Remote Monitoring and Control of Lithium Batteries
Lithium batteries are the beating heart of electric industrial machines and vehicles. Incorrect use or a fault in their operation can lead to costly repairs and machine downtime.
But why wait for machine downtime when we can take action in advance? The answer lies in predictive maintenance, an approach which means we are not limited to just reacting to issues, instead we can take action in advance through the smart collection and processing of data.
At Flash Battery we have developed the Flash Data Center internally, an advanced remote monitoring system that analyses the health and correct use of the batteries installed all over the world on a daily basis. Thanks to predictive maintenance, users are alerted to potential faults before they occur, significantly reducing machine downtime and maintenance costs.
Flash Data Center: proprietary technology for advanced remote monitoring of Lithium Batteries
The Flash Data Center is the Flash Battery proprietary cloud-based remote monitoring system which uses artificial intelligence and machine learning to analyse the data collected from every Flash Battery lithium battery operating all over the world.
The main feature of the Flash Data Center is its automatic daily monitoring of operating data from connected batteries, 24/7, that allows potential issues to be identified and nipped in the bud thanks to predictive maintenance and Over the Air updates.
Predictive maintenance not only allows action to be taken in advance of product faults, it also gives the option of identifying particular features of normal use or incorrect behaviours which, if repeated over time, could damage the battery and the vehicle itself.
Machine Learning and Monitoring: automatic learning that makes the difference
Since 2013, our Flash Data Center remote monitoring system has collected a significant amount of data from in-service battery systems. Today, we have more than 200 million logs collected from our batteries all over the world and around 160,000 new logs are generated every day, giving us data which are a unique asset in the sector.
This huge volume of Big Data would be impossible to analyse manually, which is why our remote monitoring system features the latest machine learning and artificial intelligence algorithms that provide us with ever-more accurate processing and analysis of this information.
While many actors in the market are still building their databases, we are able to use our data for predictive purposes, accurately identifying the health of the batteries and sending proactive signals about any critical issues.
Through a continuous training process, the system receives information about the performance of the batteries analysed by our experts technicians, meaning that the algorithm is able to send us ever-more accurate alerts about potentially problematic batteries, with greater scalability of data analysis.
We are currently working to make the Flash Data Center an even smarter tool which can offer innovative solutions to our customers for remotely monitoring and managing their batteries.
Digital Twin: AI-driven control for comprehensive battery performance management
The electrochemistry of a lithium battery is a very complex system: with a very long service life and countless different user profiles, it is impossible to perform laboratory tests of every battery’s real-world operating conditions, as they vary according to the individual application and end-user. It would take years to produce an accurate mapping, meaning it would not be possible to keep pace with the state-of-the-art in cell technology, which evolves very quickly.
To overcome this limitation, Flash Battery has chosen to invest constantly in the latest artificial intelligence algorithms, focusing on predictability.
By using the operating data collected by our Flash Data Center after an initial period of use, the AI generates a digital twin of each battery, in other words a virtual replica which can simulate the behaviour of the battery over the coming years, thereby estimating its expected service life.
This means that, in the most stressful situations, prior action can be taken on the machine’s parameters or settings to improve the expected service life of the machine.
The advantages of intelligent remote battery monitoring for industrial applications
Artificial intelligence applied to remote monitoring not only helps to anticipate faults before they occur and thus avoid costly machine downtime, it also results in a real overall optimisation of use in industrial applications.
Thanks to remote monitoring and predictive data analysis, the Flash Data Center allows you to:
- Size the battery appropriately for the vehicle (battery properties, energy, performance, etc.)
- Predict how the battery will behave in the future
- Refine the machines’ performance levels, increasing their productivity through FOTA updates (Firmware Over The Air)
- Verify actual use of the vehicle in service
- Manage advanced planning of unscheduled maintenance
- Plan the end of life and future replacement
Flash Data Center: the intuitive dashboard for easily accessing your battery data at any time
The graphical interface of the Flash Data Center ensures simple and instant access to the data, providing an interactive and intuitive user experience. The heart of the portal is the dashboard which offers a comprehensive overview of the batteries purchased, including the number of connected units, charge states, temperature, and health. The system also allows users to create fleets of machines or vehicles to provide simplified reporting that is easily comparable at-a-glance.
Thanks to its interactive structure, the dashboard delivers fluid navigation through the data, allowing users to quickly access the details of every single system at any time in just a few clicks.
“The Flash Data Center was created to deal with the complexity of modern batteries, transforming it into a strategic advantage. Thanks to machine learning and the unique asset of the data collected since 2013, with more than 200 million logs analysed, we are able to transform this complexity into a clear and detailed description of the batteries’ performance. This level of monitoring, which would once have been unimaginable, allows users to anticipate faults and ensure a reliable and proactive monitoring of their Flash Battery batteries, differentiating them from all of the others on the market”.
Antonio Gabriele | Chief Information Officer at Flash Battery
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