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3 April, 2020 - 06:26 By Tony Quested

Cambridge technology on the charge after battery research boost

Future advanced battery modelling as well as a broad range of lifestyle and industrial processes are set to be fashioned by technology from Cambridge University spin-out Intellegens.

A collaboration between the company, Cambridge University and Singapore duo A*STAR and Nanyang Technological University underlines one highly important aspect of the Cambridge company’s DeepTech capability. The combined research assessed methods for predicting EV battery states and revealed that a data-driven machine learning model offers the most accurate predictions for state of charge and health. That is an Intellegens strong suit.

Dr Gareth Conduit of Cavendish Laboratory and co-founder of Intellegens steered the new research alongside the Institute of Materials Research and Engineering at A*STAR, and Nanyang Technological University. They assessed various machine learning approaches for fast and accurate battery state prediction. 

Demand for electrification of transport has emerged in recent years, due to increasing concerns about global warming. The performance, cost, and safety of batteries determine the successful development of electric vehicles.

Currently, Lithium-ion (Li-ion) batteries are the preferred choice for EVs due to their cycle life and reasonable energy density. However, further research of Li-ion batteries will result in more complicated battery dynamics, where safety and efficiency will become a concern. 

Therefore, an advanced battery management system that can optimise and monitor safety is crucial for the vehicle electrification process.

Machine learning algorithms have been implemented to predict state of health, state of charge and remaining useful life. Data-driven models have attracted attention in recent years and combined with machine learning techniques appear to be more powerful and able to predict without ‘a priori’ knowledge of the system and have the potential to achieve high accuracy with low computational cost. 

With the reduced costs of data storage devices and advancement of computational technologies, data-driven machine learning seems to be the most promising approach for advanced battery modelling in the future.

Dr Conduit said: “The insights in this review article could have a transformative effect on the battery industry. Highlighting how machine learning can accurately predict and improve the health and life of a battery will enable manufacturers to embed this software straight into their battery devices and improve their in-life service for the consumer.” 

Intellegens has developed Alchemite™, a revolutionary suite of software tools that enables organisations to harness the power of deep learning to guide the design of new advanced materials, chemicals, batteries, and drugs, resulting in reduced costs, reduced development cycles and optimised solutions.

Intellegens lays claim to a unique AI toolset that can train deep neural networks from sparse or noisy data. The technique, created at the Cavendish, is  demonstrated in Alchemite™, the first commercial product. 

The innovative deep learning algorithms on which Alchemite is based can spot correlations between all available parameters, both inputs and outputs, in fragmented, corrupt or even noisy datasets. 

The result is accurate models that can predict missing values, find errors and optimise target properties. Capable of working with data that is as little as 0.05 per cent complete, Alchemite™ can unravel data problems that are not accessible to traditional deep learning approaches. 

Suitable for deployment across any kind of numeric dataset, Alchemite™ is delivering ground breaking solutions in drug discovery, advanced materials, patient analytics and predictive maintenance – enabling organisations to break through data analysis bottlenecks, reduce the amount of time and money spent on research and support better, faster decision-making.

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