308 results about "Smart Battery" patented technology

The invention relates to the technical field of battery management, and discloses a state monitoring and predicting method of an intelligent battery pack and related equipment. The method comprises the steps of obtaining original battery pack data and historical operation data of various preset battery pack parameters in a target intelligent battery pack, and performing signal filtering and spectral decomposition on the original battery pack data; abnormal data detection, abnormal correction, multi-mode characteristic decomposition and phase space mapping of the battery pack state are carried out on the initial battery pack characteristic data; extracting corresponding state-of-charge dynamic characteristics in the state evolution characteristics of the battery pack, and performing multilayer mapping and threshold classification on the state evolution characteristics based on historical operation data to obtain a state monitoring result; and obtaining a state transition matrix through state parameter coupling mapping and transition probability calculation, performing state evolution path calculation, and outputting a prediction result. According to the invention, correlation analysis among electrochemical parameters is realized, and reliable evaluation and accurate prediction of the state of the battery pack are enhanced.

The invention discloses a storage battery abnormity early warning system based on an intelligent battery gateway, and relates to the technical field of battery monitoring. The system comprises a sensor module used for collecting voltage, internal resistance, temperature, thermal runaway state and floating charge voltage parameters of a single battery in real time; the intelligent battery gateway comprises a data processing module and an early warning module, and the data processing module carries out fusion analysis on sensor data through an intelligent algorithm, calculates the battery remaining capacity SOC and the state of health SOH and executes an intelligent equalization strategy; and the early warning module generates a multi-stage early warning signal according to the parameter threshold and the anomaly detection model. According to the invention, through a multi-dimensional fusion sensing technology and a dual-drive intelligent algorithm, the aging sign of the internal microstructure of the battery is accurately captured, and high-precision estimation of the residual electric quantity and the health state of the storage battery is realized; and in the aspect of early warning performance, a multi-dimensional linkage detection model and an intelligent early warning mechanism are established, so that the accuracy and timeliness of fault early warning are effectively improved, and the early warning time is greatly advanced.

The invention relates to the technical field of battery management, and discloses an intelligent battery management method and device, equipment and a storage medium. The method comprises the following steps: performing multi-source data acquisition and feature separation on a first sensing parameter and a second sensing parameter of a target intelligent battery to obtain a basic state feature, and performing frequency domain decomposition and space-time reconstruction on the basic state feature to obtain battery multi-modal data; deep coupling of electrochemical features and structural features is achieved through multi-level combination feature extraction and fusion, a fusion feature vector is obtained, separation of two sensing parameter channels, calculation of dual-channel weight and prediction of a multi-target time sequence are carried out on the fusion feature vector, and a multi-dimensional state evaluation index is obtained; and obtaining a comprehensive state evaluation index of the battery, thereby topping the corresponding fault risk level based on the comprehensive state evaluation index, and finally generating a battery management result of the target intelligent battery. And the accuracy and reliability of state evaluation and fault diagnosis of the intelligent battery are improved.

The invention discloses a multi-level feature fusion and time sequence modeling combined lithium battery life prediction method, and relates to the technical field of lithium ion battery health management and life prediction. The method comprises the steps of receiving time sequence observation data in a battery operation process, inputting the time sequence observation data into a pre-constructed local feature extraction model, and introducing a one-dimensional convolutional neural network into the local feature extraction model to perform feature extraction on the time sequence observation data to obtain local feature representation. According to the method, three structures of local feature extraction, global context modeling and bidirectional time sequence modeling are fused, and the battery degradation modeling capability and prediction precision are effectively improved. The TFN adopts an end-to-end architecture design, has good feature perception capability and time-dependent modeling capability, and can adapt to various degradation modes and complex time sequence environments. The method is suitable for life evaluation and health state monitoring in an intelligent battery management system, and has relatively high practical value and popularization prospect.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

The invention relates to the technical field of electric power, discloses an intelligent battery early warning system and method based on user side energy storage equipment, and realizes accurate prediction and early warning of battery performance by comprehensively monitoring real-time state parameters, environmental parameters and historical operation data of a battery and combining fault risk assessment, residual life prediction and fault trend analysis. By dynamically adjusting early warning conditions and adopting a multi-stage early warning mechanism, efficient and accurate battery health monitoring and fault prediction can be realized, the safety and reliability of the energy storage system are improved, and the service life of the battery is prolonged.

