Early Fault Detection in Electric Vehicle Batteries

Predictive battery management system for electric vehicles that enables proactive maintenance through machine learning-based fault detection and proactive intervention. The system analyzes real-time vehicle data to predict battery degradation, identifies potential faults, and generates alerts for maintenance scheduling, thereby extending battery lifespan and ensuring optimal vehicle performance.

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Diagnosing battery cell health through cell-by-cell analysis using real-time operating data. The method employs both driving and stopping data to identify abnormal battery cells, with the stopping data providing a critical safety benchmark. The system uses machine learning models to predict cell health based on voltage and charging/discharging patterns, enabling targeted cell-level analysis rather than per-battery diagnostics. This approach enables precise detection of cell-level issues through both driving and stopping data, with the stopping data serving as a safety benchmark.

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Vehicle battery fault warning system that monitors battery pack health through comprehensive analysis of individual cell characteristics. The system identifies battery pack-wide trends and detects anomalies through statistical analysis of battery cell data. This enables early detection of potential battery failures by pinpointing critical cell behavior patterns and their deviations from normal operating parameters.

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Early warning system for battery failure in electric vehicles through predictive monitoring. The system collects battery data over a predefined observation period, including temperature readings and battery cell performance metrics. It analyzes this data to determine battery health through multiple cycles, then calculates the failure probability. When the probability exceeds predetermined thresholds, the system generates fault warning notifications. This approach enables proactive battery management by identifying potential issues before they lead to catastrophic failures.

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Fault detection method for electric vehicles that improves reliability through enhanced power battery monitoring. The method combines power battery data with abnormal state data to create a hybrid training set, enabling more accurate model performance. By incorporating both normal and abnormal battery states into the training data, the method enhances model detection accuracy beyond traditional power state-only training. This approach effectively addresses the complex degradation patterns of electric vehicle batteries, particularly in harsh environmental conditions.

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Fault detection method for electric vehicles that improves accuracy by incorporating abnormal states into training data. The method involves sampling only normal battery states during training, while using abnormal states to fine-tune the model. This approach enhances the model's ability to detect faults by leveraging the performance of normal states in training, while still capturing the variability of abnormal states. The method enables more accurate fault detection while maintaining high recall rates.

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Power battery fault detection method for electric vehicles that improves the accuracy of battery health monitoring. The method eliminates false alarms by preprocessing vehicle battery data through sampling and feature extraction, and then applies machine learning-based fault detection algorithms to determine battery health. The preprocessing step removes sampling errors while the algorithms extract critical battery characteristics from the data, enabling more precise fault detection and reducing false positives.

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Real-time battery fault detection and early warning system for electric vehicles using signal processing. The system analyzes battery state data from the vehicle's onboard management system to identify abnormal patterns indicative of potential battery failures. It detects deviations in state values across individual cells and their corresponding normal states, then determines the cause of these deviations. The system can predict both the occurrence of faults and their timing based on these patterns, enabling proactive battery health monitoring and early intervention before thermal runaway or other catastrophic events occur.

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Battery safety inspection system for electric vehicles using fault injection to predict and prevent battery-related incidents. The system integrates multiple components: an input module for real-time vehicle data, a fault injection module for simulating battery faults, a health model for predicting battery degradation, and a failure warning module for detecting and reporting battery-related issues. The system enables early detection of critical battery conditions through simulated fault injection, enabling proactive maintenance and reducing the risk of battery-related accidents.

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Rapid screening of large-capacity energy storage battery packs through automated analysis of their power consumption patterns. The method employs a correlation matrix inversion process to derive a comprehensive energy consumption profile, followed by a multi-step calculation of historical surplus power and external factors. This enables the identification of battery packs that do not meet established performance criteria, allowing for targeted maintenance and replacement.

