2612 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.

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.

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.

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.

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 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.

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.

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 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.

The invention relates to the technical field of batteries, and discloses a battery, a battery pack and electric equipment, and the battery comprises a pole assembly arranged on a shell; the battery cell comprises a battery cell body and a tab part extending from the battery cell body, and the tab part is welded with the pole assembly to form a first welding printing area; wherein the tab part comprises an aluminum element, a manganese element and / or a silicon element, and the mass ratio of the manganese element and / or the silicon element is a%; a resistance test point is formed on the non-welding area of the tab part, the first welding printing area is located between the pole assembly and the resistance test point, and the resistance between the pole assembly and the resistance test point is b; the tab part comprises a plurality of tab layers which are stacked, and the thickness of each tab layer is c; a, b, and c satisfy 0.004 < = b * a / c < = 15. The lithium separation risk of the battery is reduced while the over-current capability between the pole component and the tab part is ensured and the voltage jump of the battery is avoided, so that the reliability of the battery is ensured.

The invention relates to the technical field of OTA upgrading, in particular to an OTA upgrading result prediction method and system and a vehicle, and the method comprises the steps: periodically obtaining the multi-dimensional data of the vehicle, the multi-dimensional data including the current vehicle electric quantity, the OTA upgrading package size, the storage battery voltage, the environment signal strength, the network condition, the vehicle state and the current time; inputting the obtained multi-dimensional data into a trained random forest regression prediction model, and predicting the OTA upgrading success rate; and outputting a corresponding OTA upgrading suggestion according to the predicted OTA upgrading success rate. According to the method, whether upgrading can be smoothly carried out or not can be accurately predicted.

To generate a charging path for a battery, a method includes generating simulation data for charging currents based on a battery model indicating an internal state of a battery, generating an initial look-up table (LUT) for the charging currents and preset battery voltage limits based on the simulation data, the initial LUT representing initial charging limit conditions of the battery for stages corresponding to the charging currents, generating a modified LUT by adjusting at least one of the initial charging limit conditions of the initial LUT, in response to the initial LUT failing to satisfy a threshold, determining a final LUT based on the modified LUT, in response to the modified LUT satisfying the threshold, and generating a charging path for the battery based on the final LUT.

The invention relates to the technical field of safety control, and particularly discloses a safety control method, device and system for a combine harvester, and the method comprises the steps: judging whether the combine harvester meets a starting condition or not based on the position of a handle of the combine harvester, and the rotating speed value, the battery voltage value and the excitation voltage value of an engine of the combine harvester; and after the combine harvester meets the starting condition, if a target control signal of a main clutch of the combine harvester is received, and the combine harvester meets a preset condition corresponding to the target control signal, the main clutch is controlled. The method can effectively solve the potential safety hazard caused by the fact that the vehicle starting condition is judged only by depending on the position of a handle in the prior art, makes up for the defect that multi-dimensional verification is not carried out in combination with the engine state, and can prevent illegal vehicle starting caused by sensor faults or instantaneous misoperation. And meanwhile, the problem that wrong actions are executed when a combination signal is triggered by mistake before the combine harvester is started is avoided, and the operation safety and the equipment reliability of the combine harvester are improved.

The invention relates to the technical field of vehicle-mounted starting batteries, in particular to a vehicle-mounted starting battery capable of keeping emergency starting of a vehicle in a wireless mode. The battery realizes monitoring and protection of information such as voltage, current and battery cell temperature through a battery management system (BMS), and receives an instruction from a user mobile phone through a wireless Bluetooth communication module so as to control the discharge state of the battery. The battery management system (BMS) comprises a cell acquisition port, a battery equalization circuit, an MOS drive circuit, an acquisition chip (AFE), a temperature sensor (NTC) acquisition port, a microprocessor unit (MCU), a communication interface (UART / 485 / CAN), a parameter / protection information storage unit (ROM), a function key reserved port, a power conversion circuit, a power device (MOSFET) and a precision high-power resistor (shunt). According to the invention, real-time monitoring and protection of key parameters such as battery voltage, current and battery cell temperature are realized through a built-in battery management system (BMS).

The present disclosure relates to a battery arrangement with cell voltage verification. The battery arrangement includes a plurality of battery cells configured as one or more battery strings and a plurality of battery cell controllers connected to at least one terminal of the plurality of battery cells. Each battery cell controller includes a power electronics arrangement and a sensor for obtaining at least one battery cell parameter associated with the plurality of battery cell, The battery arrangement further includes a master node connected to each of the one or more battery strings for obtaining a total parameter associated with the battery strings. The cell parameter obtained by the plurality of battery cell controllers is compared with the total parameter obtained by the master node to verify the voltage associated with the battery arrangement.