The invention provides an intelligent battery management method and system based on cloud-side cooperation, and belongs to the technical field of battery management, and the method specifically comprises the steps: determining the deviation condition of the charging and discharging power of an energy storage battery in different use cycle times from other energy storage batteries based on the use cycle data of the energy storage battery in an energy storage system, and determining the deviation condition of the charging and discharging power of the energy storage battery; and determining a defective battery in the energy storage batteries according to the deviation condition, and taking the defective battery removed as a reliably used battery, so as to determine that the operation reliability of the energy storage system meets the requirement according to the capacity of the reliably used battery according to the energy storage adjustment requirements in different historical energy storage adjustment times. According to the invention, the real-time energy storage adjustment data and the change data of the battery capacity of the reliably used battery are utilized to determine whether to access the cloud to control the energy storage battery of the energy storage system, so that the service life of the battery is prolonged.

The invention provides a battery management system design method applied to a direct-current photovoltaic micro-grid. The system comprises a data acquisition and monitoring module, an intelligent algorithm analysis module and a dynamic balance management module. Parameters such as voltage, current and temperature of the battery pack are collected in real time through a sensor, data are processed and analyzed through big data analysis and a machine learning algorithm, and the health state and the residual life of the battery pack are predicted. And the system dynamically adjusts the charging and discharging states of each battery unit according to an analysis result, so that the over-charging and over-discharging phenomena are avoided, and the service life of the battery pack is prolonged. According to the intelligent battery management system, the operation efficiency and reliability of the photovoltaic micro-grid are improved, and remarkable economic and social benefits are achieved.

Disclosed is a power electronic intelligent battery unit, including: a battery module, the battery module comprising a plurality of battery cells connected in series and sensors for measuring the voltage, current, pressure and / or temperature of the battery cells; and an intelligent battery interface, the intelligent battery interface being connected to an output side of the battery module and the sensors, and the intelligent battery interface having a power interface and an information interface for the outside, wherein the battery module monitors the voltage, current, pressure and / or temperature information of the battery cells, while providing or absorbing power by means of the intelligent battery interface.

The invention relates to the technical field of digital twinning, and particularly discloses an intelligent battery detection system based on digital twinning, which comprises battery detection equipment, an edge computing gateway, a cloud computing platform and a digital twinning visual interaction platform, the battery detection equipment is used for detecting charging and discharging data of a to-be-detected battery and uploading operation data of the battery detection equipment and the charging and discharging data of the to-be-detected battery to the edge computing gateway; the edge computing gateway is used for preprocessing the received data and uploading the preprocessed data to the cloud computing platform; and the cloud computing platform is used for analyzing and processing the preprocessed charging and discharging data of the to-be-detected battery to obtain a future state index of the to-be-detected battery, and then uploading the preprocessed operation data of the battery detection equipment and the future state index of the to-be-detected battery to the digital twin visual interaction platform for display. According to the invention, the real-time performance and accuracy of battery state prediction can be greatly improved.

The invention discloses an intelligent battery low-temperature top-speed heating method, system and device and a storage medium, and the method comprises the steps: obtaining the battery temperature and battery state information of a target battery, and the battery state information comprises an SOC state and a battery open-circuit voltage; when the battery temperature is smaller than a preset target temperature, determining an EIS impedance spectrum according to the battery temperature and the SOC state, and determining a target equivalent circuit model according to the EIS impedance spectrum; performing parameter updating on the target equivalent circuit model through a global optimization algorithm to obtain equivalent circuit parameters; and updating pulse parameters according to the equivalent circuit parameters, the battery open-circuit voltage and a preset boundary condition, and performing variable bidirectional pulse cyclic heating on the target battery according to the pulse parameters. The low-temperature heating efficiency of the lithium ion battery is improved, the energy consumption and the cost are reduced, and the method can be widely applied to the technical field of battery management.

