1954 results about "Battery capacity" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

The invention relates to a lithium ion battery life prediction method, and belongs to the field of battery life prediction and intelligent maintenance. The method comprises the steps that S1, a battery capacity degradation sequence is collected, and integrity is checked and normalized; s2, decomposing the sequence by using an improved complete set empirical mode decomposition algorithm, and dividing the sequence into a high-frequency component and a low-frequency component according to a zero-crossing rate; s3, modeling the high-frequency component: fusing multi-scale channel interactive attention, a time sequence convolutional network and a hybrid expert model, and extracting short-term fluctuation and capacity recovery features; s4, modeling a low-frequency component: introducing a two-way gating circulation unit network constrained by a double-index degradation model, and simulating a long-term trend; and S5, constructing a high-frequency migration module through tensor decomposition, improving cross-battery generalization, and fusing high and low frequency results to output a residual life prediction value. According to the method, a dual-channel framework combining signal decomposition, deep learning and physical modeling is combined, the prediction precision and adaptability under complex degradation are improved, and the method is suitable for various battery systems.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention relates to the technical field of energy storage systems, and provides an energy storage system capacity configuration optimization method based on battery capacity attenuation trajectory prediction. The method comprises the following steps: acquiring operation state data of a battery module in the energy storage system, establishing a historical operation database and a battery capacity attenuation trajectory prediction model, calculating an attenuation trajectory of battery capacity along with time through a temperature accelerated aging factor and a cyclic aging factor, and obtaining a capacity attenuation prediction curve in a future time period; and establishing a capacity configuration optimization function taking net present value maximization as a target by combining a load demand curve and an economic index of the energy storage system, dynamically adjusting charge and discharge depth limitation and a power distribution proportion, dynamically adjusting charge and discharge power of each battery module according to a real-time capacity state, and realizing optimized operation of the energy storage system. According to the invention, accurate prediction of battery capacity attenuation and optimization of full life cycle capacity configuration are realized, and the economic benefit and operation reliability of the energy storage system are improved.

The invention relates to the technical field of battery management, in particular to a battery replacement cabinet battery real-time monitoring and scheduling method, the health state of a battery is analyzed and evaluated according to a preset algorithm model, and the health state of the battery is evaluated by comparing a comprehensive health score, an internal resistance change rate and a capacity fading rate with corresponding threshold values; and determining a corresponding processing mode based on the evaluation result, including generating and sending abnormal alarm information to the operation and maintenance terminal, or executing an adjustable charging mode according to the state of residual electric quantity of the battery, or executing a protective charging mode according to the deterioration degree of the battery, the health state of the battery is actively evaluated to accurately identify an abnormal battery, for example, the electric quantity may be not low but potential safety hazards exist, in advance, so that targeted charging scheduling can be realized. According to the invention, real-time monitoring and active protection of the health and safety state of the battery are realized, so that the operation efficiency is improved on the premise of ensuring the safety.

The invention discloses a storage battery capacity prediction method based on multi-source asynchronous perception and time hybrid modeling, and the method comprises the steps: collecting electrochemical parameters of a storage battery pack, carrying out the preprocessing, obtaining a time sequence sample, and extracting a capacity time sequence; constructing a multi-channel convolution encoder family module, and performing independent feature extraction on the electrochemical parameters by taking a time sequence sample as input to obtain spatial feature mapping; constructing a time hybrid modeling module, and obtaining capacity time sequence feature mapping by taking the capacity time sequence as input; constructing a cross-parameter feature fusion layer, and inputting spatial feature mapping and capacity time sequence feature mapping to obtain health state features; inputting the health state characteristics into a regression prediction head, and outputting a prediction value of the storage battery pack; and constructing a main loss function to carry out model training. The method realizes an integrated prediction mechanism for the transformer substation storage battery capacity degradation process, can effectively adapt to the complex operation environment of a transformer substation, and has relatively high engineering feasibility and popularization and application values.

