EV Battery State Monitoring Techniques

Battery management system and method that provides real-time battery monitoring and predicts battery performance based on accumulated usage. The system connects a display panel to a battery pack to show battery status like voltage, current, and temperature in real time. It calculates cumulative usage by comparing actual battery data to a model. Using this, it predicts battery performance and adjusts charging/discharging to extend life. When a battery is nearing end-of-life, it determines recyclability based on the accumulated usage.

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Battery monitoring and management system for predicting battery degradation and making maintenance recommendations. The system uses big data analysis to evaluate battery health and predict degradation. It acquires battery voltage, current, temperature, capacity, discharge rate, etc. through sensors. The data is stored and analyzed to calculate health indices and degradation rates. A decision tree algorithm uses these to generate maintenance reminders, warnings, and charging strategies. A user interface displays battery status and alerts.

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Elevator monitoring device that more accurately monitors the deterioration state of the elevator's secondary battery. The device calculates the battery's full charge capacity based on the average temperature during the monitoring period. It derives a life curve function for the battery based on this average temperature and uses it to calculate the current full charge capacity. If the calculated capacity falls below a warning threshold, it notifies a warning. This improves accuracy compared to using a fixed life curve function for one temperature.

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Battery management system that estimates battery state of charge (SOC) more accurately by leveraging nonlinear characteristics of the battery's open circuit voltage (OCV) near full discharge. The system uses an extended Kalman filter to estimate SOC from battery measurements. But instead of just using the Kalman filter output, it also calculates OCV based on the provisional SOC estimate. Then it adjusts the SOC estimate using a measurement update with both the provisional SOC and OCV. This leverages the strong nonlinear OCV-SOC relationship near full discharge to improve SOC estimation accuracy.

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Battery monitoring and management system that uses compacted sensor data to generate insights and alarms for battery health and performance. The system receives sensor data from temperature, voltage, and current sensors of a battery. It compacts the data into coefficients and generates performance indicators based on those coefficients. Alarms are then generated when performance indicators exceed thresholds. This allows efficient monitoring and management of large amounts of battery sensor data by compacting and analyzing key performance metrics instead of storing and processing raw sensor data.

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Vehicle-pile collaborative state monitoring system for electric vehicles that accurately predicts battery life during charging and discharging. The system monitors parameters of the vehicle battery, charging pile, and charging/discharging state. It uses a model to predict battery life based on these parameters. Multiple influencing factors like charging cycles are combined to correct and improve the life prediction accuracy.

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An online adaptive method to estimate and control battery parameters like remaining useful life (RUL) and state of charge (SOC) for electric vehicles. It uses a combination of extended Kalman filtering and recurrent neural networks (GRU) to provide accurate and adaptive battery parameter estimation. The method involves predicting SOC using Kalman filters, then using the predicted SOC along with current battery and environmental data to predict RUL using a GRU neural network. The adaptive component lies in dynamically adjusting battery internal and external parameters based on the predicted RUL.

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A method for estimating the health status of batteries in energy storage systems that combines Kalman filtering with a neural network to accurately estimate battery health using only online sensor data. The method involves using Kalman filters to estimate key battery parameters like state of charge, then feeding that data into a long short-term memory neural network to predict battery health. This allows estimating internal battery degradation without requiring invasive measurements.

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Online monitoring and management of lithium-ion batteries using deep learning networks to improve efficiency, longevity, and safety of battery systems. The method involves real-time monitoring of battery parameters, generating balanced charging profiles, analyzing performance, predicting battery life, and detecting abnormalities using deep learning models. It leverages data processing, trend analysis, feature extraction, and machine learning techniques to provide accurate battery condition assessment and maintenance recommendations.

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Integrated battery management system for lithium batteries in electric vehicles that improves charging and discharging efficiency and protects battery health. The system monitors battery parameters during charging and discharging to enable pre-regulation. It determines charging power based on time and battery state, synchronizes with redundant power, and calculates charging time using user habit analysis. This allows lower charging currents to protect the battery while ensuring sufficient charging.