The invention relates to the technical field of intelligent battery management, and discloses an intelligent battery hybrid parallel access system and a control method. According to the method, a battery pack is divided into a basic energy storage unit and a dynamic adjustment unit, a hybrid parallel access network is constructed based on a layered interaction architecture, unit voltage, loop current and temperature field distribution data are acquired through a multi-dimensional sensing module, feature extraction and weight fusion are performed by using a time sequence association processor, and a temperature field is obtained. And the input state decision module adjusts an access mode, and the two-way instruction analyzer executes mode switching. Meanwhile, real-time data and reference parameters are compared through a grading evaluation mechanism, after operation deviation is determined, an adjusting signal is transmitted through a feedback channel, an operation record is generated, and the response speed of the adjusting signal is in positive correlation with the deviation. The method comprises a hybrid architecture construction module, a multi-source data processing module and an operation deviation feedback module. According to the invention, efficient management and accurate control of the battery pack are realized, and the flexibility, stability and safety of the system are improved.

The invention discloses a prediction method for an electricity consumption demand of an angelica keiskei, an intelligent battery and a medium, and relates to the field of batteries, and the method comprises the steps: determining the daily discharge depth of the battery in each year of an analysis age limit based on the historical discharge data of the battery in the analysis age limit; according to the daily discharge depth of the battery in each year of the analysis age limit, determining the daily average discharge depth in one year; and obtaining the date of the angelica keiskei, and taking the average discharge depth corresponding to the date of the angelica keiskei as the predicted discharge depth of the angelica keiskei to realize prediction of the electricity demand of the angelica keiskei. According to the method, the discharge depth, namely the power demand, of the angelica keiskei is predicted based on big data analysis, so that charging is performed in time when the remaining capacity of the battery does not meet the power demand of the angelica keiskei, and the problems that the power demand of the angelica keiskei cannot be accurately predicted at present and power supply of the battery is affected when the remaining capacity of the battery does not meet the power demand of the angelica keiskei are solved.

The invention relates to the technical field of battery management, in particular to a modular intelligent battery management system, which comprises a sensing module for realizing full-dimensional monitoring of a battery through a multi-mode sensor array, implanting an MEMS stress sensing network and constructing a holographic stress tensor model; the operation and maintenance and decision-making module is used for realizing cooperative control of the battery units through dynamic impedance matrix construction, plasma resonance frequency monitoring and unbalanced thermodynamic gradient analysis; and the execution module is used for receiving the instruction sent by the stress analysis and optimization unit, controlling the piezoelectric composite material to generate a self-adaptive support structure, adjusting the internal mechanical structure of the battery, forming a stress shielding area and reducing the influence of stress on the performance and the service life of the battery. Through a dynamic impedance matrix and a main control unit election mechanism, the single-point bottleneck of traditional centralized management is avoided, and even if part of units fail, the system can still maintain the function through matrix recombination, so that the anti-interference capability and reliability are improved.

The invention relates to the technical field of electric vehicle battery thermal management, in particular to an intelligent battery thermal management method and system based on environment temperature self-adaptive adjustment. A real-time response mechanism based on the dynamic temperature difference between the environment temperature and the battery temperature is provided, and through the four steps of temperature index data collection, thermal management battery protection, thermal management adaptive control and thermal management dynamic adjustment, when the environment battery temperature difference is larger than a threshold value, a continuous high-power mode is started, a temperature region can be rapidly converged, and the power consumption is reduced. The target temperature zone arrival time is effectively shortened; when the environment battery temperature difference is smaller than the threshold value, the intermittent low-power mode is switched, and energy consumption minimization can be achieved; the working temperature of the power battery can be quickly controlled near or within the most suitable working temperature, and the probability that the battery is reduced or even does not generate related side reactions is ensured, so that the cycle life of the battery is prolonged.

