Vigilant Battery Management System (BMS) with advanced multi-function (AMF) sensors employs several new battery parameters to predict battery condition. The system from Eagle Eye Power Solutions tracks battery cell condition, battery state of health and battery (at) risk factors, providing new capabilities made possible by machine learning algorithms built into the Vigilant.
Source: Eagle Eye Power SolutionsThe BMS monitors and records string voltage, float current, cell voltage, cell resistance, terminal and connection resistance, cell and ambient temperature, DC ground fault and electrolyte level. A key advantage of the Vigilant is how it processes measurement data, as it uses artificial intelligence to calculate battery state of health rather than merely reading and displaying measured parameters. Measurement data and analysis is done via a built-in web-server, which can be accessed with any browser. The web-based software eliminates the need for a standalone software package and is viewable on a desktop or mobile environment.
The Vigilant utilizes several technologies new to the battery monitoring industry to predict battery failure:
- Battery cell condition: Using machine learning algorithms to accurately calculate deterioration much earlier than current Ohmic testing methods
- Battery state of health: Algorithms encompassing 12 key parameters to estimate the health of the battery as a whole. It includes measured changes in internal and external factors and in all parameters that could identify a potential reduction in anticipated battery life
- Battery risk factor: Employing individual cell state of health along with temperature and ripple current to better predict risk of battery failures
- True float current: AMF sensors measure true float current without the remanence and temperature problems of Hall-effect transducers
