3578 results about "Battery voltage" patented technology

The invention relates to the technical field of battery management, in particular to a battery module early abnormality early warning method and system fused with a time sequence knowledge graph, and the method comprises the steps: obtaining static parameters and dynamic sensor data of each battery node in a battery module, and constructing the time sequence knowledge graph; performing node-relation importance evaluation of topology embedding, and screening out key nodes and relations; constructing heterogeneous knowledge unified mapping of multi-manifold representation, and projecting battery voltage, temperature and state manifolds to a unified representation space; performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space, and performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space; bidirectional coupling graph-sequence information flow cooperative processing is realized, graph structure information is converted into sequence representation, and a graph structure is fed back and updated; carrying out multi-scale feature hierarchy fusion and integration, and integrating hierarchy feature information of a battery monomer, a module and a system; and evaluating the health state of the battery module based on the multi-scale fusion feature information, and generating early warning information in an abnormal state or an abnormal state.

The invention discloses a battery thermal runaway detection system based on an early warning function, and relates to the technical field of battery thermal runaway, and the system comprises a supervision center which is used for carrying out the real-time supervision of the data information of a plurality of batteries, carrying out the early warning of the time data of the battery thermal runaway, and facilitating the timely issuing of warning information and notification announcements, and a monitoring device which is used for monitoring the data information of a plurality of batteries. The monitoring device is used for collecting thermal runaway parameters of each detection node of a plurality of batteries, the thermal runaway parameters comprise battery voltage, temperature, current and pressure, and the protection device is used for carrying out delayed protection operation on the monitoring device. The battery thermal runaway detection system based on the early warning function establishes a thermal runaway early warning model by fusing multi-parameter data of heat, force, electricity and the like and historical data of a thermal runaway phenomenon and by utilizing various learning algorithms, monitors battery parameters and sets simple judgment standards and threshold values such as a non-working state current threshold value and the like, so that the battery thermal runaway early warning model is established. Early detection of battery thermal runaway is realized, and monitoring and early warning effects are achieved.

An ID token wirelessly communicates a battery voltage to a vehicle gateway via short-range communication. A battery health may be determined based on the current battery voltage and one or more previous battery voltages associated with ID token. Replacement of the ID token may be initiated based on the battery health to avoid communication failures by the ID token due to insufficient battery charge.

A high energy density lithium water-activated battery having a compact, readily manufacturable, and scalable electrode stack structure has enhanced tolerance to stress conditions such as shock and vibration, which may be experienced during shipping, transport and / or deployment into a waterbody (e.g., an ocean). A Li seawater battery pack can provide power to a marine device deployed in or on the surface of a waterbody via one or more seawater battery modules and a power circuit module having one or more input terminals for receiving battery voltages from the one or more battery modules. The power module conditions each of the battery voltages and consolidates these into a single operating output voltage, and the cathode electrodes from each of the battery modules share a common electrolyte comprising water from the waterbody.

A battery management system, a battery pack, an electric vehicle and a battery charging time prediction method are provided. The battery management system includes a memory for storing a plurality of reference charging maps, a sensing unit for detecting voltage, current, and temperature of a battery, and a control unit for determining an estimated SOC value of the battery. Each reference charging map includes first to Mth reference current values and first to Mth reference power curves associated with first to Mth SOC sections. The control unit obtains a mth reference power curve and a mth reference current value associated with a mth SOC section to which the estimated SOC value belongs, from the reference charging map associated with the temperature section to which the temperature detection value of the battery belongs. The control unit determines the time required for the SOC of the battery to reach an end point of the mth SOC section, according to the result of comparing a maximum output power of a charger and the mth reference power curve, based on the difference between the end point of the mth SOC section and the estimated SOC value, and at least one of the mth reference current value, the maximum output power and the mth reference power curve.

