Battery Operating State Determination via Operational Limits
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Solution Overview
Problem
Existing methods for determining a battery's ability to meet operational demands are unsatisfactory, particularly in aircraft systems, where factors like state of charge, temperature, and aging influence its performance, leading to potential failures in delivering required power profiles.
Innovation Solution
A method comprising a learning phase to establish operational limits for each usage profile based on battery parameters such as temperature, charge level, and aging factors, followed by a test phase to assess the battery's state and adjust parameters to ensure it meets the required profile, involving a management system integrated within the battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery control systems disconnect the battery upon failure, then safety is improved, but the battery's ability to provide power when actually operational is reduced due to premature disconnection
Solution Approach 1:
The system performs preliminary learning phases to establish operational limits before actual usage. By pre-determining the boundaries between available and unavailable states across multiple usage profiles, the system avoids premature disconnection and only disconnects when truly necessary, thus maintaining productivity while ensuring safety
Solution Approach 2:
The system dynamically evaluates multiple battery parameters (temperature, state of charge, aging parameters) against usage profile requirements. By changing the assessment parameters from simple on/off states to multi-dimensional parameter evaluation, the system accurately determines when the battery can truly meet demands, preventing unnecessary disconnections
2Use of energy by moving object
If the battery operates at low temperatures, then energy consumption is reduced, but the available capacity decreases and internal resistance increases
Solution Approach 1:
The learning phase pre-determines operational limits at various temperatures including low temperatures. This allows the system to know in advance the temperature-dependent capacity boundaries, enabling it to assess whether the battery can meet usage profiles even at low temperatures without prematurely declaring failure
Solution Approach 2:
The system continuously monitors temperature and compares it against the pre-established operational limits for that temperature. This feedback mechanism allows dynamic adjustment of availability assessment based on actual temperature conditions, preventing false unavailable declarations when the battery can still meet requirements
3Device complexity
If the battery is monitored using simple on/off status communication, then system complexity is reduced, but the ability to predict future availability and prevent failures is insufficient
Solution Approach 1:
The system performs preliminary learning to establish operational limits before actual operation. By pre-calculating the boundaries between available and unavailable states for multiple usage profiles, the system gains failure prediction capability without adding complex real-time communication protocols, maintaining simple on/off status communication
Solution Approach 2:
The system creates a virtual model of battery behavior through the learning phase, copying the expected performance boundaries into a lookup structure. This virtual model enables failure prediction by comparing actual status against pre-determined limits, achieving advanced prediction capability without complex real-time monitoring infrastructure
4Device complexity
If the battery aging is not considered, then the monitoring system remains simple, but the available capacity and internal resistance assessments become inaccurate over time
Solution Approach 1:
The learning phase is repeated at different aging stages to update operational limits. By preliminarily determining limits at various aging points, the system maintains accurate capacity assessment without requiring complex real-time aging models, simply comparing current performance against age-appropriate benchmarks
Solution Approach 2:
The system periodically updates the learning phase at different aging stages. This periodic re-calibration of operational limits ensures that aging effects are captured without requiring continuous complex monitoring, maintaining measurement precision through interval-based updates rather than constant complex computation
Data Source
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AI summary
The invention concerns a method for determining the operating state of the battery with respect to one or more use profiles. This method comprises: - a step of prior learning during which, for one or more use profiles, operational limits of said battery are defined depending on parameters of the battery; said operational limits defining an operational zone in which the battery carries out the one or more use profiles, and a non-operational zone in which the battery does not carry out the one or more use profiles, - a step of determining the operating state of the battery for a given use profile in the course of which the parameters of the battery in operation are determined, and - a comparison step in which the operational limits coming from the learning step and the parameters of the battery in operation coming from the determination step are compared and the battery is positioned in the operational or non-operational zone.