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In the right hands, BMS data can be a powerful tool for improving BESS profitability and reliability. The problem is that it is often going unused.
The value of data cannot be overstated. It is integral to any Battery Energy Storage System (BESS).
While there are many reasons for a storage system to generate data, the primary motivations usually revolve around three benefits:
Ironically, most are leaving money on the table.
Data can be used for much more. Industry leaders are realizing that the key to unlocking improved output is to fully leverage data produced by the battery management system (BMS).
The BMS protects battery cells from damage and ensures the optimum performance and safety of the storage system. The typical BMS scans a cell’s voltage, current, and temperature at around 1 kHz, or 1,000 measurements per second. This data is critical and forms the basis for balancing the battery cells.
One of the many challenges is that the data varies widely and highly depends on the system hardware. From the number of temperature sensors installed in a battery module (typically 2-8) to the quality of the DC current sensors (usually between bad and downright horrible) to the performance of the microcontroller (8/16/32-bit), no two battery management systems are alike.
The BMS also calculates abstract values such as state of charge (SOC) and state of health (SOH). The challenge is that the quality of these values is questionable due to limited computing capabilities and a lack of comparable reference data. The system uses rudimentary database lookup tables for state estimations rather than leveraging larger data sets from cloud-based modeling scenarios.
Deviations in SOC accuracy results in money being left on the table, and most BESS are not being used to their full potential.
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Once data is collected from analog sensors on the battery and converted into digital format by the BMS, it progresses to the system level. The data is then transmitted by SCADA (Supervisory Control and Data Acquisition) to the Energy Management System (EMS). Since there is no need to transfer thousands of data points every second, the BMS only passes on a predefined subset of the data to the EMS.
Data transferred varies widely from battery to battery; however, there are two distinct methods:
1) Data point granularity
Some battery management systems pass the values of all installed sensors to the SCADA system. This means one voltage per cell, one current for every parallel string, and all available temperatures. Other systems only pass the total voltage of a battery module and the average, minimum, and maximum cell voltage in that module to the SCADA system. If multiple temperature sensors are installed, the same aggregations may be applied.
2) Data point sampling
A sampling frequency of 1,000 measurements per second is usually unnecessary and impractical for most battery applications. This means the BMS needs to do some time aggregation before data is passed on to the SCADA system. This is commonly done by passing on snapshot values or applying a rolling mean to the data stream—sampled at a lower frequency.
A minimum viable dataset is required for meaningful safety, performance, and aging results. Most battery analytics experts would prefer every battery module's MIN/MAX/AVG values with a sampling rate of no less than one reading per minute. More data always helps, of course, but even seemingly limited amounts of data can go a long way if handled intelligently.
Once all the data is clean and made accessible, the fun part can finally start – at least if you are a battery scientist.
By extracting open-circuit voltages and complex impedances from the field data, it’s possible to build digital twins of the batteries. These models, when combined with cloud-based computing power, can predict safety, performance, and aging.
Extracting the right data from a BMS is not always a simple task, but it’s worth it. Data, and the operational insights it brings, is a differentiator for BESS owners. It’s a critical step towards exceeding revenue targets and getting more value out of a system.
ACCURE helps companies reduce risk, improve performance, and maximize the business value of battery energy storage. Our predictive analytics solution simplifies the complexity of battery data to make batteries safer, more reliable, and more sustainable. By combining cutting-edge artificial intelligence with deep expert knowledge of batteries, we bring a new level of clarity to energy storage. Today, we support customers worldwide, helping optimize the performance and safety of their battery systems. Visit us at accure.net.