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Early warning method for battery system safety that identifies potential issues before they become critical. The method analyzes fault data from multiple battery models, identifies common patterns, and develops a dynamic threshold based on vehicle monitoring data. This approach enables early detection of battery safety issues, reduces false alarms, and enables proactive maintenance to prevent battery-related accidents.

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A method and system for detecting battery faults in electric vehicles through a multi-level predictive approach. The system monitors battery voltage changes over time, employing a threshold-based approach that detects significant voltage deviations exceeding 25% of the previous voltage value. This enables early detection of critical battery conditions through a combination of rapid voltage changes and localized voltage deviations.

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Fault diagnosis of lithium-ion power battery packs based on confidence intervals to enhance safety and efficiency. The method employs real-time voltage and current data to determine battery health through a dynamic confidence interval approach. By dividing the confidence interval into distinct stages based on historical voltage data, the system can accurately identify voltage deviations that indicate potential battery failure. This enables prompt alarm generation and proactive maintenance to prevent battery-related issues before they escalate into thermal runaway.

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Real-time monitoring of electric vehicle battery health through integrated environmental and historical data analysis. The system, integrated into electric vehicle charging stations, calculates the battery's condition based on current environmental factors and past performance data. It then applies reliability thresholds to each environmental factor category, determining the maximum acceptable score before degrading the overall battery health rating. This approach enables real-time monitoring of battery condition while providing detailed insights into environmental influences and historical trends.

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A method for remote fault classification and diagnosis of vehicle fuel cells through re-sampling and XGBoost-based classification. The method involves collecting vehicle operating data, preprocessing it into training and test sets, and employing XGBoost to classify fuel cell faults. The preprocessing step addresses the issue of data imbalance typically encountered in fuel cell fault classification, where most classes are overrepresented and minority classes are underrepresented. The XGBoost algorithm, with its ability to handle imbalanced data through regularization, column sampling, and feature selection, enables more accurate classification of fuel cell faults compared to traditional methods.

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Predicting and locating battery faults in energy storage power stations through a multi-level fault analysis approach. The method employs a Delphi method to establish influence relationships between fault types and their characterization parameters, followed by the construction of an expert database of battery compartment faults. It then uses a combination of single battery health analysis, battery performance trend prediction, and expert-based fault location to predict and identify battery failures. The method enables early detection, precise fault location, and accurate fault prediction across battery cells, modules, clusters, and the entire battery compartment, with real-time monitoring capabilities.

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Data-driven power battery fault diagnosis method and system for electric vehicles that leverages advanced data analytics to improve reliability and maintainability. The system employs machine learning algorithms to analyze complex battery health data, including parameters like state of charge, state of health, temperature, and charge cycles, to detect potential battery faults. This approach enables proactive predictive maintenance by identifying anomalies before they lead to operational failures, reducing downtime and increasing overall vehicle reliability.

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AI-based battery fault diagnosis for electric vehicles that improves accuracy and safety through real-time monitoring and analysis. The method employs machine learning algorithms to analyze battery data from the Battery Management System (BMS) and other sensors to identify and diagnose battery pack faults. The system provides detailed diagnostic reports, including location, cause, and resolution steps, enabling proactive maintenance and reducing the risk of battery-related accidents.

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Thermal runaway protection system for electric vehicles that enables continuous monitoring of the battery pack even when the vehicle is powered off. The system integrates thermal monitoring, fire detection, and automatic circuit interruption capabilities to prevent thermal runaway incidents during vehicle shutdown. The system continuously monitors the battery pack temperature when the vehicle is stopped, triggering circuit interruption when thermal runaway is detected. This ensures safe operation of the vehicle even in situations where the battery management system fails.

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Predicting battery failures through advanced predictive analytics that proactively identifies potential battery degradation before it causes power outages. The method analyzes historical battery data to predict future battery performance, classifying potential failures based on predicted degradation patterns. This enables proactive maintenance by predicting when battery degradation is likely to occur, allowing for timely replacement or repair before a failure event occurs.

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