Battery Voltage Diagnosis Using Dynamic SOC-Based Thresholds
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Solution Overview
Problem
Conventional methods for diagnosing voltage deviations in battery modules for electric vehicles do not accurately reflect time-varying battery characteristics, leading to reduced accuracy in determining abnormal voltage deviations, especially when replacing battery modules with different characteristics.
Innovation Solution
A battery diagnosis apparatus and method that includes sensing units to detect voltage and State of Charge (SOC) values of each battery, with a processor determining the largest SOC deviation and voltage deviation between selected batteries, setting a threshold value proportional to the largest voltage deviation, and diagnosing abnormal voltage deviations by comparing measured deviations with this threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voltage deviation diagnosis methods are used, then the diagnosis process is simple, but the accuracy of determining abnormal voltage deviations is reduced due to not reflecting time-varying battery characteristics
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the voltage deviation threshold based on battery characteristics such as State of Health (SOH) and State of Charge (SOC). Instead of using a fixed threshold, the system calculates and updates the threshold according to the actual battery state, thereby improving diagnosis accuracy while adapting to time-varying battery characteristics.
2Reliability
If battery modules are replaced, then the battery system can be maintained, but the accuracy of voltage deviation diagnosis is reduced due to large differences in characteristics between existing and replaced battery modules
Solution Approach 1:
The patent applies local quality by individually assessing and managing each battery module's characteristics, particularly SOH and SOC parameters. When battery modules are replaced, the system locally adjusts the diagnosis threshold based on the specific characteristics of the replaced and existing modules, rather than applying a uniform threshold across all modules. This localized approach maintains reliability while improving diagnosis accuracy despite characteristic differences.
3Ease of operation
If a fixed voltage threshold is used for diagnosis, then the diagnosis method is simple to implement, but it cannot accurately reflect the time-varying characteristics of batteries
Solution Approach 1:
The patent applies dynamics by transitioning from a static fixed voltage threshold to a dynamic threshold that automatically adjusts based on real-time battery characteristics. The system continuously monitors SOH and SOC parameters and recalculates the voltage deviation threshold accordingly, enabling the diagnosis method to adapt to time-varying battery characteristics while remaining automated and relatively easy to implement.
Data Source
AI summary
A battery diagnosis apparatus may include a plurality of batteries, sensing units matched with the batteries on a one-to-one basis to detect a voltage or an State of charge (SOC) value of each of the batteries, and a processor. The processor is configured to determine a largest SOC deviation between a first battery and a second battery selected among the batteries, obtain a largest voltage deviation between the first battery and the second battery based on the largest SOC deviation, set a threshold value proportional to the largest voltage deviation, and diagnose whether a voltage deviation between the first battery and the second battery is abnormal by comparing a measured voltage deviation between the first battery and the second battery with the threshold value.