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  • Technology
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    • All Featured Applications
  • Products & Services
    • Alchemite™ Analytics
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    • Ichnite™
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    • All Upcoming Events
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Batteries

Improve key battery metrics and optimise performance

KEY BENEFITS

  • Design better, less toxic, battery materials and chemicals
  • Optimise battery packs, trading-off the many design variables
  • Ensure efficient scale-up and reduce experimental and prototype costs
  • Enable battery management systems

RELATED TOPICS

  • Materials design 
  • Chemistry and chemical processes
  • Manufactured products

Innovation in electric vehicles and energy storage from renewable sources are two of the key drivers in the rapid growth of a battery industry that needs to improve performance of battery systems with great urgency. Charging capability, energy density, and costs will all need to improve dramatically.

Machine learning can help, exploiting data to assist the discovery of new battery materials and chemistries, design of improved battery packs, and optimisation of battery management systems. However, in a rapidly-evolving field, this data is often sparse and noisy, requiring the unique capabilities of the Alchemite™ deep learning software.

White paper

Machine learning for battery optimisation

DOWNLOAD WHITE PAPER

Example projects

Optimising production processes and battery chemistry

In a project with industry and academic partners, Intellegens technology is being applied to optimise the battery production process. Currently, improvements in this process are done by trial-and-error, with a large matrix of experiments. Use of Alchemite™ deep learning software to predict the change in performance of an electrode from changes in the manufacturing processes is helping to focus this experiment and reduce the development time for new battery chemistries.

MORE ON THIS PROJECT
batteries

Data-driven machine learning for battery management

A collaboration between the University of Cambridge, A*STAR and Nanyang Technological University in Singapore, assessed methods for predicting electric vehicle battery states and revealed that a data-driven machine learning model offers the most accurate predictions for state of charge and health. The project highlighted how machine learning can accurately predict and improve the health and life of a battery, with the potential for manufacturers to embed these methods into their battery devices, improving in-life service for the consumer.

MORE ON THIS PROJECT
electric vehicle

Article: Battery R&D - the rise of machine learning

BEST magazine speaks to Intellegens CTO, Dr Gareth Conduit.

READ THE ARTICLE
AI for Battery Industry
Alchemite Analytics analysis of data

Alchemite™ for batteries

  • Design testing programs to achieve objectives with the fewest experiments or prototypes
  • Propose new battery materials or chemistries
  • Trade-off size, weight, power, charge speed, lifetime, etc. in designing battery packs
  • Select and optimise process parameters to improve battery manufacturing
  • Inform control systems for battery management, including state of charge and safety monitoring
  • Create battery models that can be shared to support collaborative R&D
PRODUCT INFORMATION

Company number : 10591395 | Vat number: 267525774
Address: Eagle Labs, 28 Chesterton Road, Cambridge, United Kingdom, CB4 3AZ
Copyright 2021 Intellegens Limited - All rights reserved