Battery Tracking System Using Distributed Ledger for End-of-Life Prediction
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Solution Overview
Problem
The underutilization of battery potential due to inefficiencies in monitoring, usage, and end-of-life prediction of primary life batteries leads to premature disposal and environmental hazards, while the secondary market for second life batteries is hindered by unreliable battery information and inefficient selection processes.
Innovation Solution
A power cell tracking and optimization system that monitors and analyzes battery data using a distributed ledger technology to determine approximate battery end-of-life (ABEL) and provide recommendations for maximizing battery life, enabling accurate second-life repurposing and reducing waste by optimizing battery usage and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If batteries are disposed of when they reach 80% capacity (end of primary life), then manufacturing costs and environmental hazards are reduced, but battery resource utilization is wasted and second-life market potential is lost
Solution Approach 1:
The system performs preliminary monitoring and tracking of battery data throughout the primary life cycle, establishing a comprehensive data foundation before the battery reaches end-of-life. This preliminary action enables accurate prediction of actual end-of-life points and facilitates seamless transition to second-life applications, preventing premature disposal while ensuring reliability through data-driven decisions
Solution Approach 2:
The system implements continuous feedback loops that monitor battery performance metrics, compare them against degradation models, and update end-of-life predictions in real-time. This feedback mechanism ensures accurate determination of when batteries should transition from primary to second life, optimizing resource utilization while maintaining reliability through ongoing validation
2Productivity
If comprehensive battery monitoring and tracking systems are implemented, then battery life optimization and waste reduction are improved, but system complexity and implementation costs increase
Solution Approach 1:
The system is designed as a multi-functional platform that simultaneously performs data collection, analysis, prediction, optimization recommendation, and transaction facilitation. By consolidating these functions into a single universal system rather than separate components, the solution achieves comprehensive battery life optimization while managing complexity through integrated architecture
Solution Approach 2:
The system enables batteries to effectively monitor and report their own health status and performance metrics through embedded sensors and communication interfaces. This self-service capability reduces the complexity of external monitoring infrastructure while achieving comprehensive tracking and optimization goals
3Measurement precision
If second-life batteries are selected without reliable information systems, then transaction speed is improved, but selection accuracy and market reliability deteriorate
Solution Approach 1:
The system performs preliminary classification, grading, and verification of battery data before transactions occur. By pre-processing and organizing battery information including performance history, degradation patterns, and suitability assessments, the system enables rapid and accurate second-life battery selection without compromising precision during the actual transaction process
Data Source
AI summary
A computing system can receive and compile power cell data, and in certain examples, the power cell data can be distributed to a distributed ledger. The computing system can further determine approximate battery end of life (ABEL) for each power cell based on a compiled historical record of power cell data. Based on the determined ABEL, the computing system can generate ABEL reports for users, determine optimal settings for a power cell or battery-powered device, and/or transmit notifications to users, to facilitate power cell usage optimization, and/or optimal repurposing or recycling timing.