An electrical power distribution system is disclosed. A line converter is connectable to a mains power source and an on-board battery. An electronic control system is configured to control the line converter. A first sensor arrangement is configured to measure electric current into or out of the battery. A second sensor arrangement is configured to determine a current battery voltage. The electronic control system is arranged to receive a charge / discharge power request and control the line converter to regulate the line converter output voltage using a voltage reference obtained from a setpoint electric current into or out of the battery and the measured electric current into or out of the battery, where the setpoint electric current into or out of the battery is derived as a quota of the charge / discharge power request and the determined current battery voltage.

The invention relates to a data-driven lithium ion battery voltage prediction modeling method fusing physical information, and belongs to the technical field of batteries. According to the method, a multi-working-condition battery operation database is constructed, a terminal voltage prediction model fusing a physical model and a neural network is designed, a mathematical constraint relation is established for equivalent circuit parameters and neural network intermediate variables, and data driving characteristics and physical parameters are jointly optimized by using a gradient descent method. The method specifically comprises the steps of collecting battery current, voltage and temperature data; constructing an input feature and an output tag; outputting an intermediate variable through a parameter layer and associating an equivalent circuit parameter; and introducing physical loss function constraint model training to ensure the physical rationality of parameters. The method has high precision of data driving and interpretability of a physical model, improves multi-working-condition adaptability through temperature compensation and parameter constraint, and can be widely applied to battery state estimation and health management of electric automobiles and energy storage systems.

Method for determining the internal resistance of a battery cell of an energy storage device for an electrically powered motor vehicle, wherein the method comprises: charging the battery cell with a charging current; repeatedly temporarily reducing the charging current to a measuring charging current; measuring a cell voltage dependent on the charging current; and determining the internal resistance based on the cell voltage and the charging current.

The invention discloses a control system for lithium battery power management, and relates to the field of lithium battery control, and the system comprises a voltage input module which is used for externally connecting voltage and supplying power to a battery power supply module and a time delay module; the battery power supply module is used for charging the battery; receiving the battery voltage information, and adjusting the voltage output to the battery according to the battery voltage; the battery module is used for a battery to store electric energy; compared with the prior art, the system has the beneficial effects that the output voltage of the battery power supply module is adjusted through the feedback module, the output voltage of the battery power supply module is relatively large at the beginning, and the charging voltage is reduced along with the increase of the battery voltage, so that a quick charging effect is constructed; whether battery charging is abnormal or not is detected through the time delay module and the charging fault detection module, the battery power supply module is adjusted to perform constant-voltage charging when charging is abnormal, and the battery charging effect is guaranteed.

The invention relates to a battery SOC intelligent monitoring and management method based on a precise electric quantity algorithm, and belongs to the technical field of battery power generation management, and the method comprises the steps: obtaining the voltage, current, temperature and historical data of a battery in real time, and calculating an initial SOC value through an ampere-hour integral method; when the SOC is greater than or equal to 90% in a charging state, generating a first correction factor based on a voltage hysteresis characteristic and a temperature coefficient, and dynamically reducing an ampere-hour integral weight; in the discharge state and when SOC is smaller than or equal to 20%, a second correction factor is generated in combination with the polarization voltage and the temperature attenuation coefficient, and the Kalman filtering weight is increased; and fusing the correction factors through a gradient descent optimization algorithm, performing closed-loop feedback, and outputting a calibrated SOC value. According to the method, through dynamic weight distribution, a nonlinear compensation model and a closed-loop self-optimization mechanism, the problems that an extreme SOC interval is large in estimation error and a traditional method is poor in adaptability are solved, and the precision and reliability of battery management are remarkably improved.

