The ground-breaking Vigilant Battery Management System (BMS) with Advanced Multi-Function (AMF) sensors employs several new battery parameters to predict battery condition. Included in these critical parameters are Cell Condition, Battery State of Health, and Battery (at) Risk Factor. These new features are made possible by machine learning algorithms built into the Vigilant.
The following key battery parameters are monitored and recorded by the Vigilant: string voltage, float current, cell voltage, cell resistance, terminal & connection resistance, cell & ambient temperature, dc ground fault, and electrolyte level.
Web-Based Battery Data Management
A key advantage of the Vigilant is how it processes measurement data
Rather than simply read and display measured parameters, the Vigilant also uses Artificial Intelligence to calculate the SoH of the battery
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
Predicting Battery Condition
The Vigilant utilizes several technologies new to the battery monitoring industry to predict battery failure:
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 & external factors and in all parameters that could identify a potential reduction in anticipated battery life
Battery Risk Factor (RF): Employing individual cell SoH along with temperature and ripple current to better predict risk of battery failures
True Float Current: Vigilant’s Advanced Multi-Function (AMF) sensors measure true float current without the remanence and temperature problems of Hall-effect transducers
The ground-breaking Vigilant Battery Management System (BMS) with Advanced Multi-Function (AMF) sensors employs several new battery parameters to predict battery condition. Included in these critical parameters are Cell Condition, Battery State of Health, and Battery (at) Risk Factor. These new features are made possible by machine learning algorithms built into the Vigilant.
The following key battery parameters are monitored and recorded by the Vigilant: string voltage, float current, cell voltage, cell resistance, terminal & connection resistance, cell & ambient temperature, dc ground fault, and electrolyte level.
Web-Based Battery Data Management
- A key advantage of the Vigilant is how it processes measurement data
- Rather than simply read and display measured parameters, the Vigilant also uses Artificial Intelligence to calculate the SoH of the battery
- 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
Predicting Battery Condition
The Vigilant utilizes several technologies new to the battery monitoring industry to predict battery failure:
- 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 & external factors and in all parameters that could identify a potential reduction in anticipated battery life
- Battery Risk Factor (RF): Employing individual cell SoH along with temperature and ripple current to better predict risk of battery failures
- True Float Current: Vigilant’s Advanced Multi-Function (AMF) sensors measure true float current without the remanence and temperature problems of Hall-effect transducers
