651 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.

An intelligent system for charging and maintaining an electric vehicle using IoT and AI, consisting of: a charging management unit configured to monitor and control the electric vehicle's charging cycles based on real-time power availability and vehicle battery status, with the charging management unit connected to a power grid; an Internet of Things (IoT) communication module operationally coupled with the charging management unit and configured to establish a network connection between the charging management unit and external servers, the electric vehicle, and various sensors distributed throughout the vehicle, with the IoT communication module enabling real-time data transmission;a sensor network comprising a variety of sensors integrated into the electric vehicle, including temperature, voltage, and current sensors, each configured to continuously monitor critical parameters of the battery and vehicle components and transmit data to the IoT communication module; an AI-based predictive unit connected to the IoT communication module, configured to analyze historical charging data, the current battery status, and environmental factors to optimize charging times and predict the electric vehicle's maintenance needs; a control processing unit operationally coupled with the charging management unit and the AI-based predictive unit, configured to dynamically adjust charging cycles based on AI-generated optimization recommendations;a maintenance planning unit integrated into the AI-based prediction unit and configured to automatically schedule maintenance operations based on predictive analyses of vehicle component wear and battery condition, with the schedule communicated to the vehicle user via a user interface; and a data encryption unit connected to the IoT communication module, the data encryption unit being configured to encrypt all real-time communication between the electric vehicle, external servers, and maintenance systems to ensure the secure transmission of sensitive vehicle information.

Disclosed herein is a DC-DC converter, including a high-side power switch coupled between an input voltage and a switched node and a low-side power switch coupled between the switched node and ground. An inductor is coupled between the switched node and an output node. An output capacitor is coupled between the output node and ground. A control circuit is configured to operate the high-side power switch in a constant charge mode of operation to vary on-time of the high-side power switch to maintain a constant amount of charge being transferred to the output capacitor during each charging cycle, independent of variation of the input voltage.

The invention belongs to the technical field of power supply, and discloses a source network load storage power supply-based energy control method and system and a storage medium, and the method comprises the steps: determining a first photovoltaic power generation amount and a first power consumption demand amount of a prediction day at a set moment based on first historical data and prediction data, calculating a difference value between a first available power consumption amount and the first power consumption demand amount, and when the difference value is greater than a first preset value, establishing a cost objective function based on the capacity of the energy storage device, the difference value, the operation loss and maintenance loss of the energy storage device, and the electricity price cost of each time period in the prediction day, and charging the energy storage device based on the charging time and the charging electric quantity obtained by solving the cost objective function. And periodically determining a second photovoltaic power generation amount and a second power consumption demand amount in each set time period, and carrying out charging and discharging control on the energy storage device according to a source-grid-load-storage cooperative power supply mechanism. According to the method, the source network load storage power supply system can be comprehensively optimized through accurate prediction and control.

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 embodiment of the utility model discloses a secondary battery and a battery pack, the secondary battery comprises an electrode roll formed by winding a first pole piece, a diaphragm and a second pole piece, and the diaphragm is arranged between the first pole piece and the second pole piece; one of the first pole piece and the second pole piece is a positive pole piece, the other one is a negative pole piece, the first pole piece comprises a first active layer, and the second pole piece comprises a second active layer; the electrode roll is provided with a flat part and bent parts located at the two opposite ends of the flat part, at least one of the first active layer and the second active layer is provided with a first groove located in the flat part, at least one of the first active layer and the second active layer is provided with a second groove located in the bent part, and notches of the first groove and the second groove located in the same active layer face the active layer of the adjacent pole piece; in the second direction, the size of the first groove is larger than that of the second groove. By adopting the structural design, the electrolyte infiltration time of the thick-coated pole piece can be shortened, the stress of the pole piece at the bending part can be reduced, and the low-temperature performance, the rate performance and the fast-charge cycle performance of the battery can be 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 present invention provides a resistance property diagnosis apparatus, a diagnosis method, and a unit cell for a secondary battery satisfying a specific condition according to the method, the device comprising: an acquisition unit for acquiring a charge or discharge profile in a designated charge / discharge cycle of a unit cell; a generation unit for generating capacity-voltage differential data indicating a capacity-voltage differential value (dQ / dV) of the unit cell in a charge or discharge cycle included in the designated charge / discharge cycle, on the basis of the charge or discharge profile; and a diagnosis unit for diagnosing a resistance property of the unit cell on the basis of the capacity-voltage differential data.