The invention discloses an intelligent battery replacement cabinet with battery health degree detection, display and interaction functions, and the cabinet comprises a battery health degree detection module which is used for monitoring the state of a battery in real time and carrying out the charging or disabling of the battery; the display module is used for displaying the battery state and charging information in real time; the interaction function module allows a user to operate through a touch screen, a button or a voice recognition system; the background server responds to the battery replacement request in an offline state and generates and sends a cabinet opening password; and the data communication interface is used for connecting each module with a server for data exchange. According to the battery changing cabinet, the intelligent level and the user experience are improved, and the battery management and the system stability are ensured.

A smart battery box is provided that includes a battery for selectively powering a trailer main harness coupled to an ABS controller. The smart battery box includes a controller configured to respond to a command to control a switch to close to power the ABS controller through the battery. In this fashion, the ABS controller may be tested through the smart battery box while the trailer is disconnected from a tractor.

A battery pack can comprise a battery monitoring system that can monitor a battery metric of a battery cell of a battery system cluster board, in response to a determination that the battery metric satisfies a first bypass condition, enables a bypass mode applicable to the battery system cluster board, and in response to a determination that the battery metric no longer satisfies the first bypass condition, exits the bypass mode. A primary controller can in response to a determination that a second bypass condition applicable to the battery system cluster board has been satisfied, sends an instruction to the battery monitoring system to enter the bypass mode, and in response to a determination that the second bypass condition is no longer satisfied, sends an override instruction to the battery monitoring system to exit the bypass mode.

The invention relates to the technical field of intelligent battery module rolling detection, in particular to a tab visual inspection rolling system and method suitable for a battery module, and provides a tab visual inspection rolling system comprising a position adjusting mechanism, a rolling mechanism, a top face visual inspection mechanism and a side face visual inspection mechanism. Wherein the adjusting mechanism can adjust the horizontal position and height of the rolling mechanism so as to perform rolling processing on tabs of different batches of battery modules, and the processing condition of the tabs can be detected based on a visual form after rolling processing, so that on one hand, the detection precision is improved compared with the existing manual detection or spot check; and on the other hand, the labor intensity of manual detection is greatly reduced. The first top surface visual detection part, the second top surface visual detection part, the first side surface visual detection part and the second side surface visual detection part are arranged, so that the rolling quality is detected from two dimensions, the defect type thickness uniformity comprehensive evaluation is realized, and the misjudgment rate is reduced.

An intelligent battery charge depletion system for an electrified powertrain of a range-extended electrified vehicle (REEV) determines a modified state of charge (SOC) setpoint based on an estimated road load and an estimated gross combined vehicle weight (GCCW) of the REEV. The modified SOC setpoint is different than a charge sustaining SOC setpoint and is for a battery system having an SOC and that is configured to power an electric motor of the electrified powertrain. The estimated road load is for a road segment that the REEV is traversing. The system also controls the electric motor based on the modified SOC setpoint and the battery system SOC to maintain a torque reserve that the electric motor and the battery system can use when needed; and controls an engine of the electrified powertrain to selectively recharge the battery system.

The invention discloses an intelligent battery module with an automatic temperature control function. The intelligent battery module comprises a shell assembly, the two battery cell assemblies are arranged in the shell assembly in parallel, and an air duct is arranged between the two battery cell assemblies; the plurality of ventilation plates are tightly attached between two adjacent battery cells respectively; the sealing plate is hermetically arranged on the air duct; the plurality of temperature control air volume films are respectively arranged on the outer sides of the corresponding ventilation plates; the intelligent control fan assembly is arranged on the shell assembly in a penetrating manner and communicates with the air duct; the intelligent control module and the battery management system are fixedly arranged in the shell assembly. According to the battery module, the temperature control air volume film, the ventilation plate, the air duct, the sealing plate and the intelligent control fan assembly are matched to perform uniform cooling operation on a plurality of battery cells, and meanwhile, the intelligent control module realizes warning operation through AI training, so that the problems that the temperature difference between the upper and lower parts of the battery cells in an existing battery module is relatively large and intelligent control on the battery cells is lacked are effectively solved.