The invention discloses a battery capacity prediction and state evaluation method and system based on multi-model collaborative learning, and belongs to the technical field of battery management. The method comprises the following steps: constructing a database containing multiple lithium ion battery long-term cycle data, and classifying according to a capacity attenuation trend; cleaning and preprocessing short-term cycle data of the to-be-tested battery; matching the to-be-tested data with the long-term attenuation trend in the database by using a clustering algorithm, and determining an optimal matching trend; distributing weights for the data in the matching trend by adopting a correlation algorithm, and generating initial capacity attenuation prediction; performing sequence correction on the preliminary prediction in combination with meta-learning and a related model, and generating a smooth future attenuation trend conforming to a physical law; and outputting a capacity prediction and health state evaluation result, and evaluating the prediction precision through a root-mean-square error and an average absolute percentage error. The method significantly improves the precision and generalization ability of long-term capacity prediction, and is suitable for various scenes such as electric vehicles, energy storage systems, consumer electronics and the like.

The invention provides an intelligent charging pile control system based on a new energy automobile. The intelligent charging pile control system comprises a communication module, a data processing module, a charging power adjusting module, a safety protection module, a user interaction module, a cloud management platform, a power supply checking and recording module, an information backup and transmission module and a second-hand power supply monitoring module. The power supply checking and recording module is connected with a new energy automobile power supply system, detects and records parameters such as battery capacity and internal resistance, generates a power supply state report, realizes comprehensive detection and recording of the power supply parameters, provides data support for battery maintenance and fault diagnosis, assists an automobile owner to know the battery condition, and improves the safety of the automobile. A basis is provided for reasonable use and maintenance of a battery, basic data is also provided for subsequent battery management, the information backup transmission module locally backs up a power supply state report and transmits the report to a vehicle merchant service platform, the module realizes backup and transmission of power supply information, and a vehicle merchant can timely obtain detailed information of a vehicle power supply.

The invention discloses a power battery health degree online evaluation method and system based on a charging curve, and the method comprises the steps: dynamically dividing a characteristic interval of a constant-current charging stage and a constant-voltage charging stage through monitoring the voltage and current data in a charging process in real time; extracting time domain and frequency domain characteristic parameters in the characteristic interval; inputting the time domain and frequency domain characteristic parameters into a health degree evaluation model subjected to transfer learning optimization, and generating a fusion health degree index at least comprising a battery capacity attenuation coefficient and an internal resistance change vector; and outputting a final health degree evaluation result and residual service life prediction through an adaptive weighting algorithm based on the fused health degree index in combination with historical cycle data and operating environment parameters of the battery. According to the embodiment of the invention, high-precision and non-intrusive online evaluation and life prediction of the health degree of the battery can be realized, and the real-time performance, accuracy and engineering applicability of evaluation are improved.

A body for a vehicle configured for securing safety performance and vehicle rigidity according to a vehicle collision while increasing a battery capacity mounted in the vehicle, may include an internal side member mounted on a center floor in a longitudinal direction of the body; a battery cross member mounted in a width direction of the body inside a battery case provided at a lower end portion of the center floor while intersecting the internal side member; and a coupling structure coupled to the battery case, the battery cross member, and the center floor.

The invention discloses a battery capacity loss real-time prediction method based on Arrhenius-LSTM, and the method comprises the steps: S1, collecting the current, voltage, state of charge and temperature data of a vehicle-mounted battery management system, and generating an original time series data set; s2, performing time alignment and capacity calculation on the original time sequence data set to generate a capacity loss sequence; s3, an Arrhenius Arrhenius model is constructed based on the capacity loss sequence, and a physical baseline prediction sequence is generated through temperature related parameter estimation; s4, calculating a difference value between the capacity loss sequence and the physical baseline prediction sequence, and generating a residual sequence; s5, performing feature extraction and time sequence window construction on the residual error sequence to generate an LSTM training data set; s6, inputting the LSTM training data set into the long short-term memory network for training, and generating a residual prediction model; and S7, adding the physical baseline prediction sequence and the output of the residual prediction model to generate a capacity loss prediction result. And the online monitoring and early warning functions of the health state of the battery are realized.

The invention relates to the technical field of new energy charging, in particular to a charging power scheduling method, electronic equipment and a computer readable storage medium, and the charging power scheduling method comprises the steps: obtaining an initial charging SoC, a target charging SoC, a charging curve and a battery capacity of each charging vehicle, determining a plurality of SoC check points of each charging vehicle according to the initial charging SoC and the target charging SoC, determining a plurality of initial charging power groups and a reference charging duration from the initial charging SoC to the target charging SoC according to the charging curve, and iteratively updating each initial charging power group according to a particle swarm optimization algorithm, the battery capacity and the reference charging duration, and obtaining a target charging power group, and scheduling the charging power of each charging vehicle according to the target charging power group. By selecting the power of each charged vehicle at an individual SoC checkpoint for optimization, the computational complexity can be reduced, and timely charging can be ensured according to the target optimization power that minimizes the charging delay of all charged vehicles.

