436 results about "Charge cycle" patented technology

Embodiments described herein relate generally to electrochemical cells having pre-lithiated semi-solid electrodes, and particularly to semi-solid electrodes that are pre-lithiated during the mixing of the semi-solid electrode slurry such that a solid-electrolyte interface (SEI) layer is formed in the semi-solid electrode before the electrochemical cell formation. In some embodiments, a semi-solid electrode includes about 20% to about 90% by volume of an active material, about 0% to about 25% by volume of a conductive material, about 10% to about 70% by volume of a liquid electrolyte, and lithium (as lithium metal, a lithium-containing material, and / or a lithium metal equivalent) in an amount sufficient to substantially pre-lithiate the active material. The lithium metal is configured to form a solid-electrolyte interface (SEI) layer on a surface of the active material before an initial charging cycle of an electrochemical cell that includes the semi-solid electrode.

The invention discloses a battery health state prediction method based on multi-modal feature fusion and deep learning, and the method comprises the steps: obtaining the charging data, including current, voltage and temperature data, of each charging period during the multi-stage constant current charging of a battery; determining SOH (state of health) data of the battery; performing smooth filtering on the current and voltage data through a Savitzky-Golay filter, reconstructing a current curve, and reconstructing a voltage curve by adopting a linear interpolation method and a KF filter; constructing a comprehensive characteristic pool according to the current curve, the voltage curve and the temperature data, and directly adding the constant current value at the maximum charging amount into the comprehensive characteristic pool; screening features from the comprehensive feature pool and putting the features into a prediction feature pool; completely integrated empirical mode decomposition is carried out on the SOH data, and IMF components obtained through decomposition are put into a prediction feature pool; the method comprises the following steps: establishing a CNN (Content Number)-BILSTM-ATTENTION And performing single-step prediction and multi-step prediction on the SOH of the battery by using the trained model. According to the invention, high-precision prediction of the SOH of the electric vehicle battery can be realized.

The invention discloses a battery SOH evaluation method combining an electrochemical model and machine learning, and the method comprises the steps: constructing a target battery electrochemical simulation model, generating voltage curves and SOH data under the conditions of different temperatures, charging rates and different battery aging states, and constructing an SOH evaluation model and a voltage curve prediction model according to the voltage curves and the SOH data; a constant-current charging segment of the target battery in the current charging cycle is obtained in real time, the actually measured temperature and charging rate of the target battery corresponding to the constant-current charging segment are obtained, multiple SEI thicknesses and lithium precipitation thicknesses are preset, and multiple voltage curves are obtained through prediction of a voltage curve prediction model; acquiring a voltage curve segment closest to the initial SOC of the constant-current charging segment in the plurality of voltage curves, and calculating corresponding SEI thickness and lithium precipitation thickness; and obtaining an SOH estimated value according to the SOH evaluation model. According to the invention, accurate evaluation can be realized for a small data set and a novel battery.

The invention discloses a new energy automobile secondary battery health state intelligent estimation method based on small sample transfer learning, and the method comprises the steps: firstly obtaining the voltage, current and time sequence data of a new energy automobile secondary battery through a BMS, carrying out the linear interpolation downsampling operation of the data of each charging cycle, and constructing a source domain data set and a target domain data set according to different degradation levels; carrying out normalization processing on all the input features and the SOH labels; then constructing an SOH evaluation model based on a ResNet-Transform-KAN network architecture, inputting a source domain sample and a target domain sample into the model at the same time, taking regression loss of the source domain and a tagged target domain and distribution alignment loss based on MMD as a joint objective function, and performing iterative training through an Adam optimizer; and finally, inputting target domain label-free data into the trained model to realize state estimation of the secondary new energy automobile battery SOH at different degradation levels. According to the method, the accuracy of estimating the SOH of the secondary battery of the new energy automobile at different degradation levels by the model is effectively improved.

The invention discloses a charging data auditing method based on the Internet of Things. According to the invention, a multi-dimensional tamper-proof system is constructed through deep fusion of physical signal features and a data security technology. According to the dynamic data fingerprint technology, charging waveform microscopic characteristics and equipment space-time attributes are subjected to nonlinear coupling, so that data generated in each charging period has a unique and verifiable biological-grade identifier. The introduction of a chaotic mapping mechanism endows fingerprints with sensitivity to data subtle differences, even if an attacker tampers current and voltage values with the last three decimal points, an avalanche effect of fingerprint hash values can be caused, it is ensured that tampering behaviors are inevitably exposed in an auditing link, a full-link credible closed loop from data generation to auditing conclusions is achieved, and the auditing efficiency is improved. The reliability of data sources in auditing is improved, and the overall auditing accuracy is ensured. And through an innovative architecture of edge end lightweight calculation and cloud collaborative verification, the feasibility and economy of large-scale charging facility auditing are remarkably improved.

