Battery Residual Value Evaluation Using Resistance Change Rate
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Solution Overview
Problem
Lithium secondary batteries face challenges in reusing due to physical limitations and a lack of clear criteria for determining residual value, making it difficult to quickly and accurately evaluate their remaining life for potential reuse or remanufacturing.
Innovation Solution
A battery residual value evaluation system that calculates resistance change rate by measuring voltage and current signals, converting them into digital signals, and determining residual value based on pre-learned reference ranges using machine learning criteria, including minimum and maximum resistance and change rate values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery evaluation methods are used, then the evaluation process is simple, but the accuracy and speed of residual value prediction is insufficient
Solution Approach 1:
The patent changes the evaluation parameters from simple capacity measurements to multiple resistance-related parameters (real-time resistance, resistance change value, resistance change rate) obtained through voltage and current signal measurements. This allows more accurate residual value prediction by capturing dynamic battery state changes rather than relying on static capacity data alone.
Solution Approach 2:
The patent replaces traditional mechanical/electrical measurement methods with a digital signal processing approach. By converting voltage and current signals to digital form and using computational algorithms to calculate resistance parameters, the system achieves higher precision while maintaining operational simplicity through automated processing.
2Measurement precision
If comprehensive battery testing is performed to determine residual value, then the accuracy improves, but the time required for evaluation increases
Solution Approach 1:
The patent performs preliminary calculations by pre-establishing the relationship between resistance parameters and battery residual value through machine learning. The system pre-processes data to create reference models, allowing rapid real-time evaluation without requiring extensive full-scale testing at the time of assessment. This enables accurate predictions to be made quickly using pre-learned patterns.
Solution Approach 2:
The patent skips time-consuming traditional testing procedures by directly measuring voltage and current and calculating resistance parameters in real-time. The system rushes through the evaluation process by using immediate signal processing and automated determination algorithms, achieving both speed and accuracy simultaneously rather than requiring lengthy sequential testing.
3Measurement precision
If multiple resistance parameters are calculated for each charging and discharging cycle, then the residual value determination becomes more accurate, but the computational complexity increases
Solution Approach 1:
The patent creates a universal evaluation system that uses the same voltage and current signal measurements to determine multiple resistance parameters (real-time resistance, resistance change value, resistance change rate) simultaneously. This multi-functional approach allows comprehensive battery assessment through a single integrated measurement and calculation process, reducing the need for separate testing procedures.
Solution Approach 2:
The patent merges the measurement and calculation processes by integrating voltage and current signal acquisition with real-time resistance parameter computation. All resistance-related parameters are calculated together from the same signal data within a unified computational framework, simplifying the overall system architecture while maintaining high evaluation accuracy.
4Ease of operation
If clear criteria for determining residual value are established, then the ease of operation improves, but the system requires more sophisticated reference range determination
Solution Approach 1:
The patent implements self-service through automated determination algorithms that automatically compare measured resistance parameters against reference ranges and output residual value assessments. The system performs the entire evaluation process autonomously without requiring manual interpretation or complex user intervention, making operation simple while incorporating sophisticated reference range data.
Solution Approach 2:
The patent introduces reference ranges as an intermediary layer between raw resistance parameter measurements and residual value conclusions. These pre-established reference ranges act as a mediator that translates complex multi-parameter data into clear, actionable assessment criteria, simplifying the user interface while maintaining analytical sophistication in the background.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and accurate prediction of lithium secondary battery residual value, allowing for effective reuse or remanufacturing by providing a clear criterion for determining battery performance degradation.
Implementation Method 1
a measurement unit that measures a voltage and a current of a battery to generate a voltage signal and a current signal
Implementation Method 2
a conversion unit that generate a digital signal by converting the voltage signal and the current signal, which are analog signals
Implementation Method 3
a resistance calculation unit that generates a resistance value, a resistance change value, and a resistance change rate of the battery for each of charging and discharging cycles of the battery based on the voltage signal and the current signal
Data Source
AI summary
Disclosed is a battery residual value evaluation system, which includes a measurement unit that measures a voltage and a current of an external battery to generate a voltage signal and a current signal, a resistance calculation unit that generates a resistance value, a resistance change value, and a resistance change rate of the external battery for each of charging and discharging cycles of the external battery based on the voltage signal and the current signal, an external input unit that receives reference ranges from an outside, a determination unit that determines a battery residual value based on whether the resistance value, the resistance change value, and the resistance change rate fall within the reference ranges, respectively, and an output unit that outputs the determination result to the outside.