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A method and system for monitoring the internal temperature of lithium-ion batteries in energy storage systems to improve safety and longevity. The method uses three metrics - voltage consistency difference, self-discharge index, and temperature consistency difference - to detect internal short circuits. It then uses swarm optimization algorithms like bee colony optimization and sparrow optimization to efficiently find the optimal battery temperature. This temperature is used to control the battery cooling system using a PID controller.

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Adaptive battery monitoring system for electric vehicles that provides accurate and real-time battery state estimation and prediction using a multi-parameter monitoring approach. The system uses sensors to measure voltage, current, and temperature of the battery. It calculates state of charge, capacity, and available energy based on voltage. The temperature data improves capacity estimation accuracy. An adaptive algorithm designs optimal monitoring parameters based on battery type and conditions. The system provides adaptive and accurate battery monitoring compared to static methods like open circuit voltage measurement.

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Real-time monitoring and control of individual rechargeable battery cells in a battery bank to detect and prevent faults, optimize performance, and extend life. The method involves measuring voltage, current, and temperature of each cell using onboard sensors, and sending the data to a central unit. The unit calculates cell state, health, charge/discharge times, and replacement time. It stores the data and compares against ranges. If outside, it initiates preventive/corrective actions like regulating energy flow or alerting. This allows precise real-time monitoring and control of each cell to anticipate and address issues before they spread to the bank.

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Power battery monitoring method that provides early warning of battery degradation by tracking battery usage patterns. The method involves calculating the state of charge, output power, and operating temperature during discharge, constructing scatter plots, and fitting curves to analyze trends. It predicts future values and generates warnings if they exceed normal ranges. This provides more comprehensive monitoring compared to just capacity or state of charge.

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Cloud-based Internet of Things (IoT) enabled battery management system that provides real-time battery data, state of charge (SOC), state of health (SOH) estimation, battery cooling control, and outlier detection. The system uses IoT sensors to collect battery data, cloud storage to store the data, and a mobile app to access it. Finite state machines calculate SOH based on factors like outlier current/voltage and cycle count. A thresholding technique identifies outliers. If battery temperature exceeds a threshold, a cooling system engages.

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Predicting battery condition of a lithium-ion battery in a vehicle to enable early detection of battery degradation and failure. The prediction method involves calculating quaternionic state variables from measured battery parameters like current, voltage, and time derivatives. These quaternionic variables better reflect the battery's time-varying behavior compared to static variables like impedance or SOC. By tracking the time course of these quaternionic variables, critical battery conditions like degradation or safety issues can be detected earlier.

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A system for monitoring charge level and predicting remaining life of lithium-ion batteries to improve safety and reliability. The system uses sensors to continuously track voltage, temperature, and current in battery packs. It estimates state of charge using a Kalman filter algorithm. Then, a battery life prediction algorithm calculates remaining useful life based on the estimated state of charge. The system allows online monitoring and prediction of battery health to mitigate safety issues like overcharging and overdischarging.

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A system for managing battery packs in electric vehicles using KNN machine learning to predict battery aging and optimize pack performance. The system monitors charging/discharging states of the pack cells and uses KNN to forecast aging changes. It then controls pack operation to mitigate aging and maintain optimal performance. By predicting cell aging and taking proactive measures, it aims to extend pack life beyond normal limits.

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A system for accurately predicting battery life in electric vehicles using a combination of machine learning, real-time data collection, and predictive analysis. The system continuously monitors battery parameters like charge level, cell voltages, and temperature. It uses this real-time data along with historical data and external factors to make more accurate battery life predictions.

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Method for estimating the degradation of vehicle batteries without time or space constraints by collecting battery current, temperature, and terminal voltage data during normal vehicle operation. The method involves calculating polarization voltage, open circuit voltage, and state of charge from the collected data. The battery degradation is estimated by comparing the state of charge from the current operation to the stored value from the previous operation. This allows accurate degradation estimation during driving without requiring specific charging conditions.

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