The invention relates to the technical field of energy storage lithium batteries, and discloses a remote monitoring operation and maintenance management device for energy storage lithium batteries, which is characterized in that a battery temperature acquisition module, a battery voltage acquisition module, a battery current acquisition module and an environment temperature acquisition module are respectively used for acquiring the surface temperature, voltage and current of a battery cell of an energy storage lithium battery group and the environment temperature of the battery; the state of charge calculation module calculates the state of charge (SOC) of the energy storage lithium battery group; the battery fault diagnosis module performs battery abnormity detection to identify a fault battery; and the early warning module generates alarm information when the SOC is not in a preset range and detects that a fault battery exists in the energy storage lithium battery group. According to the method, more data are collected through the intelligent sensor, multiple calculation modes are established to calculate the SOC of the battery, the calculation result can accurately reflect the actual working state of the energy storage power station, real-time fault detection is carried out on the energy storage lithium battery, and the use safety of the energy storage lithium battery is guaranteed.

An electric vehicle battery control system including a first electric motor configured to propel a vehicle in a drive mode in response to a drive current, a second electric motor operative to generate a back electromotive force voltage in a fault mode, a battery, having an open circuit battery discharge voltage, for generating the drive current for coupling to the first electric motor, and a vehicle controller for limiting the drive current in response to a difference between the back electromotive force voltage and the open circuit battery discharge voltage being less than a threshold voltage difference.

A method for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery may include measuring a battery voltage across terminals of the battery and a battery current drawn from the battery, modelling behavior of the battery with an impedance model having impedance model parameters, dynamically analyzing the battery current drawn from the battery by a load, dynamically determining safe operating limits for an augmented current for augmenting the current drawn by the load, based on analysis of the battery current and the safe operating limits, determining the augmented current, generating the augmented current to be drawn from the battery, and estimating the impedance model parameters when the augmented current is drawn from the battery.

The invention discloses a lithium battery life prediction method and device based on data analysis. The method comprises the following steps: acquiring charging current, discharging current, battery voltage and timestamps of recorded data of each complete charging and discharging cycle; performing data analysis according to the charging current, the discharging current and the timestamp, and constructing a charging and discharging current sequence; performing voltage fluctuation amplitude analysis according to the timestamp, the charging and discharging current sequence and the battery voltage to obtain a voltage jump characteristic; performing fluctuation characteristic analysis according to the voltage abrupt change characteristic to obtain a voltage fluctuation characteristic; performing correlation analysis according to the charging and discharging current sequence to obtain an optimal charging and discharging current sequence; calculating a voltage attenuation factor according to the voltage fluctuation characteristics and the optimal charging and discharging current sequence; and according to the optimal charging and discharging current sequence and the voltage attenuation factor, constructing a life prediction model, and according to the life prediction model, calculating the residual cycle index, the future battery voltage and the battery life. The method can accurately predict the service life of the lithium battery.

In one aspect, based on at least a received first state of health of a battery pack and an initial state of charge of the battery pack, a method may include determining, by a state of charge estimator of a digital twin battery model, states of charges for the battery pack. Based on the states of charges for the battery pack, the method may include determining, by a voltage predictor of the digital twin battery model, predicted battery voltages. Based on the predicted battery voltages, the method may include determining, by a state of charge corrector of the digital twin battery model, a voltage difference between the predicted battery voltages and measured voltages. Based on the voltage difference, the method may include correcting, by the state of charge corrector, the states of charges to generate corrected states of charges for the battery pack.

The invention discloses an energy storage equipment lithium ion battery connection fault diagnosis method, relates to the technical field of energy storage equipment lithium ion battery management systems, and is used for solving the problem of battery connection faults. According to the method, battery voltage and current data are obtained in real time. And a dynamic multi-modal filtering mechanism is designed, and different filtering methods are adopted for different environmental noise characteristics. And the noise is further accurately identified and filtered by calculating the ambient temperature and humidity and the spectrum characteristics in the battery signal. A data forgetting mechanism is introduced, a self-adaptive window optimization mechanism is adopted, the window length is automatically adjusted according to the voltage fluctuation real-time change rate, the battery state change is quickly responded, and the diagnosis sensitivity and adaptability are improved by dynamically adjusting the time window and the data weight through a weighted updating mechanism. And based on correlation coefficient analysis, the battery connection state is judged, and accurate fault diagnosis is realized. According to the method, the safety and the stability of the energy storage equipment are effectively improved, and the method has high practical application value.

