Cloud Battery SoH Learning for Consistent Deterioration Estimation
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Solution Overview
Problem
Existing battery management systems (BMS) have varying accuracy in determining the State of Health (SoH) due to different learning numbers under real-time conditions, affecting the precision of battery deterioration state estimation and reutilization.
Innovation Solution
A system and method that utilizes a cloud-based approach to collect and analyze statistical data from multiple BMSs, updating deterioration state values and adjusting learning speeds by comparing and sharing information across a network of batteries, thereby optimizing the operating state of batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a BMS operates independently with its own learning number, then it can function autonomously, but the accuracy of deterioration state estimation varies due to different real-time conditions
Solution Approach 1:
The patent combines multiple independent BMS units into a unified cloud-based system where deterioration state data from multiple BMS units are aggregated and analyzed together. The cloud server consolidates learning results from multiple sources to improve overall estimation accuracy while maintaining individual BMS autonomy for local operations.
Solution Approach 2:
The cloud server acts as an intermediary between multiple BMS units, receiving deterioration state data from each BMS, performing centralized analysis, and distributing optimized learning results back to the BMS units. This mediator enables accurate deterioration estimation without requiring direct complex interactions between individual BMS units.
2Productivity
If a BMS continuously learns and updates deterioration state in real-time, then it adapts to changing conditions, but the learning speed and accuracy are inconsistent across different BMS units
Solution Approach 1:
The system implements a feedback mechanism where the cloud server receives deterioration state data from multiple BMS units, analyzes the collective learning results, and sends corrected or optimized deterioration state information back to individual BMS units. This feedback loop ensures consistent and accurate deterioration estimation across all BMS units while maintaining continuous learning.
Solution Approach 2:
The cloud server performs preliminary analysis and consolidation of deterioration state data from multiple BMS units before distributing the results back to individual units. This preliminary processing ensures that each BMS unit receives pre-validated and optimized learning results, improving both learning speed and accuracy without requiring each BMS to independently process all data.
3Adaptability or versatility
If multiple BMSs operate independently with different learning numbers, then each can adapt to its specific conditions, but the overall system lacks consistency in deterioration state assessment
Solution Approach 1:
The cloud server provides a universal platform that handles multiple functions: collecting data from diverse BMS units, analyzing different deterioration patterns, and distributing standardized learning results. This universal system maintains adaptability to various real-time conditions while ensuring consistency across the entire battery management network.
Data Source
AI summary
A system for optimizing an operating state of a battery by using a cloud includes: a cloud unit configured to receive battery data; a first data collection unit configured to collect first data; a first data transmitter configured to transmit the first data to the cloud unit; a second data receiver configured to receive second data from the cloud unit; and a controller configured to control an operating state of a battery based on the second data and to perform any one of an update of first deterioration state information and an adjustment of a learning speed.