Monolith

Reduce battery test time with AI-guided test. 

Using data-driven AI models, you can better understand and predict battery performance to significantly reduce testing time without compromising coverage or safety.  

 

BMW
BAE Systems
Mercedes
Honda
Siemens
Honeywell
Kautex-Textron
Michelin
Aptar
Jota
ev battery testing monolith ai different ev forms
Optimise EV Battery Testing

 

Accelerate time-to-market by reducing testing by up to 70%

 

Batteries play a pivotal role in the electric vehicle (EV) industry, serving as the lifeblood that powers the shift in consumer adoption. Battery technology determines an EV’s performance, range, and overall appeal to consumers.

The race to develop more efficient, longer-lasting, and cost-effective battery technologies has become a key battleground. OEMs must explore and adopt innovative methods to stay ahead of the curve and maintain their competitive edge.  

AI is a game-changer across the battery engineering process. Learn how you can harness AI to accelerate new product introductions.  

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. 

Design and Sim product overview v2

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.

Instrumentation and Testing tab v2

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. 

Extracting value from data v2

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. 

forrester report press release

 

"If your model is in your data, Monolith will find it. Built by engineers for engineers, Monolith helps you make better models faster." 
Dr. Ted Duclos, Monolith Advisor and Former CTO at Freudenberg Sealing Technologies

 

ted
Why Monolith?

 

Machine Learning to guide your engineering decisions.

 

Batteries drive the cost, performance, and complexity of EVs. Don't risk falling behind with a traditional testing approach. Optimise your test plans with AI to slash time-to-market.

 

✔️ Accurately model and predict battery performance

✔️ Reduce battery validation from months to weeks

✔️ Shorten product development duration significantly

Ensure battery design quality & safety using AI

 

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.

 

Reduce Tests to Run_Kautex_laptop

Predict the critical tests to run


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
  • Optimize resources spent on costly test rigs and facilities
  • Validate your designs faster with fewer prototype iterations 
Battery testing Webinar

 

Battery testing with AI: 
Build a more efficient test plan you can trust

 

In the first part of the EV webinar series, we reviewed the latest research on using AI models to significantly reduce the testing needed for EV batteries.  In this follow-up webinar, we’ll show how to implement these concepts using Monolith software.  
battery testing plan optimization-1
 
Predict Next Tests to Run Using Machine Learning

 

Monolith AI introduces the Next Test Recommender (NTR)

 

  • Engineers can now access ranked validation test recommendations, reducing testing by 30-60% and speeding up time to market
  • With human-in-the-loop active learning powered by AI, domain experts can optimize test pans for complex products in automotive, aerospace, and industrial domains
A commissioned study conducted by Forrester Consulting on behalf of Monolith

The State of AI in Engineering  

The first-ever study on AI in product development surveys US and European automotive, aerospace, and industrial engineering leaders.

forrester report with monolith state of AI in engineering-1
 
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 

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, Honeywell 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 alot 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

 

Recommended resources 

Screenshot 2023-08-10 at 12.49.36
Challenges and Opportunities in Fuel Cell Testing
AI Battery Fuel Cell Testing: Optimising Performance Efficiency

 

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The Role of AI in Automotive Engineering
Monolith's Solution to Battery Testing Problems 
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AI to Test less & learn more.
Robust Active Learning for Next Test Recommendations

Ready to get started with AI?

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Mercedes
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