Event Detection Battery Analysis With Predictive Capacity Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery capacity testing and analysis methods for event detection systems are time-consuming, technically demanding, and prone to false readings, leading to costly and unplanned site visits, especially due to variations in battery performance characteristics from different manufacturers and deployment environments.
Innovation Solution
Implementing a system that remotely monitors battery performance characteristics using a controller to record data at intervals, apply prediction models (equivalent circuit or machine-learning), and generate alerts for battery replacement before capacity thresholds are exceeded, reducing the need for on-site visits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery capacity testing methods are used, then battery capacity can be measured, but the process is time-consuming and requires frequent site visits
Solution Approach 1:
The system performs preliminary actions by continuously monitoring battery voltage and calculating state of charge in advance, so that battery capacity status is already known before a site visit occurs. This eliminates the need for time-consuming on-site capacity testing.
Solution Approach 2:
The system creates a virtual copy of the battery capacity information through electrical measurements and prediction models, replacing the need for physical capacity testing. The controller measures voltage and uses algorithms to generate accurate capacity estimates without actual discharge testing.
2Measurement precision
If traditional battery testing is performed, then battery status can be assessed, but false readings occur due to manufacturer and environmental variations
Solution Approach 1:
The system changes the measurement parameter from direct capacity testing to voltage-based state of charge calculation. By measuring voltage and using prediction models that account for temperature and battery age, the system achieves more reliable and consistent readings across different manufacturers and environments.
Solution Approach 2:
The system implements feedback by continuously monitoring battery parameters and comparing predicted state of charge with actual measurements. This allows the system to adjust for manufacturer variations and environmental factors, improving measurement precision and reliability over time.
3Reliability
If battery capacity is monitored frequently, then accurate predictions can be made, but the system complexity increases
Solution Approach 1:
The battery monitoring system performs self-service by using the control panel's existing processor and memory to store and analyze battery data. No separate complex monitoring hardware is needed - the system uses its own resources to perform continuous battery assessment and prediction.
Solution Approach 2:
The control panel's processor is made multi-functional by having it perform both its normal control functions and battery monitoring functions. This universal use of existing components avoids increasing device complexity while enabling frequent and reliable battery capacity measurements.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Devices, systems, and methods for battery analysis in an event detection system are described herein. In some examples, one or more embodiments include a processor to record a characteristic of a battery in a control panel of the event detection system at predetermined intervals, determine, by the processor via a prediction model using the recorded characteristics, a trend associated with an performance characteristic of the battery, and generate, by the processor, an alert based on the trend associated with the performance characteristic.