BMS-Driven Condition Control in EV Batteries

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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Predicting battery health degradation in a vehicle battery pack using multiple batteries to improve reliability and avoid unexpected failures. The method involves measuring electrical parameters from both batteries during cranking, calculating spreads between them, and forecasting battery health based on spread rate changes when ignition is off. This allows detecting degradation in a single battery before failure by monitoring its spread with a healthy battery. The forecast helps plan maintenance.

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Monitoring the health of battery cells in an electric vehicle pack using a reference cell that has been artificially aged to accelerate degradation. The reference cell is charged and discharged more times than the operating cells to simulate aging. A sensing device measures physical characteristics like voltage, temperature, and acoustic emissions from the reference cell. These readings are used to determine the health status of the operating cells by comparing to the aged reference cell. This allows detecting latent battery issues before they escalate like thermal runaway. The technique involves applying a reference current to the aged cell that matches the load current of the operating cells.

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Remote monitoring and analysis of internal parameters of a power battery to improve battery management and maintenance. The method involves real-time sampling of voltage, current, and temperature inside the battery using a sensor group. The data is transmitted wirelessly to a battery management system for analysis using techniques like clustering, neural networks, and energy consumption calculations. This allows understanding battery states, optimizing usage strategies, and extending life by monitoring internal parameters in real time and remotely.

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Method for monitoring a battery system like electric vehicle packs to improve safety and efficiency. It involves using sensors in the batteries to monitor parameters like voltage and temperature when the battery management system is sleeping. If a violation occurs, it wakes up the system. But if no violation is seen after a set time, it wakes up anyway based on predicted values. This allows proactive monitoring and intervention before failures. The predicted values are calculated using slope analysis of the sensor data. This lets the system wake up earlier than just waiting for violations. It also allows individual adaptation of wake-up times and thresholds for each sensor based on cell variations.

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Smart battery monitoring system to improve battery reliability, performance, and lifespan. The system uses a central controller connected to a battery pack monitoring host and a battery management system host. The battery pack host has modules to measure voltage, temperature, internal resistance, and SOC/SOH of individual batteries. The battery management host monitors pack voltage, current, and temperature. The controller, monitoring center, and cloud server provide real-time monitoring, protection, and early warning against issues like thermal runaway, open circuits, overvoltage, etc. The system enables comprehensive battery performance analysis, balancing, and optimization to extend battery life.

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Battery electric vehicle system with improved thermal runaway propagation (TRP) control. The system uses diodes integrated into each battery module to automatically bypass faulty modules during TRP events. This allows the healthy modules to provide power to critical loads like cooling systems and propulsion functions when a module fails open. This prevents total pack failure and enables limited functionality during TRP events. The diodes bypass the faulty module cells while maintaining power to the bus and critical loads.

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Battery monitoring system that continuously tracks battery parameters like voltage, current, temperature, and resistance over the life of the battery. It uses a battery-integrated monitor with sensors, a controller, memory, and wireless communication. The monitor can be powered by the battery itself using energy harvesting. This allows capturing detailed usage data for analyzing battery degradation, performance, and recycling. It also facilitates sorting and recycling by discharging batteries before shredding to prevent explosions. The monitor can also disable cells in series or open parallel cells to protect the battery pack.

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Smart battery management system (SBMS) that predicts and prevents battery failures in advance using sensors and machine learning. The SBMS monitors metrics like pressure, temperature, voltage, current, and capacitance from cells. It predicts failures using a trained neural network. If a cell failure is predicted, the SBMS disconnects the cell to prevent damage. This allows load balancing and disconnecting cells before thermal runaway or other failures occur. The SBMS can also provide visual representations of SoH, temperatures, pressures, etc. throughout a pack.

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A battery pack design and control method to prevent thermal runaway propagation in electric vehicle battery packs. The battery pack has a case with a cavity containing the battery cells. A spray system is installed inside the case that can be activated in case of a thermal runaway event in one cell. The spray system sprays a fire suppressant into the cavity to extinguish the runaway cell and prevent further propagation. The suppressant is a material with a low melting point that turns into a liquid at the high temperatures encountered during runaway. This helps absorb and dissipate the heat from the runaway cell to contain it. The control method involves monitoring cell temperatures and activating the suppressant spray system if a cell reaches a certain threshold indicating runaway.

