Battery Parameter Update via Server-Side Classification
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Solution Overview
Problem
Lithium battery cores in handheld electronic devices experience inaccurate power estimation due to outdated battery parameters, leading to inefficient power management and reduced battery life if not updated regularly.
Innovation Solution
A method and system that utilize a server to collect, classify, and update battery parameter sets for target battery modules, employing AI to select suitable parameters based on usage conditions, ensuring timely and accurate updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery parameters are updated frequently to improve power estimation accuracy, then measurement precision improves, but loss of time increases due to repeated updates
Solution Approach 1:
The system performs preliminary actions by collecting battery parameter data from multiple sources (battery management system, charging device, discharging device) in advance and storing it in a database. When an update is needed, the system retrieves pre-collected data rather than collecting it in real-time, significantly reducing the update time while maintaining data comprehensiveness and accuracy.
2Measurement precision
If battery parameters are updated using only local data, then device complexity is reduced, but measurement precision deteriorates due to insufficient data samples
Solution Approach 1:
The patent introduces a server as an intermediary between multiple battery devices and the central system. The server collects, stores, and manages battery parameter data from multiple sources (battery management systems, charging devices, discharging devices), and provides this aggregated data to individual devices for updates. This intermediary approach enables access to diverse data samples without requiring each device to have complex data collection capabilities.
3Productivity
If battery parameters are updated at fixed intervals, then productivity is improved through automated periodic updates, but adaptability deteriorates when unexpected aging occurs
Solution Approach 1:
The system implements feedback mechanisms where the battery management system continuously monitors battery state and can trigger parameter updates based on detected aging patterns. Additionally, the server receives feedback from multiple devices and can proactively push updated parameters to devices that need them, creating a responsive system that adapts to actual battery conditions rather than relying solely on fixed schedules.
Data Source
AI summary
Disclosed are a method and a system for battery parameter update. The method is adapted to update a first battery parameter set of a target battery module, and includes: collecting a plurality of second battery parameter sets to a server when updating of a plurality of other battery modules are completed; classifying the second battery parameter sets based on a collection condition and storing them as reference battery parameter sets; determining whether the target battery module has not updated the first battery parameter set for more than a specified time; selecting a suitable battery parameter set close to a use state of the target battery module from the reference battery parameter sets when the target battery module does not update the first battery parameter set for more than the specified time; and updating the first battery parameter set of the target battery module according to the suitable battery parameter set.