A computer-implemented method comprises obtaining (202) a plurality of open circuit voltage curves of a battery (150) at a plurality of predefined charge cycles of the battery. Each of the open circuit voltage curves is indicative of an open circuit voltage over a predetermined charge range of the corresponding charge cycle. The method further comprises fitting (204) parameters of a model of the battery based on the plurality of open circuit voltage curves. The parameters include electrode parameters that model a corresponding open circuit electrode potential for each electrode of the battery.

The application discloses a kind of fast charging method and device of lithium ion battery, it is related to power battery charge-discharge technical field.The application aims at the problem that the fast charging method of existing lithium battery cannot better adapt to the state of lithium battery in different charging stages.The SOC interval is selected in the application, and the SOC interval is divided into several charging stages with the defined ΔSOC as interval;The residual life prediction value of lithium ion battery is obtained according to the temperature, state of charge, charge rate and charge cycle number of lithium ion battery;The characteristic curve of lithium ion battery is obtained, and the charge time weight α of each charging stage is obtained according to the charge characteristic curve;The charging current of each charging stage is obtained by inputting the charging time, charge time weight and residual life prediction value into target equation.The application effectively prolongs the service life of battery while ensuring safe and fast charging.

The invention provides a method, a system and a terminal for judging charging electric quantity abnormity in a lithium battery charging process, and the method comprises the steps: firstly collecting charging data of a plurality of lithium batteries in a specific time period, then cleaning the data to remove abnormal values, then extracting effective charging cycle data from the cleaned data, and carrying out the battery feature analysis; then grouping the characteristic data according to battery types, constructing an exclusive training data set for each type of battery, and then training a regression model network by using the data sets to form an anomaly judgment model for different battery types. And analyzing the charging data of the to-be-detected battery through the constructed model, and automatically predicting whether the electric quantity is abnormal or not. According to the method, the big data and machine learning technologies are utilized, historical data are combined, and through data cleaning, feature extraction and model training, high-accuracy anomaly prediction is achieved, manual participation is reduced, economical efficiency and efficiency are improved, electric quantity anomalies in the charging process are effectively prevented, and the situation that the health of the battery is affected due to overcharge of the battery is avoided.

A foamed liquid dispensing system includes a container for receiving a liquid to be dispensed connectable to, and removable from, a gas charging unit. The gas charging unit includes a gas compressor, gas pressure sensing means, a control unit and a user interface. The user interface permits a user to input a desired final gas pressure from a minimum value to a maximum value in discrete steps. The control unit instructs the gas compressor to charge the container with gas to the desired final pressure. The gas pressure is continuously monitored and the flow of gas into the container is stopped once the desired final gas pressure has been reached. The user interface displays the gas pressure in the container as the charging cycle proceeds. Once charged with gas and removed from the gas charging unit, the container substantially retains the final desired gas pressure until the liquid is dispensed.

The invention discloses a lithium battery state data generation method based on a fusion time sequence generative adversarial network. According to the method, a charging period is extracted from lithium battery state data to serve as real data for training the generative adversarial network. An environment temperature label and random noise corresponding to real data are used as condition information to be input into a generator, modeling is conducted on the condition information through a gating circulation unit, the spatial resolution of the hidden state output by a GRU module is gradually recovered through a convolution layer by means of a transposition convolution module, and lithium battery state data are generated. In the training process, a weight constraint strategy is introduced to control parameter distribution of a discriminator, a dynamic gradient penalty mechanism is introduced into a loss function of the discriminator, and the gradient is adaptively adjusted to improve the training stability. A voltage differential constraint is added in a loss function of the generator, and it is ensured that generated data meet a specific physical constraint condition. The confrontation process is optimized through an asymmetric gradient penalty mechanism, the model convergence time is shortened, and the authenticity of generated data is improved.

The invention discloses a management method of a battery pack and electronic equipment, the management method of the battery pack is applied to a controller, the controller is connected with the battery pack through a detection circuit, and the battery pack comprises at least one battery cell; the management method comprises the following steps: acquiring the charging cycle index and the battery capacity of the battery cell through the detection circuit; and when it is determined that the number of charging cycles is greater than a preset number of times, corresponding charging and discharging power is matched according to the battery capacity so as to control the battery pack to charge and discharge. Through the controller outside the battery pack, after it is determined that the number of charging cycles is larger than a certain number of times, the corresponding charging and discharging power is matched according to the battery capacity of the battery cell, and the charging and discharging power at the moment is smaller than the charging and discharging power of a new battery cell, so that the probability of occurrence of safety problems is reduced, and the safety of electronic products is improved.