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Active cell monitoring system for a multi-cell rechargeable battery that proactively monitors battery cells to prevent thermal propagation events, enable prognostic features, and perform high coverage diagnostics. The system uses sensors to periodically measure cell parameters like voltage, temperature, pressure, etc. A microcontroller analyzes the sensor data to detect faults and communicate them to the battery management system. This allows proactive monitoring and diagnosis of cells even when the vehicle is off. The periodic wake-up mechanism conserves power when the vehicle is sleeping.

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Lithium-ion battery management system using deep learning for accurate battery health monitoring. The system has modules for environmental sensing, status monitoring, human-computer interaction, display warning, and deep learning. It collects battery parameters like voltage, temperature, and impedance. The deep learning model uses historical data to accurately reflect battery electrical characteristics under different conditions. It provides reference for charge and health estimation. The model is trained based on individual battery differences. The system can update the training data as needed for more accurate results.

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Intelligent battery monitoring at the individual cell level using flexible electronics to improve safety and performance of batteries in applications like electric vehicles. The monitoring system has a flexible substrate with integrated sensors, processing, and communication components. It uses machine learning to learn characteristic signals for normal cell states and detect anomalies for issues like thermal runaway. The flexible form factor allows conforming to cell shapes. The on-cell monitoring provides early warning of cell issues before they escalate.

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Battery monitoring system that can accurately and effectively predict thermal runaway of lithium-ion batteries in real time, integrated into the battery itself. The system uses sensors inside the battery to monitor parameters like temperature, voltage, and current. A processor analyzes the sensor data to determine the battery's state. The processed information is then reported to external devices through two separate circuits, one connected to the positive terminal and the other connected to the negative terminal. This allows monitoring the battery's health without affecting capacity or cycle performance.

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Smart battery system for electric vehicles that uses AI, IoT, and blockchain to monitor, control, and manage rechargeable batteries in real-time. The system connects the battery monitoring module and control module to a smart battery management platform and blockchain network. It extracts battery data, analyzes it, predicts health, renders simulations, and sends control signals. It manages batteries via charging stations and provides features like battery life prediction, thermal management, fault detection, and location services.

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Multi-dimensional perception battery management system for energy storage systems that improves battery safety and health monitoring by using sensors inside and outside the battery. The system has modules for safety detection, infrared sensing, battery management, and cluster management. It collects data like gas, pressure, temperature, voltage, current, and capacity from inside the battery, as well as external temperature and gas. This comprehensive sensing allows accurate calculation of battery state like SOC/SOH and safety status. It provides better battery life, charge/discharge efficiency, and safety compared to conventional narrow sensor battery management systems.

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Battery health monitoring system that accurately determines state of health (SoH) of a battery system without needing full charge/discharge cycles. The system uses a preprocessor to normalize rate of change data from sensors during charging/discharging. This normalized data is then fed into a neural network to determine SoH. The normalization reduces network complexity and training data requirements. It works by focusing the network on detecting horizontal shifts in normalized data patterns rather than amplitude variations. This allows consistent SoH estimation across cell types.

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Electrochemical energy storage unit, sensor device and related method for predicting and warning of thermal runaway in Li-ion batteries. The method involves monitoring pressure increases in the battery using a sensor with two repetition rates, one faster than the other. If the faster rate pressure increase exceeds a threshold, it indicates an initial cell issue. If the slower rate pressure increase exceeds a higher threshold, it indicates a spreading thermal runaway. This second event triggers an output signal to the control unit for actions like waking it, warning, relieving pressure, shutting off, or disconnecting the battery.

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A vehicle battery system that can prevent damage from overcharging and cell failures. The system has an overcharge limit device that individually disconnects cells with high pressure. When a cell is disconnected, the controller stops controlling that cell and continues operating the rest. This prevents further overcharging. If multiple cells are disconnected, it stops all cells. This prevents overcharging of remaining cells. It also excludes disconnected cells from balancing and lowers the overall battery output limit.

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Bidirectional lithium-ion battery health monitoring using machine learning techniques to accurately predict battery faults. The method involves collecting historical charge/discharge data, simulating real-time data, generating synthetic fault data, training a fault prediction model, and feeding back fault categories to the battery management system. This amplifies the fault dataset size and improves prediction accuracy compared to just using historical faults.

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