First developed in the early 1980’s, Battery Monitoring Systems (BMS) have been deployed in data centers of all sizes as a management tool to determine the health performance and increase the lifespan of battery assets. Traditionally, measuring each and every unit in a string of batteries has been seen as the optimal way to capture performance data of backup power systems. While this may have been a reasonable process for data centers 20 years ago, data centers are currently expanding at a rapid rate – requiring more power and, as a result, more battery assets to measure and manage.
Unfortunately, due to the sheer number of batteries in data centers today, the battery monitoring hardware needed to collect all of this data is expensive, and many facility operators can’t justify the required upfront capital expense - despite its benefits. As a result, they must instead rely on the manual readings taken during quarterly battery inspections to determine the health of their assets and estimate the appropriate time to replace them based on disparate numbers from manufacturers, industry experts and vendors.
However, one failure in a single battery string can have disastrous reputational and financial consequences. In addition, batteries can fail in as little as a few days, making quarterly maintenance visits ineffective at best – as batteries will presumably fall below the failed line between checks. While a monitoring system is essential to prevent battery such failure, measuring each unit is no longer the most efficient, cost effective solution, nor is it necessary with today’s technology.
Traditional Node-based vs. String-based Monitoring
Traditional battery monitoring systems include a node or sensor connected to each and every battery in a string, as well as a master control unit and devices that aggregate the data for analysis. Although seemingly necessary, all this hardware has a substantial impact on the cost of the system. In addition, connecting traditional monitoring hardware to every battery in a string results in more connections to infrastructure and more points of failure. The connections can cause operational challenges and generate false readings if not properly maintained. Further, connecting each unit requires granular analysis of thousands of data sets on a regular basis by a facility operator already spread thin.
With a quadrant monitoring system, the measurement unit is transitioned from each battery to the string segment. A master control unit is still required, but the sensor hardware is integrated into a single device that collects performance data across a string of batteries. This results in fewer hardware components and costs up to 40% less than traditional battery monitoring solutions.
With quadrant monitoring, there are only 8 physical connections in any string. This format significantly simplifies operations during installation and reduces the probability that the system will be impacted during maintenance. In addition, the quadrant system collects battery health and performance data across an aggregated group of batteries within the string. To make data collection most efficient and effective, the string is broken into quadrants and measurements are taken for each string quadrant.
Making a Case for Quadrant Technology
With the rising cost of downtime and new thermal runaway regulations, facility operators are facing mounting pressure to identify potential threats in advance and shift from a reactive to proactive approach to reduce hardware costs and prevent unplanned downtime.
Facility operators must understand the available battery monitoring technologies in order to make the best business decision when purchasing and deploying a BMS. While these are a few examples of fundamental feature differences between a traditional node-based system and a string quadrant monitoring system, our latest white paper takes an in-depth examination of system hardware, performance metrics, thermal runaway detection, management and maintenance and monitoring service and predictive analytics features of both systems.
Download the white paper for free here.