The invention relates to a detection device (1) for a steering wheel (2) of a motor vehicle and to a method for detecting the presence of a hand (3) in a gripping area of ​​the steering wheel (2), wherein the detection device (1) has at least one heating device (4) which is designed to heat the steering wheel (2) in a heating mode and to detect the presence of the hand (3) in a detection mode.The detection device (1) has two switching elements (5, 6) and is designed to carry out at least one charging cycle (21) and discharging cycle (22) of the heating device (4) in detection mode by alternately opening one of the switching elements (5, 6) while the other switching element (5, 6) is closed and to determine an impedance of the heating device (4) during this time, wherein the determined impedance is influenced by the presence of the hand (3) so that the presence of the hand (3) can be detected by evaluating the determined impedance during the charging cycle (21) and discharging cycle (22).

The present application relates to a method for equalization control of a battery pack, comprising: detecting an open-circuit voltage of each battery cell of the battery pack after a charging cycle of the battery pack is ended (S1); when the first open-circuit voltage of the first battery cell is lower than a predetermined voltage threshold value, a second battery cell with the highest open-circuit voltage is discharged, and the discharge amount W of the second battery cell is determined as the difference between the first deviation X and the discharge adjustment amount M. A first deviation X between a first charge state of charge of the first battery cell and a second charge state of charge of the second battery cell is determined on the basis of the charge SOC-OCV curve and the detected open circuit voltage of the battery cells, and a discharge adjustment amount M is determined on the basis of at least the charge and discharge SOC-OCV curve of the battery cells (S2). According to the invention, the charging and discharging cycle period number of the battery can be greatly reduced in the equalization process of the battery pack, and the efficiency of the equalization process of the battery pack is effectively improved.

Methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. The reinforcement learning model can determine state-of-health characteristics of the battery over the charge cycles, and in a second phase, the reinforcement learning model is augmented accordingly. During this augmentation, the reinforcement learning model is trained with rewards given on a charge cycle-by-cycle basis, wherein rewards are given after looking at the charging optimization after the conclusion of each charge cycle. Commands are given to charge an on-field battery based on the augmented reinforcement learning model, associated state-of-health characteristics of the on-field battery are determined, and the reinforcement learning model is further augmented accordingly.

A battery management system is disclosed herein. The battery management system may monitor a charge cycle of a battery. The battery management system may determine a degradation rate of the battery. The battery management system may determine a usage pattern of the battery based on usage of the battery during a time period. The battery management system may determine, using a battery analysis model, a quantity of remaining charge cycles of the battery based on the degradation rate and the usage pattern. The battery analysis model may be trained based on historical data associated with historical usage of one or more other batteries. The battery management system may determine a status of the battery based on the quantity of remaining charge cycles and the degradation rate. The battery management system may perform, based on a time period associated with a remaining useful life, an action associated with the battery.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention discloses a high-cycle-performance aqueous battery, which comprises an aqueous electrolyte, a negative electrode, a first positive electrode and a second positive electrode, wherein the first positive electrode and the second positive electrode are positioned on two sides of the negative electrode, and a first diaphragm and a second diaphragm are respectively arranged between the first positive electrode and the negative electrode and between the second positive electrode and the negative electrode; the battery is provided with an electromagnetic valve, the electromagnetic valve is electrically connected with the first positive electrode and the second positive electrode to control switching of the positive electrodes in the charging and discharging loop, and every time discharging and charging circulation are completed, the electromagnetic valve switches the electrode connection relation, and the first positive electrode and the second positive electrode are alternately connected into the charging and discharging loop. Switching of the two positive electrodes in the aqueous battery is accurately controlled through the electromagnetic valve, a positive-negative battery structure is formed in real time, the double positive electrodes alternately perform lithium removal-lithium intercalation reaction, excessive Li intercalation or insufficient positive electrode reduction caused by long-term charging and discharging of a single positive electrode is avoided, the structural stability of the positive electrode material is improved, and the service life of the battery is prolonged. The cycle life and the electrochemical performance stability of the aqueous battery are obviously improved.

