Early Fault Detection in EV 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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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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A data-driven method for predicting battery thermal runaway in electric vehicles using machine learning. The method involves preparing and cleaning battery usage data from EVs, integrating it into a feature engineering process, and training machine learning models to accurately predict battery temperature evolution and thermal runaway risk. It leverages big data techniques to overcome challenges of real-time monitoring and prediction of battery thermal runaway without invasive sensor monitoring. The method uses machine learning models trained on historical battery usage data to predict thermal runaway based on current operating conditions.

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Online estimation of lithium-ion battery health status for space applications through a dynamic state-space model that integrates real-time measurements. The estimation utilizes a modified state-space model where the battery health factor is defined by a sequence of isoelectric voltage differences, enabling real-time monitoring of the battery's state. This approach provides accurate and reliable health assessment during operational conditions, particularly challenging due to the battery's dynamic degradation characteristics. The method employs a modified particle filter algorithm with improved adaptability to non-linear systems, enabling accurate estimation of battery health status even in complex operating conditions.

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Fault diagnosis for series hybrid electric vehicle AC-DC converters using a novel pulse-based method. The method employs a unique pulse-based diagnostic approach that leverages the converter's pulse waveform characteristics to detect faults. The diagnostic process involves generating a specific pulse sequence that is uniquely characteristic of converter operation, and analyzing the response to this sequence to identify faults. This pulse-based approach enables rapid and accurate fault detection in hybrid electric vehicle AC-DC converters.

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Battery life monitoring device and method that enables proactive battery replacement by detecting internal resistance changes due to aging. The device measures battery temperature and battery cell resistance, then continuously monitors the battery's state of charge and discharge patterns. When the battery's aging state exceeds predetermined thresholds, it triggers a replacement recommendation through user interface.

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Improved consistency detection for lithium-ion battery packs through a simplified and accurate method. The detection system uses a single set of parameters to evaluate the battery pack's voltage consistency, eliminating the need for complex calculations involving multiple parameters. The system employs a voltage calibration reference point, along with parameters for internal resistance dispersion and voltage platform deviation, to determine battery pack consistency. This approach streamlines the detection process while maintaining accuracy, enabling reliable battery pack assessment for new energy vehicles.

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A method for rapidly assessing lithium-ion battery self-discharge consistency through accelerated testing. The method employs a controlled temperature environment to enhance battery self-discharge rates, enabling faster analysis compared to traditional methods. The accelerated testing process involves maintaining the battery at specific charging and discharging temperatures while monitoring its voltage response. By comparing the voltage change before and after charging/discharging, the method identifies batteries with consistent self-discharge characteristics, allowing for rapid selection of high-quality cells for battery assembly.

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A method for evaluating consistency of self-discharge in lithium-ion batteries through a simplified, automated approach. The method replaces traditional voltage-based screening by analyzing the difference between the battery's initial and final states after discharge. This approach eliminates the need for manual testing and reduces production costs compared to traditional methods. The method uses a single measurement point during discharge, eliminating the need for separate voltage measurements before and after discharge. This approach enables rapid consistency testing without the complexity of multiple parameters and provides a reliable indication of battery performance.

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Monitoring battery thermal state through voltage monitoring during charging to detect rapid temperature increases. The method detects when the battery voltage drops below a predetermined threshold before the voltage increases, indicating a potential thermal condition. This early warning enables proactive intervention to prevent temperature-related issues before they escalate into thermal runaway.

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Fault diagnosis method and system for electric drive vehicles that improves accuracy by leveraging multi-level data filtering. The method employs a Gaussian distribution-based approach to collectively filter fault-free data, enabling precise positioning and elimination of complex faults. The system comprises an acquisition module, a screening module, a probability module, and a judgment module. The acquisition module captures vehicle data, while the screening module applies multi-level filtering to remove anomalies. The probability module calculates fault probabilities based on the filtered data, and the judgment module determines fault severity. This integrated approach enables accurate fault diagnosis while maintaining timeliness.

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Predictive maintenance of electric vehicle batteries through machine learning-based analysis of real-time driving data. The method employs large-scale data processing and machine learning algorithms to identify patterns in battery performance that predict potential failures. The analysis involves data cleaning, feature engineering, and model development, followed by model evaluation and selection. The approach enables proactive battery maintenance by identifying critical battery states and predicting potential failures through predictive analytics, enabling early intervention and reduced maintenance costs.

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Lithium battery fault diagnosis using improved evidence theory for enhanced reliability in electric vehicles. The method employs a data-driven approach to analyze battery performance data through a comprehensive evidence theory framework, specifically leveraging support vector machines (SVM) and probabilistic neural networks. By integrating data from various sources and leveraging the support matrix, the method improves the accuracy of battery health status determination, particularly in cases of battery inconsistency. This approach enables early detection of potential battery failures through data-driven analysis, thereby ensuring optimal battery performance and reducing the risk of battery pack failure.

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A battery pack modeling method that accurately captures the performance inconsistencies between individual cells in a pack. It uses probability and statistics to extract distributed regularities from cell test data, rather than assuming uniformity. This allows modeling the pack as a distributed system of cells, accounting for variations in parameters like voltage, capacity, and resistance. It provides a more realistic and accurate model of non-uniform battery packs compared to simplistic linear amplification of cell models.

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Consistently estimating battery health through the integration of charge state and capacity metrics. The method calculates the state of health (SOH) of the battery pack by considering both the total capacity and the charge state of individual cells. It updates the SOH value based on the battery's capacity degradation over time, while maintaining consistency between cell-level characterization and overall pack health. This approach ensures accurate health estimation across battery cells, enabling reliable pack-level monitoring.