This invention discloses a configuration method for collaborative power supply equipment between the power grid and energy storage batteries in oil and gas drilling operations. Belonging to the field of energy storage technology, it primarily addresses the configuration issues of power grid capacity, minimum energy storage battery capacity, and maximum allowable charge / discharge power in collaborative power supply modes for oil and gas drilling. Based on Kalman filtering and power change rate analysis of existing historical drilling operation data, this method calculates the required power grid capacity and minimum energy storage battery capacity. Furthermore, it calculates the maximum allowable charge / discharge power of the energy storage battery based on the seamless switching requirements of the battery output. This method can be used to design equipment configuration schemes for collaborative power supply between the power grid and energy storage batteries in oil and gas drilling operations, reducing the energy supply cost of oil and gas drilling operations.

The invention is suitable for the technical field of battery safety monitoring, and provides a sodium ion battery energy storage system health state dynamic prediction method based on simulation digital twinning, and the method comprises the steps: constructing a digital twinning body comprising a physical layer, a virtual layer and a data interaction layer; performing health state estimation based on the physical layer and the virtual layer, and outputting a health state estimation value; comparing the health state estimated value with the measured value, calculating a residual error, and updating key aging parameters in the simulation model; inputting the updated key aging parameters into a pre-trained time sequence neural network model, carrying out sequence learning, and outputting a capacity attenuation trend and a residual life prediction result of the battery; generating a full life cycle optimization strategy based on the prediction result, and generating an optimal charge-discharge power table by simulating the influence of different charge-discharge strategies on the life; the problems of life attenuation and high operation and maintenance cost caused by insufficient prediction precision in the prior art are effectively solved.

The invention belongs to the technical field of batteries, and discloses a battery, which comprises at least two pole groups, a cover plate assembly and a shell, the pole groups are electrically connected, the end parts of the pole groups are connected with tabs, the end face of one of the adjacent pole groups facing the other is connected with a first connecting part, and the other one is correspondingly connected with a second connecting part. The first connecting part and the second connecting part are clamped; the cover plate assembly comprises a cover plate body and a pole, the cover plate body is arranged at the end part of the pole group in the first direction, and the pole is arranged on the cover plate body and is connected with the tab; the shell and the cover plate body are connected and enclose to form a space for accommodating a pole group; the first direction is the height direction of the cover plate body. Therefore, by arranging the first connecting parts and the second connecting parts and mutually splicing the first connecting parts and the second connecting parts, a plurality of pole groups can be connected, the battery capacity is improved, the positions of the pole groups in the shell are limited, the space utilization rate is improved, and the battery structure is more compact.

The invention discloses an energy storage lithium ion battery capacity abnormity identification method, system and equipment, and relates to the technical field of lithium ion batteries. The method aims to overcome the defects that an existing battery capacity diagnosis method needs to depend on complete charging and discharging data, a complex battery model or large-scale historical operation data support, and it is difficult to meet capacity anomaly judgment requirements under the conditions of rapidness, disturbance and data sparseness in actual operation. Obtaining a standing voltage curve after charging and discharging of the energy storage lithium ion battery are stopped, and constructing a standing voltage time sequence; selecting a group of wavelet functions satisfying multi-scale orthogonality, performing wavelet decomposition on the standing voltage sequence, and extracting low-frequency approximate components; performing feature extraction on the obtained low-frequency approximate components to obtain standardized three-dimensional features of each battery monomer; and clustering the batteries with abnormal capacities by adopting a clustering algorithm to obtain a normal battery category and an abnormal battery category. The method is mainly used for identifying the capacity abnormity of the energy storage lithium ion battery.