A method for scheduled charging for diagnosing a battery cell for an energy storage device for a motor vehicle, comprising: acquiring a charge request for the scheduled charging of the battery cell from an initial state of charge to a final state of charge within a charging period; determining a plurality of charge strokes corresponding to a difference between the final state of charge and the initial state of charge, and an associated plurality of partial charge periods corresponding to the charging period, wherein each of the partial charge periods comprises a partial charge time and a relaxation time; and outputting a control signal for the scheduled charging of the battery cell based on the plurality of charge strokes and the plurality of partial charge periods.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes power packs and a photovoltaic array with interconnected photovoltaic cells that supplies electric charge to the power packs for charging the power packs. A charge management database stores data tracking respective charge cycles of the power packs based on the charging and discharging of the power packs. An energy control system controls flow of power in the system to control the charging and the discharging of the power packs, optimizes the flow of power in the system based on the respective charge cycles as tracked in the data stored in the charge management database, and updates the charge management database based on the optimization(s). An output interface outputs a status of the power packs based on the flow of power, for instance to indicate effect(s) of the optimization(s) on the power packs.

The invention provides a working condition lithium battery SOH estimation method based on improved cordyceps sinensis correction-visual Transform, belongs to the technical field of battery health state estimation, and aims to solve the problems that the existing lithium battery capacity estimation depends on experimental data, the experimental data and working condition data have huge difference, and a real SOH label is lacked. The method comprises the following steps: acquiring lithium battery charging cycle and SOC change data, and selecting data sampling points; calculating a battery capacity estimation result, and carrying out statistical analysis on the battery capacity estimation result to obtain a distribution condition and statistical characteristics of a capacity estimation value; correcting the battery capacity estimation result after statistical analysis; and establishing an SOH estimation model, taking the corrected lithium battery capacity estimation result and the corresponding voltage, current and temperature data as input, and outputting a final lithium battery SOH estimation result.

A method for determining a condition indicating imminent thermal runaway in a power source includes changing a state of charge of the power source during one of a plurality of charge cycles and a plurality of discharge cycle. In response to the state of charge in each of the one of the plurality of charge cycles and the plurality of discharge cycles, the method includes matching a first condition measuring the state of charge of the power source and determining an overpotential value based on the measured state of charge. The method also includes determining a trend based on a plurality of the overpotential values and identifying an indication of possible thermal runaway in the power source based on the trend indicating a downward trend of the plurality of overpotential values.

A lead acid battery separator includes a porous membrane wherein within the porous membrane, a conductive element and / or a conductive or semiconductive nucleation additive is contained. According to the present invention, inside the battery separator, numerous nucleation sites are provided to provide starting points for crystal formation at numerous locations, thereby dispersing the total amount of lead sulfate into a large number of smaller crystals, rather than a small number of larger crystals; thus, these smaller crystals are more readily returned to the electrolyte during the charge cycle of the lead acid battery, thereby hindering the slowed growth of dendrites within the separator of the lead acid battery, increasing the charge acceptance of the battery, and / or improving the cycle performance of the battery.

The invention relates to the field of vehicle batteries, and discloses an early warning method, device and equipment for a composite adhesive layer battery and a storage medium, a composite adhesive layer of the composite adhesive layer battery comprises a sensing network, the sensing network comprises a conductive medium, and the sensing network is used for collecting the resistance of the conductive medium; the early warning method comprises the following steps: determining the degumming area of the composite adhesive layer according to the current resistance and the initial resistance of the conducting medium; determining an early warning threshold value according to the charging cycle index and the degumming area of the composite adhesive layer battery and the insulation resistance and the heat conductivity coefficient of the composite adhesive layer; and determining a target early warning strategy according to the degumming area and an early warning threshold so as to carry out early warning. According to the application, the sensing network comprising the conductive medium is introduced into the composite adhesive layer to monitor the resistance of the conductive medium, so that the degumming area of the composite adhesive layer is determined. And carrying out real-time early warning on the battery according to the charging cycle index of the composite adhesive layer battery and the degumming area, the insulation resistance and the heat conductivity coefficient of the composite adhesive layer.

The lithium ion battery comprises a positive electrode, a negative electrode and a non-aqueous electrolyte, the negative electrode comprises a negative electrode active material, the negative electrode active material comprises graphite and an amorphous carbon layer coating the surface of the graphite, the thickness of the amorphous carbon layer is X [mu] m, and the non-aqueous electrolyte comprises a first additive, the first additive comprises a compound A with a structure of a chemical formula 1, R1 and R2 are independently selected from hydrogen, a nitrile group, a carbonyl group, a C1-C12 alkyl group, a C2-C12 alkenyl group, a C2-C12 alkynyl group and a C3-C12 cyclic alkyl group, the mass percent of the compound A in the non-aqueous electrolyte is a%, 0.1 < a < 20, and X and a meet the condition that X / a is more than 1 and less than 10. According to the invention, the compound A is introduced into the non-aqueous electrolyte as an additive, and the fast charge cycle performance of the lithium ion battery can be effectively improved by regulating and controlling the relationship between the thickness of the amorphous carbon layer and the content of the compound A. # imgabs0 #

