EV Battery Self-Diagnosis Control for Cell Anomaly Detection
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Solution Overview
Problem
Existing electric vehicle technologies lack effective methods for diagnosing battery anomalies to prevent battery fires and improve user experience by optimizing charging processes while reducing resource consumption.
Innovation Solution
A vehicle control apparatus and system that utilize a processor to transmit battery cell characteristics to a server, activate a self-diagnosis protocol based on top percentage data, and perform charging operations according to the protocol, identifying anomalies through resistance deviations and voltage changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of battery cells is performed to identify anomalies, then battery safety is improved, but resource consumption increases
Solution Approach 1:
The system implements self-diagnosis protocols where the battery management system automatically monitors itself for anomalies without requiring external intervention. The processor continuously assesses battery cell states using pre-stored diagnosis protocols, enabling the system to self-monitor safety while optimizing resource usage through intelligent activation only when necessary.
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on battery state and risk assessment. Diagnosis protocols are selectively activated or updated based on changes in battery parameters such as temperature, voltage deviations, and charge states, allowing continuous safety monitoring while reducing resource consumption during normal operating conditions.
2Measurement precision
If diagnosis thresholds are set to be highly sensitive to detect anomalies, then diagnosis accuracy is improved, but false alarms increase
Solution Approach 1:
The system pre-stores multiple diagnosis protocols with different threshold settings and activation conditions before actual battery operation. These protocols are prepared in advance based on historical data and manufacturer specifications, allowing the system to select appropriate sensitivity levels for different operating scenarios, thereby improving diagnosis accuracy while minimizing false alarms through context-appropriate threshold selection.
Solution Approach 2:
The diagnosis thresholds are not fixed but dynamically adjusted based on real-time battery conditions, environmental factors, and historical performance data. The system adapts sensitivity levels according to the current operating context, maintaining high diagnostic accuracy while reducing false alarms by modulating threshold strictness based on actual risk levels and battery state.
Data Source
AI summary
A vehicle control apparatus may include a communication circuit, a battery including battery cells, and a processor. The processor transmits at least one characteristic value among standard deviations of voltages of the battery cells, standard deviations of temperatures of the battery cells, standard deviations of states of charge (SOCs) of the battery cells, or standard deviations of states of health (SOHs) of the battery cells, or any combination to a server via the communication circuit, receive a signal for updating or activating a self-diagnosis protocol for identifying whether there is an anomaly in the battery cell or the battery via the communication circuit from the server, based on that the at least one characteristic value is included in a specified top percentage of characteristic values corresponding to the at least one characteristic value and obtained from other vehicle control apparatuses, and updates or activates the self-diagnosis protocol.