Battery Pack Assembly Defect Diagnosis via Internal Resistance
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Solution Overview
Problem
Existing methods fail to effectively diagnose assembly defects in battery packs, which can lead to inefficiencies and safety issues in hybrid and electric vehicles.
Innovation Solution
A method that calculates internal resistance values for both the battery module and the battery control unit (BCU), and compares these values with measured voltage values to diagnose assembly defects in the battery pack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If internal resistance values are calculated for both battery module and BCU, then assembly defect diagnosis accuracy is improved, but device complexity increases
Solution Approach 1:
The battery pack is segmented into distinct components (battery module and BCU) with separate internal resistance measurements. This segmentation allows targeted diagnosis of assembly defects in specific components while maintaining manageable measurement complexity through modular approach.
Solution Approach 2:
Internal resistance values serve as intermediary parameters that indirectly indicate assembly defects. Instead of directly detecting defects, the system measures internal resistance of individual components as intermediate indicators, which then inform the overall diagnosis through comparison with voltage values.
2Reliability
If multiple internal resistance values are measured and compared, then diagnosis reliability is improved, but measurement time increases
Solution Approach 1:
Internal resistance values of the battery module and BCU are measured and stored in advance before actual operation. This preliminary measurement allows rapid diagnosis during operation by comparing pre-stored resistance values with real-time voltage readings, eliminating the need for time-consuming multi-parameter measurements during critical diagnostic moments.
Solution Approach 2:
The system establishes a feedback mechanism where pre-measured internal resistance values are continuously compared with real-time voltage measurements. This feedback loop enables reliable defect detection by identifying deviations from expected relationships between resistance and voltage, providing timely diagnosis without requiring extensive real-time measurement.
Data Source
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AI summary
Provided is a method of diagnosing an assembly defect of a battery pack, the method including calculating an internal resistance value of a battery module (110) including battery cells (111) in the battery pack, calculating an internal resistance value of a battery control unit, BCU, (100) in the battery pack and configured to control charging/discharging of the battery module (110), and comparing a first voltage value of the battery pack with a second voltage value of the battery pack, the first voltage value being calculated based on the internal resistance value of the battery module (110) and the internal resistance value of the BCU (100), and the second voltage value being a measured voltage value of the battery pack.