Gas Generation Detection in EV Batteries

Early detection and response system for battery thermal runaway to mitigate risks and protect against battery fires in electric vehicles. The system uses multiple sensors in the battery packs and modules to monitor properties like strain, hydrogen gas, pressure, temperature, CO/CO2, and current. An algorithm analyzes the sensor data to detect abnormalities indicating potential thermal runaway. If a risk is detected, high-power sensors like gas sensors are selectively activated to confirm. If elevated gas levels are found, remedial actions like battery disabling are taken before thermal runaway occurs. The system provides earlier and more accurate detection compared to conventional methods.

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Battery pack monitoring system for electric vehicles that can detect thermal runaway in individual cells and initiate safety measures when needed. The system uses piezoelectric sensors attached to the battery pack housing to detect dimensional changes due to internal cell pressure increases. Excessive deformation indicates cell degassing and potential runaway. The sensors are located on opposite sides and outside surfaces of the housing to detect pressure-induced expansion. By monitoring housing deformation over time, runaway can be detected before cell rupture. This allows targeted cell shutdowns and cooling to prevent propagation.

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Early detection of thermal runaway events in batteries, like those used in electric vehicles, by detecting the initial venting of gases during the runaway process. A sensor measures the thermal conductivity of the gas atmosphere inside the battery. Changes in conductivity due to venting can be detected as an indicator of an impending thermal runaway. An apparatus with interface and processing circuitry receives the conductivity measurement and determines if venting has occurred. This allows early warning of potential thermal runaway events to mitigate safety risks.

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Early warning system to detect thermal runaway in electric vehicle batteries using acoustic sensors. The system monitors sound waves emitted from battery cells to predict thermal runaway before it escalates. Acoustic sensors detect low frequency infrasound generated by gas bubbles forming during early stages of thermal runaway. An algorithm analyzes the acoustic data to predict thermal runaway. This allows earlier intervention to prevent escalation compared to temperature sensors.

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Alert system for predicting battery thermal runaway in electric vehicle packs, using a gas sensor in a degassing unit attached to the battery housing. The sensor detects gas properties like CO2 concentration inside the battery. A processor analyzes the sensor data to estimate the probability of future thermal runaway. The degassing unit has a semipermeable membrane allowing gas exchange but preventing liquid/solid leakage. This isolates the battery interior from external conditions.

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Fire suppression system for battery packs in vehicles that detects and responds to cell failure gases and fire conditions. The system uses detectors inside battery packs to sense leaked gases like hydrogen and carbon monoxide, as well as infrared radiation. When certain concentration thresholds are reached, an alarm is triggered and a fire suppressant is released into the battery pack. This cools the cells and prevents thermal runaway. The system also dispatches an alarm to the vehicle operator.

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Fire suppression system for battery packs to prevent and extinguish fires in electric vehicle batteries. The system uses external gas detectors to monitor battery cell gases like hydrogen and carbon monoxide, as well as infrared radiation. When detectors sense elevated gas levels or infrared, a controller triggers an emergency fire suppression system to release a cooling agent into the battery packs to smother flames. This prevents runaway cell fires from spreading. The external gas detection allows early intervention before internal sensors are affected.

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Energy-efficient gas monitoring for electric vehicle batteries that reduces power consumption and extends sensor lifetime while still detecting gas leaks. The monitoring system has a gas sensor unit with multiple sensors that can be woken up selectively to measure gas levels. The sensors are in an idle state most of the time to save power. They are woken up based on events like temperature increases or accidents to check for leaks. This allows targeted sensor activation instead of continuous monitoring.

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Monitoring system to detect and warn of potential thermal runaway in batteries before explosions or fires occur. The system uses sensors inside the battery enclosure to measure the amount of gas released during charging/discharging cycles. It also monitors the ambient atmosphere around the battery. By comparing the relative percentage changes in battery gas vs ambient gas, it can differentiate between normal battery emissions vs abnormal thermal runaway gases. This allows earlier warning of impending thermal runaway compared to just monitoring battery gas alone.

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Monitoring system for batteries to detect early signs of thermal runaway, the condition that can lead to battery explosions. The system has a gas sensor inside the battery enclosure to detect gas released by the battery. It also has a separate gas sensor outside the enclosure to measure ambient gas levels. The system compares the percentage change in battery gas signal to the percentage change in ambient gas signal. If the battery gas signal percentage change exceeds the ambient gas signal percentage change, it indicates an abnormal increase in battery gas and could signal an impending thermal runaway. This allows early warning before explosive conditions occur.

