Battery Cell Replacement Selection Based on SOC Variation
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Solution Overview
Problem
Existing battery management systems do not adequately account for differences in vehicle usage when selecting replacement cells for battery assemblies, leading to inefficient maintenance and potential reduction in battery capacity and lifespan.
Innovation Solution
A battery information processing apparatus and method that assess the variation in State Of Charge (SOC) per vehicle run to generate replacement information, allowing for the selection of appropriate replacement cells based on usage patterns, thereby optimizing battery performance and lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a cell is replaced with an identical cell in specifications, then the replacement is simple and cost-effective, but the battery assembly performance may not be optimized for specific user usage patterns
Solution Approach 1:
The patent applies local quality by selecting replacement cells with specific characteristics tailored to the actual usage conditions of each battery assembly. Instead of uniform replacement, the system analyzes individual usage patterns (SOC variation, temperature conditions, charge/discharge rates) and selects cells with locally optimized properties matching those specific conditions, thereby improving adaptability without significantly complicating the replacement process
Solution Approach 2:
The patent implements dynamics by making the cell selection process adaptive rather than static. The system continuously monitors usage conditions and dynamically adjusts replacement cell selection based on actual performance data, allowing the battery assembly to be optimized for evolving usage patterns while maintaining a systematic replacement approach
2Ease of operation
If maintenance is performed based on general battery block variation, then the maintenance system is simple to operate, but it cannot provide optimized maintenance timing for individual usage conditions
Solution Approach 1:
The patent applies feedback by implementing a closed-loop system that continuously monitors battery assembly usage conditions, analyzes SOC variation patterns, temperature profiles, and charge/discharge rates, then uses this feedback to determine optimal maintenance timing. This automated feedback mechanism maintains ease of operation while significantly improving measurement precision through continuous data collection and analysis
Solution Approach 2:
The patent replaces manual maintenance scheduling with an automated information processing system that uses algorithms to analyze usage data and determine maintenance timing. This substitution of mechanical/manual processes with automated computational analysis improves precision in assessing usage conditions while keeping the system easy to operate through automated decision-making
3Productivity
If cells are selected without considering usage patterns, then the selection process is quick and straightforward, but battery lifespan and capacity are reduced due to mismatched cell characteristics
Solution Approach 1:
The patent applies preliminary action by pre-establishing the framework for usage condition analysis and cell characteristic matching before actual replacement occurs. The system pre-analyzes usage patterns, pre-identifies suitable replacement cell characteristics, and pre-prepares selection criteria, enabling quick decision-making at the time of replacement while ensuring reliability through thorough preliminary assessment
Solution Approach 2:
The patent implements self-service by enabling the battery management system to automatically analyze usage conditions and select appropriate replacement cells without requiring extensive manual intervention. The system serves itself by autonomously evaluating usage patterns, comparing cell characteristics, and making selection decisions, thereby maintaining quick selection speed while improving reliability through consistent, data-driven choices
Data Source
AI summary
When ΔSOC (an amount of variation in SOC per one running) is greater than a second threshold value, a management server generates third rebuilding information indicating that a cell is a suited cell. When ΔSOC is greater than a first threshold value and smaller than the second threshold value, the management server generates second rebuilding information indicating that a cell is a suited cell. When ΔSOC is smaller than the first threshold value, the management server generates first rebuilding information indicating that a cell is a suited cell.