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.

Source

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.

Source

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.

Source

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.

Source

Battery monitoring device and method for energy storage systems that provides real-time battery status monitoring and protection at the rack level. The device integrates battery management functionality directly into the cable assembly of an energy storage system. It monitors battery charging/discharging modes, voltages, and currents. The monitored data is analyzed for abnormalities and used to calculate battery health cycles. If abnormalities are detected, an automatic shutdown function is triggered to protect the rack. This allows localized battery monitoring and protection without requiring additional external components for each battery.

Source

Energy storage battery early warning system that provides improved real-time performance and reduced computing pressure compared to centralized battery management systems. The system uses a hierarchical architecture with localized battery monitoring units that preprocess and filter battery performance data before sending it to a central battery management system. This reduces the amount of raw data the BMS needs to handle and allows faster fault detection compared to sampling all cells at once. The local units also perform initial fault analysis and trigger local alarms before sending detailed data to the BMS for further investigation.

Source

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.

Source

Signal transmission system for monitoring and managing energy storage systems that provides real-time monitoring, flexibility, and stability for remote monitoring of energy storage systems. The system uses a wired transmission network established around the storage system for reliable monitoring. Analog signals are converted to digital and transmitted over a separate channel. This allows real-time monitoring of key components using wired analog signals. Digital signals are transmitted over a separate channel for higher bandwidth and flexibility. Preprocessing, battery status assessment, and management decisions are made based on the digital signals. This provides real-time monitoring and flexible management while leveraging the reliability of wired analog signals.

Source

Battery state monitoring system for personal mobility vehicles like power chairs that uses neural networks to accurately predict battery health and remaining charge without requiring additional sensors or hardware inside the battery. The system uses sensors on the vehicle to gather voltage, current, and temperature data. A microcontroller processes this input data through neural network models to calculate battery state metrics like charge level and health without needing raw battery data. This reduces transmitted data by thousands while still providing accurate state estimates. The microcontroller can store the results or transmit them over the internet. The neural networks are trained with a range of operating states to accurately predict battery health.

Source

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.

Source

Battery system for electric vehicles that allows detecting leaks inside the battery housing to prevent safety issues. The system has a liquid detector in the tray between the housing frame and tray base that connects to the battery bus bar. It monitors the cooling liquid level and tray liquid level, and compares declines vs increases. If the cooling liquid is leaking into the tray, it indicates a problem. This allows detecting cooling liquid leaks inside the housing before they reach the battery cells.

Source

Battery cell monitoring system for detecting abnormalities in individual cells of a battery pack. The system uses sensors on each cell to continuously monitor parameters like voltage, temperature, and resistance. Anomaly detection models are trained using cell parameter sets to identify when a cell is in an abnormal state. This allows targeted processing and management of cell issues rather than just overall pack health. The models also predict explosion times for cells with serious issues. By detecting abnormalities early and accurately, it enables timely mitigation of cell problems to improve safety and reliability of battery packs.

Source

Battery system for electric aircraft that prevents thermal runaway propagation between modules to avoid total system failure. The system uses thermal barriers that transition to a lower conductivity state when cells exceed a certain temperature. This reduces heat transfer from compromised cells to the shared cooling structure, preventing thermal runaway spread. The barriers transition from a high initial thermal conductivity state to a lower state when cell temperatures exceed a threshold. This prevents thermal runaway propagation between modules by limiting heat transfer from overheated cells. The shared cooling structure is thermally coupled to all modules to dissipate cell heat.

Source

Detecting thermal runaway of battery cells in electric vehicles to quickly alert occupants of potential fire hazards. The system uses multiple sensing lines to monitor battery voltages. Main lines connect to individual cells to measure their voltages. Auxiliary lines connect to input and output terminals of the battery module. By comparing the sum of cell voltages to the module voltage, abnormalities in cells or main lines can be detected. If the cell sum and module voltages match, all is normal. If they differ, further checks are made. If module voltage is normal, the main lines are faulty. If module voltage is abnormal, the cells have runaway. This rapid detection allows warning occupants before serious damage occurs.

Source

Intelligent system for predicting battery life using real-time monitoring and machine learning models. The system continuously monitors battery parameters like voltage and current, builds prediction models based on the data, and uses them to forecast remaining battery life. It also corrects model predictions using comparison with actual battery aging. This allows accurate prediction of battery life without needing extensive testing.

Source

Detecting thermal events in battery packs of electrified vehicles while accounting for faulty sensors. The method involves flagging temperature readings from sensors as reliable or unreliable based on assessments. If a sensor is flagged reliable, its temperature reading is used to detect thermal events. But if a sensor is flagged unreliable, its temperature reading is ignored. This prevents using inaccurate readings from faulty sensors to falsely trigger thermal event alerts.

Source

A user-oriented battery health monitoring system for IoT devices that provides comprehensive battery health monitoring and alerting. The system collects battery operating data and charge/discharge data, sets work alarm thresholds, and uses a health state assessment model to accurately monitor and alert on battery health. This involves constructing target feature parameters from charge/discharge data, calculating capacity sequences, and building a health state assessment model based on those parameters. This allows monitoring and alerting on both battery capacity degradation and operational issues.

Source

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.

Source

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.

Source

Battery state detection device for vehicles that improves accuracy and convenience. The device has a removable sensor unit to measure battery voltage, current, and temperature. It also has a separate communication unit with wired and wireless interfaces. The sensor unit connects to the battery and sends data via the wired connection. The communication unit receives the battery state info and wirelessly transmits it to external devices like phones and servers. This allows remote monitoring and analysis of battery health. The communication unit also receives reference info from external sources to improve battery state estimation accuracy.

Source