Battery Pack Internal Short Detection via Cell Segmentation
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Solution Overview
Problem
Identifying internal shorts in battery packs is challenging due to their potential to cause performance degradation and hazardous conditions, especially during charging, and existing methods are not sufficiently accurate or reliable for large-scale battery systems like those used in electric vehicles.
Innovation Solution
A fault-detection system that includes a data-acquisition and monitoring system to identify anomalous conditions in battery packs during charging, comparing these conditions against predetermined profiles to establish an internal-short state, which can prevent unsafe charging conditions by terminating the charge and initiating diagnostic tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for battery packs, then device complexity is reduced, but measurement precision and reliability of internal short detection deteriorate
Solution Approach 1:
The battery pack is divided into multiple individual cells, each equipped with its own monitoring circuit that measures voltage, current, and temperature independently. This segmentation allows precise detection of internal shorts in specific cells while keeping each monitoring unit relatively simple.
Solution Approach 2:
A microcontroller unit acts as an intermediary between the distributed sensor nodes and the central control system. It collects data from multiple sensors, performs preliminary analysis, and communicates with the charging system, thereby reducing overall system complexity while maintaining high detection precision.
2Reliability
If continuous monitoring of all battery parameters is implemented, then reliability of hazard detection is improved, but use of energy increases
Solution Approach 1:
The monitoring system performs measurements at periodic intervals rather than continuously. During normal charging operation, parameters are sampled at predetermined time intervals, reducing energy consumption while maintaining reliable hazard detection capability.
Solution Approach 2:
The monitoring system uses the existing charging current and voltage as part of its measurement inputs, rather than requiring separate dedicated measurement circuits for all parameters. This self-service approach reduces the energy overhead of the monitoring system.
3Measurement precision
If detailed individual cell data is collected and evaluated, then measurement precision for fault identification is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system continuously compares measured cell parameters against expected values and provides feedback when deviations are detected. This feedback mechanism guides the evaluation process, focusing computational resources on anomalous conditions rather than uniformly processing all data, thereby reducing overall evaluation difficulty.
Solution Approach 2:
Complex data evaluation and pattern recognition tasks are performed by a microcontroller unit using embedded algorithms, replacing what would otherwise require complex manual analysis or sophisticated external measurement equipment. This substitution simplifies the physical measurement system while maintaining high fault identification precision.
Data Source
AI summary
An apparatus and method for identifying a presence of a short circuit in a battery pack. A fault-detection apparatus for a charging system that rapidly charges a collection of interconnected lithium ion battery cells, the safety system includes a data-acquisition system for receiving a set of data parameters from the collection while the charging system is actively charging the collection; a monitoring system evaluating the set of data parameters to identify a set of anomalous conditions; and a controller comparing the set of anomalous conditions against a set of predetermined profiles indicative of an internal short in one or more cells of the collection, the controller establishing an internal-short state for the collection when the comparing has a predetermined relationship to the set of predetermined profiles.