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Role of Big Battery Data in Lithium-Ion Battery Asset Management – ACCURE Battery Intelligence

Role of big battery data in lithium-ion battery asset management – ACCURE Battery Intelligence.

Leveraging big data in lithium-ion battery asset management can reduce safety risks, save money, and extend battery life, but all big data comes with challenges.

Big Data is a widespread term in many sectors, and it has also reached battery engineering. But what does Big Data mean and how is it relevant for lithium-ion batteries?

To understand what makes drum data “big” and how you can take advantage of it, it helps to examine the commonly used definition of big data through the five Vs: volume, variety, velocity, value, and veracity. Each of the Vs highlights a particular aspect of Big Data and helps explain the challenges faced when managing Big Data.

Volume: It all starts with BMS data

Volume is the simplest V to describe Big Data, because it’s all about size. According to the definition of Big Data, it starts from terabytes (1 terabyte or TB equals 1000 gigabytes) and goes up to petabytes. A petabyte (PB) equals 1,000 terabytes.

In the battery space, the data volume is generated by battery management systems (BMS). The volume of data generated by a single BMS is small and does not fall within the scope of Big Data. However, when we start collecting historical BMS data, we easily get into the terabyte range of data volume.

Depending on the application and complexity of the battery system, a given system may have multiple subsystems (modules in most cases) that send data to a central collection unit.

For example, a home PV storage system might have 1 to 4 modules (5 kWh to 15 kWh) sending data, whereas a large grid-scale battery storage unit might have up to 6 to 6000 modules (50 kWh to 500 MWh) sending data continuously. Thus, in addition to collecting data and storing historical data, increasing system size and complexity can lead to a rapid increase in data volume.

Just storing this amount of data can be a challenge for owners and operators of battery systems, as well as public transport authorities with growing electric fleets. It is important to consider the data pipeline for battery data whenever a new battery or electric vehicle (EV) is purchased and deployed.

These are the tools and processes you will need to automate the movement and storage of BMS data and the transformation of data between the source system and your target database.

PRO TIP: A common solution is to use cloud storage and compute, which allows for near limitless scalability.

Variety: the essence of big battery data

Having a high volume of data is hard enough, but having variety in that data increases complexity. Available data and resolution are highly dependent on the application in which the battery is used.

A home storage system can provide current, voltage, and power supplied by the solar system. An electric bus would provide speed, requested power, voltage and current.

When it comes to resolution, variety is also important, ranging from resolutions of 1-5 minutes in home storage systems to 1 second or less in EV applications, depending on the signal. The data produced by the BMS also depends on the manufacturer of the module and the integration of the module into the larger battery system.

Keeping this variety of data in mind during the design phase of a data pipeline will reduce painful workarounds later when new types of battery systems are introduced. A useful concept and technology to consider here are the data lake, a non-relational data storage solution. Non-relational storage offers greater flexibility than traditional relational databases by accepting different data formats.

Velocity: As fast as the computing power can handle

Speed ​​factor or speed and with it real-time or near real-time analysis is something that was traditionally handled on the battery management system itself. No one likes to wait for results, but many processes in lithium-ion battery systems require immediate calculation.

In particular, battery safety algorithms have become more computationally intensive, making cloud computing a necessary complement to embedded BMS algorithms in order to maintain speed and avoid critical failures.

High-speed data management in turn influences design decisions on the cloud backend, such as whether to opt for batch processing or event-based data processing.

Batch processing allows programmed computation of large data sets, but is not always suitable for real-time or near real-time analyses. Event-based data processing is often a good option for time-sensitive analyses. This is the key to finding the right balance.

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The Role of Big Battery Data in Lithium-Ion Battery Asset Management, November 17, 2022