The invention provides an intelligent battery diagnosis system and method based on vehicle operation data and a large model of a hybrid electric vehicle. The method comprises the following steps: collecting charging and discharging data of operating vehicles of various types in different working modes; fault data, fault types and fault handling suggestions of all vehicle types are collected in a classified mode according to the fault types; constructing a fault prediction model based on multi-model fusion based on the collected data; training the large language model according to a mapping relation generated by the fault prediction model, and constructing an intelligent fault knowledge base; and inputting the charging and discharging data and the fault data output by the vehicle-mounted BMS into the intelligent fault knowledge base to obtain a disposal scheme corresponding to the fault data. According to the method, charging, discharging and fault data of the series plug-in hybrid electric vehicle are considered, the fault prediction model is constructed by adopting an intelligent algorithm, and the fault knowledge base is constructed through the fault data and the fault disposal data, so that the fault recognition accuracy and efficiency of the power battery and the fault disposal efficiency of the power battery can be greatly improved.

An electrical power supply system for an aircraft comprising: a string of a plurality of connected smart battery modules, wherein the string is configured to provide a common output voltage; wherein each of the smart battery modules comprises, terminals for outputting an output voltage to a device external to the smart battery module; a battery assembly configured to supply a DC voltage between two poles; a power converter electrically connected to the terminals and the poles, and a controller operably coupled to the semiconductor stage and configured to control the semiconductor stage for regulating the voltage conversation of the DC voltage into the output voltage a control unit operably coupled to the controller of each smart battery module and configured to set an output voltage and / or current setpoint or limit for each controller individually or configured to set an output voltage and / or current setpoint or limit for all controllers collectively.

The invention discloses a high-precision intelligent battery residual capacity detection method and device, and relates to the technical field of battery capacity detection. The method comprises the steps of collecting end current and end voltage of a target battery module in real time, and judging whether the target battery module triggers a characteristic load scene or not; when the characteristic load scene meets any one of a plurality of triggering conditions, recording a triggering moment; analyzing a polarization voltage difference value based on the trigger time, and introducing a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference value to obtain polarization loss data; obtaining the basic battery residual capacity of the target battery module; and performing polarization loss correction on the residual capacity of the basic battery by taking the polarization loss data as correction data to obtain and output the corrected residual capacity of the battery. The technical problem that the detection precision of the residual capacity of the battery is low due to the fact that the influence of battery polarization on the residual capacity is not fully considered in the prior art is solved, and the technical effect of improving the detection precision of the residual capacity of the battery is achieved.

The present invention discloses a sensory battery cell including a plurality of battery cells arranged in a first battery network, wherein the first battery network is a series network or a parallel network or a combination thereof, and a sensing integrated circuit connected in parallel with each battery cell to sense performance parameters of the plurality of battery cells, wherein the sensing integrated circuit includes a function circuit able to take an action for the battery cell that reaches a set condition. The present invention further discloses a intelligent battery using the sensory battery cell, a power battery using the intelligent battery, a battery management system and related methods thereof.

The invention relates to the field of photovoltaic technology, in particular to a photovoltaic power storage intelligent battery pack management system based on intelligent regulation and control, which is characterized in that a mobile battery vehicle carries a plurality of independent drawer type battery packs, is provided with positive and negative relays, is connected with a bus through a low-resistance copper bar, and is automatically conducted with an electric equipment interface through a power conversion connector after the battery packs are connected in series; each battery pack is provided with an independent sensor node, data of each drawer type battery pack is obtained through the sensor nodes, all the nodes are connected in series through twisted-pair shielded wires, and multi-node communication is achieved based on a CAN protocol; according to the real-time electricity demand, an optimal drawer type battery pack combination is dynamically selected by applying a greedy algorithm, and a BMS host controls the on-off states of a plastic shell switch, an air switch and a relay array according to a decision result; an emergency stop protection mechanism is arranged and comprises DI1 and DO2. Each drawer type battery pack is an independent energy storage unit, so that a single battery pack can be conveniently maintained, overhauled and replaced, and the work of other battery packs is not influenced.