The invention provides a method for realizing in-phase online equalization of a modular battery energy storage system based on an NLM, and the method comprises the steps: S1, regularly obtaining SOCj, x and (1-SOC) j, x, rechargeable energy SOEj, x and dischargeable energy SODEj, x of a module DC side battery through a battery management system BMS according to an equalization control period Tequ; s2, acquiring an equalization index of each module according to engineering requirements, determining an input priority sequence during charging and discharging, and generating a battery voltage queue of all modules; and S3, obtaining the size of a modulation wave in each power control period, and determining the first k modules which should be input currently and the total number k of the input modules. In-phase equalization is realized by fully utilizing an alternating-current reactive energy exchange process and a reactive state of the system, a blind area of a traditional method is eliminated, an application range of the in-phase equalization is expanded, in-phase module equalization is realized in each power frequency period, an operation boundary of the system is not influenced at all, charging of some modules in one power frequency period is realized, and charging efficiency is improved. Some modules discharge, and the equalization speed is higher.

The invention relates to the technical field of battery management, and discloses a high-voltage lithium battery cluster voltage balance control method and system for rail transit. The method comprises the following steps: acquiring battery voltage and tunnel environment parameters, and obtaining environment characteristic data through Kalman filtering; predicting a voltage deviation trend through LSTM in combination with a UPS load rule; calculating a dynamic equilibrium threshold according to the predicted value; when the voltage difference value exceeds a threshold value, the bidirectional energy transmission equalization circuit is started; and monitoring the battery state change in the equalization process, and outputting a maintenance instruction through a fault recognition algorithm. The problem that battery balance control in a rail transit UPS system cannot adapt to complex environments and dynamic load changes is solved, and the precision and the intelligent level of battery cluster voltage balance control are improved.

The invention discloses a lithium battery thermal runaway monitoring method and a related device, and relates to the field of lithium battery thermal runaway monitoring, and the method comprises the steps: obtaining a multi-sensing parameter time sequence of a target lithium battery; the multi-sensing parameter time sequence comprises a battery temperature time sequence, a battery voltage time sequence, an in-battery gas concentration time sequence and a battery deformation time sequence; inputting the multi-sensing parameter time sequence into a monitoring model to obtain the spatial-temporal characteristics of each sensing parameter; the monitoring model comprises a convolutional neural network, an LSTM network and an attention mechanism module which are connected in series; and monitoring the thermal runaway state of the lithium battery according to the spatial-temporal characteristics of the sensing parameters and the corresponding dynamic thresholds. According to the invention, the thermal runaway state of the lithium battery can be accurately monitored in real time, the occurrence of thermal runaway can be predicted in advance, and an early warning signal can be sent out in time.

The invention relates to a motor power self-adaptive control method and system based on multi-dimensional sensor data, and the method comprises the steps: collecting multi-dimensional signals, such as ground resistance, motor temperature and battery voltage, in real time, carrying out the self-adaptive filtering and noise reduction, analyzing the correlation of all parameters through a multivariable decoupling algorithm, and determining a power demand weight; when it is detected that the ground resistance is a dominant factor, the system generates a load feature vector through weighted fusion, a database is matched to determine an optimal power target value, and PWM output is dynamically adjusted through a fuzzy control algorithm; and a closed-loop feedback mechanism continuously optimizes control parameters to ensure that the system quickly responds to sudden change of ground resistance. According to the method, intelligent adaptation of the motor power is achieved, the problems that adjustment lags behind and multi-parameter coupling processing is insufficient in the traditional technology are effectively solved, the stability of the sweeping robot under the complex ground working condition is remarkably improved, and the service life of the motor and the service life of a battery are remarkably prolonged.