The invention discloses a new energy automobile charging management system, and aims to improve the charging efficiency, prolong the service life of a battery and improve the user experience. The system comprises a battery monitoring module, a charging mode management module, an intelligent scheduling module, a remote control module, a safety protection module, a big data analysis module and a billing management module. The battery monitoring module collects data such as battery voltage, current and temperature in real time; the charging mode management module dynamically selects a fast, standard or slow charging mode according to the battery state and the user demand; the intelligent scheduling module optimizes a multi-vehicle charging sequence to realize reasonable resource allocation; the safety protection module provides overvoltage, overcurrent and temperature protection to ensure charging safety; the remote control module enables a user to monitor and adjust the charging state in real time. According to the invention, through intelligent charging management, resource configuration optimization, charging efficiency improvement, safety risk reduction in the charging process and battery life prolonging, convenient charging experience is provided for users.

The invention discloses a battery state-of-charge detection method based on deep learning and ultrasonic feature fusion. The method comprises the following steps: collecting and preprocessing battery ultrasonic signals and battery electrical parameters; extracting an ultrasonic amplitude; extracting and fusing the characteristics of the battery voltage, the surface temperature, the ultrasonic amplitude and the phase difference; the method comprises the following steps of: optimizing parameter values of a BiTCN-BiLSTM-Multi-Head Attention network on the basis of a WAA weighted average optimization algorithm, extracting basic characteristics related to the SOC of the battery in a characteristic matrix on the basis of a bidirectional time convolution network BiTCN, extracting long-term information related to the SOC of the battery in the basic characteristics on the basis of a BiLSTM network, extracting important characteristics related to the SOC of the battery in the long-term information on the basis of a Multi-Head Attention network, and calculating the SOC of the battery according to the extracted important characteristics. And outputting a predicted value of the SOC of the battery through the full connection layer. According to the invention, the estimation precision of the battery SOC can be improved.

The present application relates to a warming-up control method for a fuel cell system in a starting process and a fuel cell system, applied to the technical field of fuel cells. The method includes: starting the fuel cell system, turning on a small-cycle cooling circuit, and pull-loading an output power to a first power; in response to an inlet temperature of a stack reaching a first temperature, rotating a three-way valve at a first rotating rate; calculating a variance of a cell voltage value; in response to the variance being smaller than a third threshold and reducing the variance to be within the first threshold, returning to the rotating the three-way valve at the first rotating rate until all turn-on of a large-cycle cooling circuit and all turn-off of the small-cycle cooling circuit; pull-loading the output power to a rated power.

The invention discloses a thermal runaway detection method and system for a sodium ion battery. The detection method comprises the following steps: acquiring battery voltage and surface pressure of a battery shell; a voltage change curve and a pressure change curve are obtained, the voltage change curve is a curve that the battery voltage changes along with time, and the pressure change curve is a curve that the surface pressure changes along with time; and in combination with the battery voltage, the pressure change curve, the surface pressure and the change of the pressure change curve, multi-stage early warning signals are sent out. According to the method, multi-stage early warning detection with pressure as a core parameter is adopted, the severity of thermal runaway can be effectively identified, corresponding measures are taken according to different stage characteristics, the detection accuracy is improved, the thermal runaway detection reliability of the sodium ion battery is improved, and the use safety of the sodium ion battery is improved.

The invention discloses a three-phase vehicle-mounted charger hierarchical control method and system based on a battery state, and the method comprises the steps: obtaining the battery state data of a vehicle-mounted power battery, the battery state data at least comprising a battery voltage and a battery charge state; determining a charging mode according to the battery voltage and / or the battery charge state; when the charging mode is a first charging mode, controlling the PFC converter to operate in a constant voltage output mode, and executing voltage following control on the bidirectional DC-DC converter based on the battery voltage; and when the charging mode is a second charging mode, executing voltage following control on the PFC converter based on the battery voltage, and controlling the bidirectional DC-DC converter to operate in a constant-frequency or constant-phase output mode at the same time. By setting hierarchical control strategies under different working conditions, the output voltage of the PFC converter is adaptively adjusted, the voltage regulation transformation ratio under different working conditions is optimized, the overall circuit structure is improved, and the charging efficiency of the vehicle-mounted charger within the full working condition range is improved.

A battery diagnosis apparatus includes a measuring unit to measure a battery voltage, which is a voltage between both terminals of a battery and a battery current which is a current flowing through the battery, a storage unit to store a plurality of actual-measurement internal resistance values, each of which has been calculated based on at least one of the battery voltage and the battery current at each diagnosis time when diagnosing defects of the battery, and at least one test internal resistance value determined according to a predetermined criterion, and a control unit to extract a plurality of previous diagnosis times, corresponding to a predetermined number of samples, from each diagnosis time, at the diagnosis time, calculate a moving average, and diagnose defects of the battery by comparing an actual-measurement internal resistance value calculated at each diagnosis time with reference values calculated based on the moving average.