HomeTechnologyTechnology FocusAI-Based System That Resolve Defects In Lithium-Ion Batteries

AI-Based System That Resolve Defects In Lithium-Ion Batteries

The development of AI driven hardware system helps to significantly reduce the battery ‘Turn Around Time’ (TAT)

In the current scenario where electric vehicles (EVs) are gradually becoming popular as a viable eco-friendly mode of transportation, the problem of lithium battery repair presents a challenging task in the market as the supply chains are not well established. Electric vehicle customers are reliant on EV OEMs who, in turn, are reliant on battery manufacturers.

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Therefore any innovation that assures current and prospective customers and stakeholders on a low turnaround time will significantly encourage them to continue to adopt this emerging technology. 

To solve this challenge, researchers at the IIT Hyderabad incubated startup Pure EV have designed Artificial Neural Network (ANN)-based algorithms for a system called BaTRics Faraday. The algorithms help identify battery cell defects and rectify the damage. The process is fully automated by the hardware and requires no manual intervention. 

Highlighting the issue, Dr Nishanth Dongari, founder, Pure EV and associate professor, Department of Mechanical and Aerospace Engineering, IIT Hyderabad said, “Lithium batteries are the most critical component of electric vehicles. They contain multiple lithium cells welded together in series and parallel arrangements to meet the desired voltage and ampere-hour (Ah) capacity. In case of any defects coming to batteries in any of the cell series, it leads to significant downtime to the EV owners. Additionally, the usage behaviour pattern and Indian environmental conditions put more load on the batteries. It is, indeed, a very difficult task for battery OEM to get the defects rectified through diagnosis and replacement of defective series of cells.” 

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“Hence the need of the hour is to develop an external intelligent hardware device which resolves battery defects through an external healing process. PURE EV has developed AI-driven hardware which carries out the diagnosis and resolution of defects in the battery through an externally-connected device to the battery. This saves precious man-hours otherwise spent in replacement of a series of cells and significantly reduces the battery ‘Turn Around Time’ (TAT),” added Dr Dongari.

According to the start-up, extensive field testing of ‘BaTRics Faraday’ has already been completed and is suitable for use in the two-wheeler models launched by Pure EV. Plans are to establish high-end ‘Battery Diagnostics and Repair’ workshops at major demand centres across India, which will ensure a significantly lower TAT for battery repair. The system will be launched in the first quarter of 2021.

Pure EV has an in-house battery manufacturing facility and a research setup based out of IIT Hyderabad campus where the company’s dedicated R&D team works on core areas of battery thermal management system for development of long-range and high-performance lithium batteries.


Vinay Prabhakar Minj
Vinay Prabhakar Minj
Vinay Prabhakar Minj is a technology writer and science communication specialist with a Master’s degree in Communication of Science and Innovation (Science Communication). He is a prolific contributor to Electronics For You, where he has authored over 1,000 articles covering electronics, semiconductors, embedded systems, IoT, and emerging technologies. With a strong foundation in science communication, Vinay focuses on translating complex engineering concepts into clear, accessible, and application-oriented content. His work spans topics such as sensor technologies, chip design, wireless systems, and next-generation electronics, making advanced innovations easier to understand for engineers, students, and industry professionals. Through his extensive contributions, he has built a reputation for delivering reliable, well-researched, and practical insights that help readers stay updated with the rapidly evolving electronics ecosystem. His writing bridges the gap between technical depth and real-world usability, supporting both learning and decision-making in the field.

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