The invention belongs to the technical field of energy storage batteries, and discloses a deep learning prediction method and device for the voltage range of a battery system. The method comprises the following steps: collecting battery operation data of a battery cluster; the battery operation data comprises single battery range data, battery cluster charging and discharging power and battery cluster total voltage data; inputting the battery operation data into a pre-trained deep learning prediction model for prediction, and obtaining a voltage range prediction result of the battery cluster in future preset time; wherein the deep learning prediction model which is trained in advance is a BiGRU (BiGRU)-Transform-LSTM (Long Short Term Memory) model. According to the method, a mixed model of three different architectures of BiGRU-Transform-LSTM is adopted, the voltage range change of the battery can be effectively predicted, the voltage range change of a period of time in the future can be predicted according to current voltage and power information, and the method has a voltage consistency evaluation function; the method facilitates reasonable planning of a charging and discharging strategy of the battery pack, prolongs the service life of the battery, and improves the safety and reliability of the battery.

This invention relates to the field of battery testing technology and discloses an ice-water impact testing system, comprising: a temperature control unit for regulating the temperature of the testing environment, the temperature control unit including a liquid nitrogen cooling device and an electric heating device, and using a PID algorithm to regulate temperature fluctuations within ±0.5℃; an impact unit for simulating water flow impact and mechanical vibration of battery samples during testing, the impact unit including a high-pressure water pump and a mechanical vibration device, capable of adjusting water flow pressure and vibration acceleration; and a data acquisition and analysis module for real-time acquisition of temperature, humidity, pressure, and battery voltage data during the testing process, and evaluating battery performance through intelligent analysis algorithms. By combining the liquid nitrogen cooling device and the electric heating device, and employing a PID control algorithm, precise adjustment of the temperature control unit is achieved, enabling rapid temperature switching within the range of -40℃ to 80℃, while maintaining temperature fluctuations within ±0.5℃.

A system for estimating current-dependent non-linear equivalent circuit model (ECM) parameters of a battery may include measurement circuitry for measuring a battery voltage across terminals of the battery and a battery current drawn from the battery. The system may also include an impedance model having impedance model parameters, the impedance model for modelling behavior of the battery. The system may further include an augmented stimulus signal generator configured to dynamically analyze the battery current drawn from the battery by a load, based on analysis of the battery current, determine an augmented current for augmenting the current drawn by the load; and generate the augmented current to be drawn from the battery. The system may also include a battery model estimator for estimating the impedance model parameters when the augmented current is drawn from the battery.

The invention relates to the technical field of lithium ion battery fault diagnosis, and discloses a new energy automobile power battery multi-fault diagnosis method. According to the method, a double-layer diagnosis strategy is adopted, in the first layer, multi-layer feature extraction is conducted on battery voltage data, multiple abnormal conditions of abnormal single batteries in a battery pack are accurately recognized through an ALOF algorithm, and in the second layer, inner short circuit fault diagnosis is further conducted on the abnormal single batteries through the Mahalanobis distance. In order to extract fault features with finer granularity, kurtosis and change rates are extracted from different time windows of voltage data after the influence of environment and sensor errors is eliminated as indexes, and then preliminary anomaly screening is carried out on voltage changes of different time scales of each single battery by using an ALOF algorithm. Normal battery average voltage data which can reflect the current battery working condition and aging condition and is screened out after first-layer diagnosis is adopted as a comparison object, and the mahalanobis distance is used for further diagnosing the internal short circuit fault of the single battery which is diagnosed to be abnormal in the first layer.

A battery pack includes an attaching portion, a battery, a voltage detection circuit, a current acquisition circuit, a next current calculation circuit, a first charging stop circuit, and a second charging stop circuit. The attaching portion is electrically and mechanically connected to the battery charger. The first charging stop circuit stops charging of the battery in response to a first condition being satisfied. The second charging stop circuit stops the charging of the battery in response to a second condition different from the first condition being satisfied.