The invention provides a dynamic environment adaptive low-altitude route planning method and system, and the method comprises the steps: collecting multi-source environment information in real time, and constructing a flight environment model; according to flight historical data, in combination with the flight environment model, carrying out route planning; based on the battery capacity of the unmanned aerial vehicle, the endurance demand of the air route is checked, and the optimal air route is output; verifying whether the optimal route meets the requirements of safety and tasks or not, and controlling the unmanned aerial vehicle to fly according to the optimal route after verification is passed; and the unmanned aerial vehicle feeds back the flight state in real time, and dynamically optimizes the air route in combination with the flight environment model. The problems that in the prior art, the environment perception capability is insufficient, situation perception is missing, the optimization target is single, and the algorithm practicability is poor are solved. The problem of route planning of the unmanned aerial vehicle in a dynamic environment is solved.

The invention belongs to the technical field of batteries, and discloses a battery which comprises a pole group, a cover plate assembly and a shell, the pole group is connected with a protruding part and tabs, the protruding part is arranged on the end face of the pole group, and the tabs are arranged on the two sides of the protruding part in the first direction; the cover plate assembly comprises a cover plate body, a protective side plate and a pole, the cover plate body comprises a first part and a second part, the first part corresponds to the protruding part, the protective side plate and the first part define a space for containing the protruding part, the second part is connected with the first part, and the pole penetrates through the second part and is connected with the first part in the third direction. The surface, deviating from the pole group, of the first part protrudes out of the surface, deviating from the pole group, of the pole; the pole group is arranged in the shell, and the cover plate body and the protective side plates are connected with the shell. Thus, the protruding part can increase the capacity of the battery, assembling spaces can be reserved on the two sides of the protruding part so that the adjacent pole sets can be connected, the pole sets and the protruding part can be protected through the protection side plates and the cover plate body, and the structural strength of the cover plate assembly is improved.

The invention discloses a battery and a power utilization device, and belongs to the technical field of secondary batteries. The battery comprises a positive plate, the positive plate comprises a positive current collector and a positive active material layer, the positive active material layer comprises a positive active material and a lithium supplement agent, and the lithium supplement agent comprises an iron-containing lithium supplement agent; the ratio (set as a) of the characteristic peak area of Fe < 3 + > to the peak area of O1s in the hollow electric state in the XPS spectrum of the positive plate, the ratio (set as b) of the negative electrode discharge capacity to the positive electrode discharge capacity of the battery and the discharge curve end potential (set as cV) of the negative plate are adjusted, so that a * b * c is controlled within a suitable range, the gram capacity of the positive plate can be effectively improved, and the battery performance is improved. And meanwhile, the cycle performance of the battery is ensured.

An underwater robot application scene-oriented sodium battery residual life prediction method comprises the following steps of: obtaining normalized features after input features pass through a double-path feature normalization module; processing the input features through a multi-local sensing path module, and fusing and splicing the processed input features with the normalized features to obtain spliced features; performing 1 * 1 convolution operation on the input features, adding the input features and the splicing features element by element, and performing processing through a lightweight residual connection module to obtain multi-scale features; enabling the coder-decoder to predict an initial capacity prediction value according to characteristics rich in global time sequence information in the multi-scale characteristic data; performing element-by-element addition on the initial capacity prediction value and the normalized feature, and calculating a final sodium battery residual life prediction value; the coder-decoder can capture the long-range dependency relationship of the battery capacity sequence through a self-attention mechanism, the gradient disappearance problem of the recurrent neural network in the long sequence is solved, and the prediction precision is remarkably improved.

The invention relates to an electric vehicle power battery capacity estimation and remaining service life prediction method, and belongs to the technical field of electric vehicle battery management. The method comprises the following steps: S1, data preprocessing and calibration capacity fragment extraction; s2, data screening and feature selection; s3, deep learning SOH model training is carried out; s4, capacity prediction and data correction; s5, semi-empirical physical model fusion and gridding: establishing a battery capacity semi-empirical physical model, and performing gridding processing on the mileage-capacity data in the step S4 in combination with RANSAC fitting parameters; s6, carrying out vehicle clustering analysis; s7, constructing a physical model and a residual LSTM (Long Short Term Memory) model for vehicles of each clustering category, and predicting the residual service life by fusing static physical model parameters and a dynamic capacity-mileage sequence; and S8, evaluating and optimizing the model. The method improves the accuracy and robustness of capacity estimation and life prediction, and is suitable for vehicle-mounted BMS or cloud battery health management.