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Method for detecting thermal runaway gases in batteries using machine learning to improve accuracy and reliability of gas detection compared to existing methods. The method involves preprocessing and feature extraction of sensor data, training a convolutional neural network (CNN) model to predict gas concentrations, and setting up a dual alarm mechanism for out-of-control abnormality verification. The CNN model extracts spatial features from sensor data and performs regression to predict gas concentrations. Regular training with machine learning algorithms improves model accuracy. The dual alarm mechanism checks gas data against known standards to verify abnormalities.

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Early detection and mitigation of thermal runaway in electric vehicle (EV) batteries to prevent fires and damage. The system uses sensors to detect outgassing, a precursor to thermal runaway, and alerts the fleet management system. This allows proactive response like evacuating the vehicle or redirecting assignments. On-board cooling and fire suppression can also be triggered. In EV facilities, the system identifies nearby vehicles and takes actions to protect them if a battery is outgassing.

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Remote monitoring of trace gases in lithium iron phosphate (LiFePO4) batteries using LoRa wireless communication to improve battery management safety. The method involves placing gas sensors inside LiFePO4 batteries to detect gases like hydrogen, hydrogen sulfide, and carbon dioxide. The sensor readings are wirelessly transmitted using LoRa to a remote monitoring device. The LoRa communication allows monitoring of large-scale battery installations without needing wired connections. The data is encrypted and checked for integrity to ensure secure and reliable transmission. The remote monitoring enables real-time detection and alerting of abnormal gas levels, which can indicate battery degradation or failure.

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A system for diagnosing the health status of aqueous zinc-ion batteries based on gas signals. The system uses sensors to detect hydrogen (H2), oxygen (O2), and carbon dioxide (CO2) inside the battery. It analyzes the concentrations of these gases to diagnose issues like hydrogen evolution, corrosion, and passivation. Normalization processes are used to reduce cross-interference between gases. Threshold levels are set for accurate diagnosis of battery health.

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Gas monitoring system for sealed battery compartments in electric buses to detect battery abnormalities and prevent fires. The system uses a separate control box outside the battery compartment with a communication unit and control unit. Inside the compartment, air ducts connect the battery packs to a monitoring unit with gas sensors. Fans circulate air through the ducts to rapidly detect and spread any battery gas leaks for faster response time. This prevents gas accumulation and hysteresis compared to sensors just inside the compartment. The external control box allows quicker response and isolation from any battery fires.

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A multi-modal based automatic gas monitoring and early warning system that uses a sensor array with temperature, humidity, and multiple gas sensors to accurately detect and predict gas hazards. The system corrects gas concentration readings using a neural network model that takes temperature, humidity, and multiple gas concentrations as input and outputs a risk probability value. This allows adaptive concentration adjustments based on environmental conditions to improve detection accuracy.

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Monitoring battery systems for electrolyte leakage using gas sensors and variable correlation. The method involves monitoring the gas analyte level using gas sensors while also monitoring variables of the battery system. Correlation between the gas level and variables indicates electrolyte leakage. By modulating sensor operating parameters and analyzing the resulting gas species, the method can also determine battery conditions.

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Thermal runaway monitoring device for lithium batteries that provides early warning of thermal runaway in the battery pack. The device uses a multi-gas sensor array to accurately monitor gases like CO2, H2, CO, and hydrocarbons that are released in the early stages of battery thermal runaway. This allows detecting thermal runaway earlier and more reliably compared to monitoring a single gas. The array helps overcome limitations of single-gas sensors that may miss early warnings or false alarm due to sensor malfunction or difficulty in monitoring one specific gas.

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Sensor-based battery thermal runaway early warning method to improve timeliness and accuracy of detecting battery faults like thermal runaway. The method involves monitoring battery parameters like temperature, gas type, concentration, sound, and smoke to detect different stages of thermal runaway. It provides early warning at the thermal runaway stage, followed by further warnings at the bulging, combustion, and explosion stages based on subsequent sensor data.

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Accurate battery fault detection using sensors to improve reliability and safety of batteries in applications like electric vehicles. The method involves collecting gas sensing data from sensors that are gas-sensitive to fault marker gases produced by batteries. It then extracts frequency domain features from the time domain gas sensing data using Fourier transform. These features are fed into a trained fault identification model to recognize fault marker gases. Battery faults are determined based on the recognized gases, improving fault detection accuracy compared to just monitoring battery parameters.

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