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A method and system for accurate and reliable battery state of charge (S0C) estimation that eliminates the limitations of traditional methods. The method employs a hybrid approach combining direct measurement of cell state, advanced resistance profiling, and machine learning algorithms to accurately predict battery state. The system includes a flowchart that iteratively measures cell state, conducts advanced resistance profiling, and applies machine learning to predict S0C. This approach provides precise and stable S0C estimates, particularly critical for battery management systems in electric vehicles.

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A battery management system for electric vehicles that accurately assesses battery health through real-time data analysis of driving habits and vehicle performance. The system integrates with CAN bus sensors to collect critical parameters like speed, acceleration, braking, and rest times, which are correlated with battery degradation. By analyzing these parameters in conjunction with vehicle performance metrics, the system provides a comprehensive assessment of battery health, enabling proactive maintenance and predictive capacity management.

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Predicting battery life in electric vehicles using online machine learning algorithms that enable real-time monitoring of battery health. The method employs an ELM-MUKF architecture that continuously updates battery state estimates based on sensor data, enabling dynamic monitoring of battery condition. This approach enables accurate predictions of remaining battery life in real-time, allowing proactive maintenance and predictive maintenance strategies to be implemented.

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Battery deterioration monitoring for hybrid vehicles through advanced cell-level analysis. The method measures the degradation of individual battery cells within the pack, enabling early detection of pack-level performance degradation. This enables proactive maintenance and replacement of the battery pack when necessary, ensuring reliable hybrid vehicle operation and preventing potential accidents.

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Accurately modeling parallel connected battery systems using a compensator technique to improve prediction accuracy. The method involves creating a battery model for each cell in the parallel system using known cell parameters. Then, during operation, measured currents from each cell branch are used to compensate the model parameters based on the compensator design. This compensated model provides more accurate predictions of cell voltage and charge/discharge characteristics for the parallel system. The compensator detects branch currents and adjusts cell parameters to account for inconsistencies in parallel operation.

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Detecting battery cell consistency without opening the battery pack by analyzing voltage characteristics of individual cells during charging and discharging cycles. The method measures voltage deviations between adjacent voltage curves for each cell, calculates correlation coefficients between cell pairs, and evaluates the coherence parameter of the voltage characteristic curves. This enables rapid detection of cell inconsistencies through simple voltage measurements, significantly reducing the time required for battery pack consistency testing.

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A method for precise battery state-of-health (SOH) estimation in electric vehicle batteries. The method employs a novel combination of open-circuit voltage monitoring and Kalman filtering to accurately predict battery health. The approach uses a dynamic battery model that accounts for the complex nonlinear behavior of lithium-ion batteries, enabling precise SOH estimation even in non-operational states. The model is constructed based on the battery's internal resistance, state of charge, and temperature, and is derived from advanced battery management system (BMS) data. The Kalman filter algorithm provides real-time, continuous monitoring of the battery's state-of-health, enabling early detection of degradation and proactive maintenance.

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A lithium-ion battery life prediction method that combines principal component analysis (PCA) and Bayesian updating to achieve more accurate and reliable predictions. The method employs PCA to extract underlying features from the battery's performance data, followed by Bayesian updating to refine the degradation model parameters. This approach enables real-time monitoring of battery degradation while maintaining precision beyond traditional point predictions. The method's Bayesian updating incorporates confidence intervals to provide both point estimates and probabilistic predictions, enabling battery owners to understand the reliability of their battery's remaining capacity.

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A method for improving lithium-ion battery performance through dynamic voltage monitoring and separator design. The method involves monitoring battery voltage and resistance using high-speed current pulses and voltage sensors during charging and discharging cycles. This comprehensive monitoring enables the detection of voltage deviations that indicate separator failure, allowing for real-time separator replacement. The method achieves improved battery pack performance by automatically identifying and replacing damaged separators during charging and discharging operations.

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Improved lithium-ion battery screening method that enhances consistency through dynamic polarization analysis. The method employs a novel approach that dynamically monitors battery performance during charging and discharging cycles, particularly at the 10-20% and 80-90% states. By analyzing the characteristic differences in internal resistance and capacity degradation during these critical states, the method identifies potential inconsistencies that can be addressed through specific cell selection and configuration. This approach addresses the limitations of traditional static separation methods by capturing the dynamic changes in battery behavior during charging and discharging.

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Dynamic battery group consistency detection method for power batteries that enables accurate monitoring of battery pack performance and charging/discharging conditions. The method measures voltage deviations across individual battery cells, calculates their standard deviations, and computes voltage coefficients. It also monitors standard deviation thresholds and coefficient ranges for battery pack operation, enabling precise analysis of individual cell performance.

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Improved battery modeling for precise state of charge estimation through consideration of the battery's response characteristics, particularly during high-current charging and discharging cycles. The method involves building an equivalent circuit model that incorporates the battery's internal resistance, polarization resistance, and capacitor characteristics. This model is then used to analyze the current response of the battery under different operating conditions, providing more accurate state of charge estimation compared to traditional methods.

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Evaluating battery health through charging mode selection to prevent degradation. The method assesses the impact of charging modes on battery performance, particularly during prolonged charging periods. It analyzes the battery's capacity, state of charge, and temperature to determine optimal charging parameters for each mode, providing detailed insights into the selected charging mode's impact on battery health. This enables drivers to make informed decisions about charging modes that balance performance, safety, and longevity.

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A dynamic evaluation method for battery consistency that addresses the limitations of traditional static testing. The method measures battery performance through real-time monitoring of current, voltage, and internal resistance during discharge or charge cycles. By analyzing these dynamic parameters, it identifies deviations from expected behavior that indicate battery degradation. This approach enables the detection of performance anomalies that may not be captured by traditional static testing methods, particularly in complex battery configurations like parallel and series configurations.

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