The invention relates to the technical field of new energy equipment charging, and particularly discloses an intelligent monitoring system for the charging state of an inland ship, and the system comprises a charging track system which employs a parallel overhead contact network mode; the top of the ship powered device is provided with a liftable pantograph with an intelligent adjusting device; the ship end control system and the central control system are in two-way communication; the ship power supply system comprises a power supply access and power supply conversion system, an intelligent tension adjusting system and a power supply monitoring system and is used for supplementing power to the ship; meanwhile, the intelligent battery management system distributes and dispatches electric energy according to the charging requirement of the ship and the real-time load condition of the overhead contact system; when the high-voltage mains supply is powered off or unstable, the standby power supply system can switch a high-capacity energy storage battery pack mode or a small emergency generator mode; the safety of the ship in the charging process is guaranteed through the safety protection system; the urgent demand that the ship cannot be quickly charged during sailing is solved, and reliable charging service is provided.

The invention discloses a small electric freight vehicle SOC prediction method, and belongs to the technical field of electric vehicle battery management. According to the method, aiming at the problem that a traditional SOC prediction model is insufficient in precision under a cross-speed working condition, the driving working condition is divided into three typical scenes, namely a low-speed scene, a medium-speed scene and a high-speed scene through K-means clustering; for each scene, screening key features by adopting an SHAP method to construct a scene feature set; constructing a hybrid model by using a time sequence convolutional network and a long short-term memory network, and training by using each scene feature set to obtain a corresponding scene prediction model; in practical application, according to a real-time vehicle speed matching scene, a corresponding model is called to carry out SOC prediction. According to the method, the SOC prediction precision under different speed working conditions can be improved, the model scene adaptability is enhanced, and support is provided for electric vehicle endurance estimation and intelligent battery management.

The invention discloses an electric vehicle battery replacement encryption system and method, and relates to the technical field of electric vehicle battery replacement encryption systems.The electric vehicle battery replacement encryption system comprises a communication unit, a biological characteristic collection unit and an encryption chip and is used for conducting identity authentication and initiating a battery replacement request; the intelligent battery replacement cabinet module comprises a rail type battery bin array, a battery identification unit and an emergency power supply unit; the transfer guarantee module is used for automatically transferring by using a mechanical arm; the cloud collaboration platform is used for deploying a block chain node and a privacy calculation module and is used for verification code distribution, battery full life cycle tracking and cross-regional resource scheduling; the intelligent battery module comprises a multi-dimensional sensor group and an anti-disassembly self-locking mechanism. According to the electric vehicle battery replacement encryption system and method, by arranging the user terminal module, the intelligent battery replacement cabinet module, the transfer guarantee module, the cloud collaboration platform, the intelligent battery module, the payment gateway module and the like, the potential safety hazard of identity counterfeiting in the prior art is solved, battery information matching is guaranteed, and the situation that a high-quality battery is replaced stealthily is effectively avoided.

Embodiments described herein provide smart battery based electric vehicle charging systems with multiple input ports and multiple output ports. The system is flexible and adaptable for different EV charging and battery technologies. Embodiments described herein provide smart mobile or stationary battery based electric vehicle charging systems with multiple input ports and multiple output ports. The system is flexible and adaptable for different EV charging and battery technologies. The system can be a mobile battery based electric vehicle charging systems with multiple input ports and multiple output ports in some embodiments. The system can be a stationary battery based electric vehicle charging systems with multiple input ports and multiple output ports in other embodiments. The system that is stationary can be fixed at a specific location, such as an EV charging facility, a commercial complex EV charging centre, a residential property, or other location.