Battery performance has been a big concern, as batteries are widely deployed in stationary, mobility, and maritime applications as part of the energy transition. This performance is affected by many operational factors, which makes monitoring the accurate battery state of health, a key parameter reflecting available capacity, a challenge for battery asset management.
The failure of a system in service could have negative or dangerous consequences. Battery state of health is normally determined by offline and time-consuming testing, but this is not an optimum solution as the testing cannot always replicate real operating conditions and battery variability.
Online battery health monitoring solution
Battery AI 2.0 is a major upgrade and expansion of Battery AI 1.0, which focused on the development of the battery degradation analysis tool.
By utilizing artificial intelligence to learn from more than one million channel-hours of lab testing data, Battery AI 2.0 is an effective tool that can determine the relationship between operating conditions and battery degradation and provides a bespoke model for battery state of health estimation.
The combination of AI modelling and cycle counting method also provides a practical solution to efficiently and accurately access the actual battery state of health under intended operating conditions.
Using a real-time data stream from physical operating systems, the solution can also be used to provide online health monitoring and residual useful life prediction. This permits relevant stakeholders to know the exact performance of the battery asset and thus optimize their asset management in an informed manner.