The utility model relates to the technical field of energy storage power supplies, in particular to an auxiliary source activation circuit which comprises an auxiliary source activation module, a battery judgment module, an auxiliary source module, a battery charging module, a battery and a photovoltaic power supply. The auxiliary source activation module is used for outputting a first auxiliary source activation signal when the voltage of the photovoltaic power supply is greater than or equal to a photovoltaic voltage threshold; the auxiliary source module is used for outputting an auxiliary power supply when receiving the first auxiliary source activation signal; the battery charging module is used for charging the battery through the photovoltaic power supply when receiving the auxiliary power supply; and the battery judgment module is used for outputting a first battery signal when the voltage of the battery is greater than or equal to a battery voltage threshold value, so that the auxiliary source activation module outputs a second auxiliary source activation signal, and the auxiliary source module outputs an auxiliary power supply when receiving the second auxiliary source activation signal. On one hand, due to the arrangement of the battery judgment module, the battery can be prevented from being discharged during power shortage. And on the other hand, the auxiliary power supply can be activated or maintained to supply power when the battery voltage is normal.

A battery management apparatus according to an embodiment disclosed herein includes a detecting unit configured to detect a voltage of a battery and a controller configured to determine a state of the battery based on cycle information related to a use cycle of the battery and an abnormal count value related to whether the voltage of the battery is abnormal.

The invention relates to the technical field of vehicles, in particular to a battery charging method and device of a vehicle and the vehicle, and the method adopts a battery charging system which comprises first to eighth switches, and comprises the following steps: judging whether the vehicle is connected with a charging pile or not; if the vehicle is connected with the charging pile, acquiring a current charging mode of the battery charging system, and determining target states of first to eighth switches according to the current charging mode; and the first to eighth switches are controlled according to the target states of the first to eighth switches, and / or the multiple bridge arms are controlled based on a preset control strategy, so that the vehicle is subjected to direct fast charging or boost charging or current-rising charging. Therefore, the problem that the vehicle power battery cannot be charged or the charging efficiency is low when the charging pile is not matched with the battery voltage in the prior art is solved, and the charging experience of the vehicle power battery is improved.

The invention belongs to the technical field of tugs, and provides an intelligent energy management system of a hybrid power tug, comprising: a sensor for collecting energy consumption and generation conditions of the tug, including measuring battery voltage, current and temperature, and measuring ship speed, acceleration and direction; the data acquisition module is used for transmitting the data collected by the sensor to the processing unit in a digital form and has data synchronization and timestamp functions; the processing unit is used for automatically selecting an optimal operation mode according to the collected data and a preset algorithm; by integrating a plurality of sensors, the energy consumption and generation conditions of the tug can be monitored in real time, including key parameters such as battery voltage, current and temperature and speed and direction of a ship, the data are analyzed through a data processing algorithm, the algorithm adopts an advanced mathematical model and a machine learning technology, and the energy demand can be accurately calculated. And an energy distribution strategy is dynamically adjusted according to actual operation requirements.

The invention discloses a method for detecting and displaying the electric quantity of an earphone, which relates to the technical field of electronic equipment and comprises the following steps of: acquiring an analog signal of battery voltage through an ADC (analog-to-digital converter) channel, converting the analog signal into a digital signal, performing digital filtering processing on the acquired signal, matching a preset voltage-electric quantity mapping table according to an updated battery voltage value, and displaying the electric quantity of the earphone according to the preset voltage-electric quantity mapping table. According to the method for detecting and displaying the electric quantity of the earphone, the voltage of a lithium battery of 0-4.2 V is detected through an ADC differential input mode, the sampling frequency is 100-500 Hz, the precision is 10-12 bits, and signal stability is ensured through low-pass filtering. A sliding window mean filtering algorithm is adopted to process signals, fluctuation and noise are effectively eliminated, and voltage changes are dynamically tracked. A deviation comparison mechanism avoids misjudgment and ensures accurate electric quantity detection. The electric quantity mapping table is dynamically adjusted according to the voltage, the battery use duration and the environment temperature, the earphone electric quantity state is accurately reflected, the user experience is improved, and the electric quantity detection stability and accuracy are improved.