The invention relates to a switching-type wide-range AC charging pile metering performance and electric energy quality tester, and the tester comprises a sampling unit which is used for carrying out the data collection of a charging pile; the self-adaptive spectrum decomposition engine is used for performing spectrum decomposition on the voltage and current signals obtained by real-time sampling based on a fast Fourier transform algorithm; the adapter structure interface module adopts a standard alternating current plug and a socket to form adapter connection and is inserted between the alternating current charging pile and the electric vehicle; the communication and positioning module is used for uploading the detection data to a provincial charging pile operation management platform in real time; the data storage and processing module supports recording of all metering data and event information in each complete charging period; and the power management module supplies power through alternating current input, is integrated with a battery standby power supply, supports short-time data caching and emergency communication in a power-off state, ensures data integrity, and has the characteristics of high accuracy, high integration, miniaturization, remote communication and the like.

A method for managing a charging process of a battery pack for an electric vehicle is provided herein. The method includes initiating a charging cycle for the battery pack, actively managing temperature of the battery pack during the charging cycle by flowing a temperature-controlled coolant through a thermal management system integrated with the battery pack, monitoring state-of-charge (SOC) and temperature of the battery pack, adjusting a charging current and a coolant temperature of the temperature-controlled coolant based on a coupled electro-thermal model to achieve a target SOC and a target temperature range for electric vehicle operation, and terminating the charging cycle based on the target SOC and the target temperature range being achieved within predefined constraints.

According to one embodiment disclosed in the present document, a battery diagnosis device may comprise: an interface unit which obtains the SOC after charging for each charging cycle for each of a plurality of battery cells; and one or more processors which calculate the charge amount relative to the average for each of the plurality of battery cells, on the basis of the SOC after charging, calculate the degree of change in the charge amount relative to the average according to the charging cycle of each of the plurality of battery cells, calculate an index value by applying a weight for each cycle to the degree of change in the charge amount relative to the average of a battery cell, among the plurality of battery cells, which is undergoing diagnosis, and diagnose an abnormality in the battery cell undergoing diagnosis on the basis of the index values for each cycle of the battery cell undergoing diagnosis.

The present invention discloses a preparation method and application of a positive and negative electrode additive for a lead-carbon battery, belonging to the technical field of lead-carbon batteries. The method comprises: mixing Ketjen black with an acid and uniformly dispersing the mixture; then shunting the resulting mixture at 70-90°C for 8-24 hours; cooling, washing, and centrifuging the mixture until the pH value is neutral; and then vacuum drying and grinding the mixture to obtain the positive and negative electrode additive for the lead-carbon battery. By adding a carbon material modified with an acidic oxygen-containing functional group as the positive and negative electrode additive, the present invention reduces side reactions at the positive and negative electrodes and reduces the loss of water in the electrolyte, thereby improving the cycle stability and high-rate state-of-charge cycle performance of the lead-carbon battery.

The invention discloses a battery changing cabinet battery aging evaluation method and system based on charging mode clustering, and relates to the technical field of battery changing cabinet battery detection. The method comprises the following steps: analyzing a battery charging cycle according to a charging period of a battery changing cabinet, and constructing a multi-dimensional charging characteristic; clustering learning is carried out based on the features, a plurality of charging mode clusters are constructed, health analysis is carried out on the battery based on the clusters, and battery health state parameters are obtained; calling real-time charging data, matching the charging mode cluster in combination with the health state parameters, and determining a target charging mode cluster; and performing battery aging evaluation according to the target charging mode cluster to generate an aging evaluation result. The technical problems of low evaluation precision and poor real-time performance caused by charging mode diversity of an existing battery aging evaluation method are solved, and the technical effects that refined analysis and dynamic matching of the battery health state are achieved based on charging mode clustering, and the accuracy, scene adaptability and real-time responsiveness of a battery aging evaluation result are improved are achieved.

The invention relates to a method for the open- / closed-loop controlling of a stepper motor. The stator windings of the stepper motor are controlled via a full-bridge circuit with switching elements. The switching elements can be controlled in such a way that a rapid-decay mode or a slow-decay mode can be set. A charge cycle time in a pulse width modulation (PWM) period is stored, and the rapid-decay mode or the slow-decay mode for the next PWM period is set according to the charge cycle time. The invention additionally relates to a control unit, and a stepper motor comprising the full-bridge circuit and the control unit.