<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/114A8800-87BB-49C2-A58F-0CD8A3C4BF9D" ns1:id="114A8800-87BB-49C2-A58F-0CD8A3C4BF9D"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/A7E35E55-1C21-47A6-AB3C-D457CB42F125" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/1378534F-0C40-44DC-9FE3-C7BCBFEB0B66" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EC9326B5-50D8-459F-9932-1D39B5055FC9" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/1378534F-0C40-44DC-9FE3-C7BCBFEB0B66" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2025-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/D7E4AC35-F882-4619-993E-968E0D80C049" ns1:rel="FUND" ns1:start="2024-01-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10081877</ns2:identifier></ns2:identifiers><ns2:title>Breathe Life: a Physics Enhanced Battery Life Controller</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>There is an industry need for accurate prediction of battery lifetime under different operating conditions to optimise battery pack design and battery management. Electric vehicle(EV) batteries typically degrade due to temperature, cycles and time. Continuous cycles of use and charging damages EV batteries' ability to maintain optimum range, power and storage over time.

Breathe Battery Technologies has created a market leading battery-charging product in charge time optimisation and degradation minimisation. On this project, we will develop a physics-based approach to predict battery lifetime with quantified uncertainties within a significantly shorter testing timeframe than competitive offerings.

We will also introduce a new battery life-control algorithm to further minimise lifetime uncertainties from cell and usages variations. This software will complement our existing charge-control software. It will meet the need and challenge of battery life extension/reduced degradation by actively controlling battery performance at a reduced charge time.

Using the results of research done by Imperial College London as part of the Faraday Institution Multi-Scale Modelling project, we will establish a parameterisation framework using 1) a matrix of 2-3 week degradation experiments conducted at different charge-control settings, combined with 2) sensitive electrochemical measurements to probe degradation kinetics without requiring significant capacity and power fades to occur in degradation experiments. We will significantly reduce time and data requirements for making lifetime predictions.

Our existing health-adaptive charging software already demonstrates industry-leading charge time reduction(proven via customers). Developments on this project will further enhance battery lifetime via improved, model-based selection of initial charge-control settings and an active real-time life control algorithm to optimise battery lifetime to industry targets(15-20-years).

By project end, our new charge-control software will be validated as integrated into existing charge products as a prototype feature. Following the project, control algorithms will be validated in representative automotive environments for swift market entry following verification tests on different cell types/conditions and system completion.</ns2:abstractText></ns2:project>