeling a new product?

Most often, you need less data than you think! 100s of rows and a handful of columns for a tabular data approach can already get you going on your journey to adopt AI. If you don't have enough data for an AI project, we recommend the following: 

  1. Data augmentation: Create new training examples from existing data by applying transformations or scaling to existing data (this is how our customer Kautex Textron did it).
  2. Transfer learning: Use and reuse data gathered from an old product to improve a new product.
  3. Consider a simpler model: A simpler model requires less data to train. The best approach will depend on the specific task and constraints of your project.

Our mantra: Start small - but start! Find a small, incremental quick win, and let our AI team of experts guide you on your way forward to intelligently acquire more data and build even more impactful models.

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How will having a model in Monolith help me reduce design cycles?

Monolith reduces design cycles by offering rapid analysis, accurate predictions, custom models, collaboration, and optimization capabilities. Its algorithms are optimized for speed, enabling quick iteration and testing of multiple scenarios. Custom models capture domain-specific knowledge, while collaboration and optimization capabilities improve effectiveness and identify the best design options. By leveraging Monolith's capabilities, teams can deliver better designs more efficiently.

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Does my team need to start from scratch once an AI model is built?

Monolith's self-learning models automate repetitive or time-consuming tasks, allowing teams to focus on more complex work. However, data quality, model drift, and interpretation are critical considerations, all supported by Monolith. Teams may need to update or retrain models to account for changes and understand their decision-making processes. By monitoring and managing these factors, teams can ensure ongoing value from Monolith's AI models.


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Learn more about Monolith

 

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