Battery Cell Short-Circuit Prognostics Using Open-Circuit Voltage Drop
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Solution Overview
Problem
Vehicle battery cells with internal short circuits experience undetectable voltage drops due to current flow, leading to heat generation and operational failures, which existing technologies struggle to promptly identify and address.
Innovation Solution
A system comprising a battery pack with voltage sensors and a computerized prognostic controller that monitors open-circuit voltage data over time, evaluates voltage drop rates using linear regression, recursive least square algorithms, or Kalman filters to identify cells with internal short circuits and signals alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage sensors and prognostic controllers monitor open-circuit voltage data over time, then internal short circuits can be detected through voltage drop rate analysis, but the system complexity and computational requirements increase
Solution Approach 1:
The monitoring system divides the battery pack into individual cell units, each with its own voltage sensor and monitoring parameters. The analysis is segmented by evaluating voltage drop rates for each cell independently through linear regression, allowing targeted detection without requiring complex system-wide analysis.
Solution Approach 2:
The patent replaces complex physical diagnostic methods with computational analysis. Instead of using intricate electrical testing equipment or invasive measurements, the system uses algorithms (linear regression, recursive least squares, Kalman filters) to analyze voltage data and detect internal short circuits through mathematical modeling of voltage drop patterns.
2Measurement precision
If linear regression and recursive least square algorithms are applied to evaluate voltage drop rates, then detection precision improves, but computational time and processing power requirements increase
Solution Approach 1:
The system continuously collects and stores open-circuit voltage data over time windows before analysis is needed. By pre-processing and organizing the voltage data into time-series records, the system reduces the computational burden during actual detection, allowing algorithms to work with structured data rather than raw measurements.
Solution Approach 2:
The monitoring system dynamically adjusts the time window length for voltage drop rate evaluation. By adaptively selecting appropriate analysis windows based on operating conditions and detected anomaly patterns, the system optimizes the balance between detection precision and computational efficiency, using longer windows for stable conditions and shorter windows when rapid changes occur.
3Reliability
If the system monitors all battery cells continuously, then early detection of internal short circuits is achieved, but energy consumption and operational overhead increase
Solution Approach 1:
The system applies monitoring at different levels of intensity. All cells are monitored for basic voltage levels, but full algorithmic analysis (linear regression, recursive least squares) is applied selectively based on preliminary indicators. This partial action approach ensures early detection capability while reducing overall computational energy consumption by not continuously applying heavy processing to all cells.
Solution Approach 2:
The system uses feedback from voltage sensor readings to dynamically adjust monitoring intensity. When voltage drops within normal ranges, standard monitoring continues with lower energy consumption. When abnormal voltage drop patterns are detected, the system intensifies monitoring and analysis for affected cells, allocating computational resources based on actual risk levels rather than uniformly across all cells.
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
The system effectively detects internal short circuits in battery cells, providing timely warnings to prevent vehicle operational failures by accurately analyzing voltage drop rates and resistance, thereby enhancing battery health monitoring and vehicle safety.
Implementation Method 1
A battery cell may operate in charge mode, receiving electrical energy. A battery cell may operate in discharge mode, providing electrical energy.
Implementation Method 2
Such an internal short circuit may cause undesirable heat to be generated
Data Source
AI summary
A system for self-discharge prognostics for vehicle battery cells with an internal short circuit includes a plurality of battery cells and a voltage sensor providing open-circuit voltage data over time for each battery cell. The system further includes a computerized prognostic controller operating programming to monitor the open-circuit voltage data over time for each of the plurality of battery cells and evaluate a voltage drop rate through a time window for each of the plurality of battery cells based upon the open-circuit voltage data. The controller further identifies one of the plurality of battery cells to include the internal short circuit based upon the voltage drop rate and signals an alert based upon the one of the plurality of battery cells including the internal short circuit.