Battery Diagnosis Using Correction Profiles for Lithium Plating
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Solution Overview
Problem
Existing battery diagnosis technologies lack the ability to accurately reflect long-term trends and diagnose the state of batteries, particularly in terms of lithium precipitation, which is crucial for improving safety and lifespan.
Innovation Solution
An apparatus and method that utilize a storage unit to store battery profiles and a controller to generate correction profiles, calculate normalization values, and diagnose the battery state based on these values and a preset reference value, incorporating kurtosis analysis to differentiate between normal and lithium precipitation states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery diagnosis methods are used, then the diagnosis process is simple, but the diagnosis accuracy and ability to reflect long-term trends is insufficient
Solution Approach 1:
The system performs preliminary actions by storing multiple battery profiles corresponding to different cycles before diagnosis. Each profile represents voltage-capacity relationships at different states of charge, prepared in advance to enable accurate long-term trend analysis when diagnosis is needed.
Solution Approach 2:
The invention adds another dimension to battery diagnosis by introducing cycle-based temporal dimension. Instead of single-point diagnosis, the system analyzes voltage-capacity relationships across multiple cycles using stored profiles, transforming the diagnosis from a static snapshot to a dynamic multi-dimensional analysis that captures long-term battery behavior.
2Reliability
If battery profiles for multiple cycles are stored and analyzed, then long-term trends can be reflected, but the computational complexity and processing time increase
Solution Approach 1:
Battery profiles for multiple cycles are stored in advance in the storage unit, representing voltage-capacity relationships at different states of charge. This preliminary data collection enables rapid diagnosis without real-time computational burden, as the data preparation is done beforehand during battery operation.
Solution Approach 2:
The system creates simplified correction profiles that copy and condense the essential characteristics from multiple detailed battery profiles. These correction profiles contain normalized capacity change amounts that represent long-term trends without requiring processing of all original profile data, significantly reducing computation time while maintaining diagnostic reliability.
3Measurement precision
If normalization values and kurtosis analysis are calculated, then lithium precipitation states can be distinguished, but the computational requirements increase
Solution Approach 1:
The system extracts only the essential diagnostic information from complex battery profiles by calculating normalization values and kurtosis metrics. Instead of analyzing all raw voltage-capacity data, the method extracts key statistical features that specifically indicate lithium precipitation states, reducing computational energy while maintaining high detection precision.
Solution Approach 2:
The invention transforms raw battery profile data into different parameter representations through normalization and kurtosis calculation. By changing the parameter space from raw voltage-capacity pairs to normalized capacity change amounts and statistical moments, the system enables efficient lithium precipitation detection with reduced computational requirements.
Data Source
AI summary
An apparatus for diagnosing a battery according to an embodiment of the present disclosure includes a storage storing a plurality of battery profiles, each battery profile corresponding to a respective cycle of a plurality of cycles, wherein each battery profile represents a respective relationship between voltages and capacities of the battery in the respective cycle; and a controller configured to generate a plurality of correction profiles, each correction profile-representing a respective relationship between the voltages of the battery and capacity change amounts in each cycle, calculate a plurality of normalization value of the plurality of normalization values corresponding to each correction profile of the generated plurality of correction profiles, and diagnose a state of the battery based on the calculated plurality of normalization values and a preset reference value.