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.

Examples of the present disclosure include the method. The method includes receiving a battery in a slot. The battery is removable from the slot to power a device separate from the slot. The method includes determining a profile for the battery. The profile includes at least one of a charging parameter based at least in part on a charging priority for the battery, a discharging parameter based at least in part on a discharging priority for the battery, or a combination thereof. The method includes initiating at least one of a charge cycle to charge the battery based at least in part on the charging parameter while the battery is received by the slot and a discharge cycle to discharge the battery based at least in part on the discharging parameter while the battery is received by the slot.

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 invention relates to the technical field of mining chargers, in particular to a mining charger charging module input voltage system which comprises a voltage adaptation control module, a rectification and filtering module, a voltage and current adjusting module and a charging system monitoring module. The microcontroller is used for real-time load demand measurement and calculation, and the output voltage of the transformer is automatically adjusted. According to the invention, through real-time current and voltage data analysis and in combination with a feedback mechanism, the voltage regulation precision and response speed are improved, so that the system automatically adapts to the fluctuation of a power grid in a mining area and keeps the voltage stable, thereby ensuring the operation reliability and continuity of mining charging equipment under an unstable power supply condition, reducing the risk of equipment damage, and improving the reliability and reliability of the system. Through efficient current and voltage regulation, the system not only reduces the energy loss, but also optimizes the charging period, and significantly reduces the maintenance cost and improves the operation safety while maintaining the long-term stable operation of equipment.

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.

A charging controller includes an internal resistance measurement unit configured to measure an internal resistance of a rechargeable battery multiple times over a charging period of the rechargeable battery; a detection unit configured to detect a peak of the internal resistance based on a change in the internal resistance measured by the internal resistance measurement unit; and an update unit configured to update a full charge capacity of the rechargeable battery referring to the peak of the internal resistance detected by the detection unit.

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.

The invention relates to the technical field of batteries, in particular to a battery. The battery comprises a positive plate, a negative plate, electrolyte and a diaphragm located between the positive plate and the negative plate, the diaphragm comprises a base material and a ceramic layer located on the surface of one side or two sides of the base material, the ceramic layer comprises inorganic particles, the ceramic layer is of a porous structure, the porous structure comprises holes with the diameter larger than or equal to 0.5 micrometer, and the inorganic particles are distributed in the range of 100 micrometers * 100 micrometers randomly selected on the surface of the ceramic layer. The number of the holes with the diameter larger than or equal to 0.5 micrometer is 10-800, and the coverage rate A1 of the holes with the diameter larger than or equal to 0.5 micrometer in the surface of the ceramic layer is 10%-50%; the electrolyte comprises an organic solvent, and the organic solvent comprises at least two of dimethyl carbonate, ethyl methyl carbonate, propylene carbonate, ethylene carbonate, diethyl carbonate, propyl propionate, ethyl propionate and ethyl acetate. The battery disclosed by the invention can have relatively high quick-charge cycle performance and rate capability under the condition that the safety performance is not influenced.

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.

To provide a management device and a method for management which can update software after a battery is charged to a target charge level in a charging period of time, in a case where an update period of time of software of a vehicle and a charge period of time of the battery of the vehicle overlap.SOLUTION: When at least a part of an update period of time for updating software and a preset charge period of time for charging a battery overlap, charging the battery is prioritized than updating the software in a charging period of time. Whether a charge level of the battery satisfies a target charge level is determined before the update period of time begins. If the charge level of the battery is determined to satisfy the target charge level before the update period of time begins, the time of starting the update is changed to the time when the charge level of the battery satisfies the target charge level and the software is updated from the time when the update period of time begins.SELECTED DRAWING: Figure 1

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.