The invention provides a lithium ion battery health state assessment method based on physical information and data driving, and belongs to the technical field of lithium ion battery health state assessment, and the method comprises the steps: constructing a physical model for describing a lithium ion battery capacity attenuation rule according to empirical model constraints and physical rule constraints; a physical information neural network model is built, the physical information neural network model comprises an attention-based cross-modal fusion estimation network and a loss function embedded into a physical model, and optimization strategies such as adaptive weight equalization, time-frequency domain equalization and boundary hard constraint are introduced; training the physical information neural network model by using the battery aging data; and estimating the health state of the lithium ion battery by using the trained physical information neural network model. According to the method, under the condition that only a small amount of training data is used, the estimation precision and stability of the battery health state under the scenes of interpolation and extrapolation are remarkably improved, and the problem of small-sample battery SOH prediction is solved.

The utility model provides a battery capable of quantitatively repairing the capacity of a lithium ion battery. The battery capable of quantitatively repairing the capacity of the lithium ion battery comprises a shell, and a battery cell, an energy supplementing system and a battery management system which are arranged in the shell, the energy complementing system is connected with the battery management system through a wire harness; the battery cell comprises a cathode, a third electrode and an anode; the third electrode is used for supplementing lithium for the cathode and the anode; the third electrode is a porous electrode or a mesh electrode; the number of the negative electrodes and the positive electrodes is greater than or equal to 1; a layer of third electrode is arranged between each pair of negative electrode and positive electrode; the battery management system is connected with the negative electrode, the third electrode and the positive electrode through wire harnesses. The third electrode serving as a lithium supplementing electrode is arranged between the positive electrode and the negative electrode, the size of the third electrode is equivalent to that of the positive electrode, the third electrode is of a porous structure or a net structure, lithium ion transmission between the positive electrode and the negative electrode is not blocked, and the third electrode can stably exist in the electrolyte when not working (dormancy).

The invention discloses a battery capacity degradation prediction method based on parameter identification and neural network joint modeling. The method comprises the following steps: firstly, carrying out data preprocessing on voltage and current data of a lithium ion battery to extract more local information; then defining key parameters to be identified; carrying out parameter identification on key parameters in the physical model by adopting a genetic algorithm; generating a high-precision prediction voltage sequence for filtering instantaneous working condition disturbance by using a physical model; and finally, based on a PKA network, time sequence features are extracted, and high-precision capacity degradation trajectory prediction is realized. And evaluating MSE and RMSE indexes on the test set, and supporting uncertainty estimation and visualization. According to the method, the physical model is introduced to predict the voltage to serve as a feature enhancement signal, physical constraint and mechanism interpretation are provided for capacity degradation modeling, and therefore prediction precision, robustness and interpretability are improved, and the method can still operate when a capacity label is lacked in the early stage.

The present invention discloses a voltage-configurable rechargeable battery device and charging terminal and charging station, wherein the voltage-configurable rechargeable battery device comprises a rechargeable battery, a battery voltage management circuit, and a battery capacity acquisition circuit; the rechargeable battery is electrically connected to a charging terminal; the battery voltage management circuit comprises a boost / buck management circuit; the battery capacity acquisition circuit is connected to the battery voltage management circuit; the rechargeable battery can realize discharge curve following according to the current capacity via the battery voltage management circuit. The present invention can ensure basically consistence of the actual power level and the displayed power level, and improve users' experience.

The invention discloses an energy storage battery SOC and SOH joint estimation method based on adaptive double extended Kalman filtering, and the method comprises the steps: constructing a second-order RC equivalent circuit model of an energy storage battery, and building a state-space equation of a battery state and a battery parameter; measuring the open-circuit voltage of the energy storage battery under different SOCs through a pulse discharge experiment, and fitting a polynomial function relationship between the SOC and the open-circuit voltage by using a least square method; establishing a battery capacity model and an internal resistance model for energy storage battery SOH estimation; the measurement noise variance is dynamically adjusted based on an improved Sage-Husa adaptive algorithm, and joint estimation of the SOC and the SOH of the energy storage battery is realized through an iteration mode. According to the method, a relatively large state estimation error caused by fixed measurement noise is avoided, self-adaptive adjustment of the measurement noise is realized, and the reliability and the accuracy of SOC and SOH joint estimation of the energy storage battery are improved.