7137 results about "Battery system" patented technology

The invention discloses an electric bicycle lithium battery data real-time monitoring and early warning system and method, and relates to the technical field of battery management, and the system comprises a multi-dimensional data collection module which is used for obtaining a multi-source heterogeneous data set of a lithium battery system; the collaborative feature extraction module is used for generating a comprehensive evaluation parameter set; the dynamic threshold generation module is used for constructing a self-adaptive early warning boundary model according to the comprehensive evaluation parameter set; the intelligent decision module is used for generating a hierarchical control instruction set based on a multi-objective optimization algorithm; and the cloud collaboration module is used for synchronizing the hierarchical control instruction set to the edge computing node and the cloud management platform, and triggering a multi-level linkage protection mechanism based on the game theory when the thermal runaway risk index is detected to exceed a first dynamic threshold value. According to the electric bicycle lithium battery data real-time monitoring and early warning system and method provided by the invention, the safety and reliability of a battery system are improved.

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The present application relates to the technical field of batteries, and discloses a thermal runaway risk prediction method and apparatus, a device, and a storage medium. The method comprises: processing, by at least two neural network layers in a target prediction model, thermal runaway risk parameters layer by layer, wherein the target prediction model is obtained by pre-training on the basis of state vectors of a plurality of time steps, so that the memory capability of the model for past state sequences can be enhanced, and thus the model can better learn the dynamic characteristics of an energy storage battery system, and captures a complex temporal association relationship among multiple variables, thereby improving the accuracy of thermal runaway risk prediction and reducing the safety risk of the energy storage battery system.

The invention discloses a battery cluster level fire-fighting linkage control method and system based on a BMS, and the method comprises the steps: carrying out the thermal runaway prediction through a multi-source feature fusion network according to the real-time voltage, temperature and gas concentration data of a battery cluster, and outputting a thermal runaway prediction level; on the basis of the thermal runaway prediction level, a dynamic priority mapping algorithm is utilized, and a control instruction set containing execution priority and spatial positioning is output according to the battery cluster topological structure; according to the control instruction set, a dual-channel redundant signal verification mechanism is adopted to execute a fire-fighting action, and a real-time action feedback signal is output; and on the basis of the real-time action feedback signal and the residual state data of the battery cluster, updating the parameter weight of the multi-source feature fusion network through a reinforcement learning model so as to be used for fire-fighting linkage control in the next period, and forming a closed-loop control link. According to the embodiment of the invention, battery cluster-level fire-fighting linkage control can be realized, so that the safety protection capability of a battery system is improved.

The invention relates to the technical field of battery power supply fault detection, and particularly discloses a power supply fault detection method and system for an electric vehicle battery, and the method comprises the steps: collecting the multi-modal data of the electric vehicle battery, carrying out the preprocessing and feature extraction of the multi-modal data of the electric vehicle battery, and constructing an intelligent diagnosis model. And carrying out potential fault propagation path prediction, predicting the diffusion path and speed of the fault in a battery system, carrying out electric vehicle battery fault positioning and early warning, and outputting a three-dimensional power supply fault thermodynamic diagram and a hierarchical autonomous disposal instruction of the electric vehicle battery. According to the invention, the problems of poor detection effect, emphasis on single fault diagnosis, large diagnosis difficulty, influenced accuracy and the like caused by incapability of accurately adapting to a fault detection method based on a model in a complex and changeable actual working condition in the prior art are solved.

The invention discloses a battery safety risk grade early warning and evaluation method, and relates to the technical field of lithium ion batteries, and the method comprises the steps: collecting battery operation data, carrying out the processing to obtain a capacity attenuation characteristic and an internal resistance growth characteristic, and obtaining a degradation characteristic parameter related to the temperature through an Arrhenius model; estimating capacity loss trends at different temperatures and time; inputting the multi-source time sequence data into a long short-term memory network, and predicting the capacity and internal resistance change of a plurality of cycles in the future; and in combination with a life end criterion, calculating the remaining service life, fusing the remaining service life with the degradation index of the aging model and the degradation index of the capacity model, generating a safety state index, and outputting a battery safety risk grade and corresponding early warning information. According to the invention, overall safety risk assessment can be provided for the whole battery system, accurate monitoring and early warning can be carried out on the single batteries or local modules, and the safety and reliability of battery operation can be improved.

The invention provides a battery life intelligent prediction method fusing a time convolution network and a Mama state space model. The method comprises the following steps: a TCN branch extracts local time sequence characteristics in a battery degradation process by utilizing multi-layer expansion causal convolution, and nonlinear degradation phenomena such as capacity regeneration and the like are effectively identified; the Mama branch is based on a selective state space mechanism, dynamically models a long-period dependency relationship of a battery aging track, and adaptively focuses a key degradation node through weight adjustment of context sensing. In order to enhance model robustness, a variational mode decomposition unit is integrated to carry out noise suppression and mode separation on an original capacity sequence, and the generalization ability of a cross-battery chemical system is improved in combination with normalized constraint of a hidden state matrix. The accuracy of life prediction is remarkably improved, and key features in a complex degradation mode are effectively captured; the method has excellent noise suppression capability and cross-model generalization, is suitable for multiple types of battery systems, and realizes real-time aging state evaluation.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention provides a dynamic reconfigurable battery energy storage system balance control method and device based on PWM duty ratio modulation. Belongs to the battery system control field. Voltage information of all single batteries in the energy storage system is collected in real time, total loop current, system operation modes and temperature distribution are monitored synchronously, a controller dynamically calculates charge state values of all the single batteries based on the information, a target object to be balanced is recognized, and meanwhile a global battery consistency evaluation index is established. The method dynamically adjusts the maximum allowable equalization current and generates an equalization topology. The modulation frequency, the duty ratio spectrum amplitude and the phase distribution are adjusted through PWM, and the maximum duty ratio is limited. All the operation instruction sets take effect synchronously at the starting moment of the next equalization period. Besides, the efficiency and working condition mapping table in the historical learning library is synchronously updated to form a closed-loop self-evolution optimization mechanism, so that the balance efficiency optimization of the system in a dynamic scene is realized, and the voltage overshoot is ensured to be within a set threshold value.

Methods are disclosed of controlling operation of a Li-ion battery system. Such methods include obtaining a predicted state of the Li-ion battery system from a reduced order model either with degradation (in first methods) or without degradation (in second methods), and correcting said predicted state by applying a Kalman filter to the predicted state and battery measurements such that an improved predicted state is generated. In second methods, degradation is modelled through degradation model separated or independent from reduced order model without degradation. Li-ion battery system is controlled based on the improved predicted state of the Li-ion battery system. Systems, computing systems and computer programs are also disclosed which are suitable to perform said methods.

The invention discloses a power battery health state on-line monitoring and early warning method and system based on multi-data fusion. The method comprises the following steps: synchronously acquiring multi-dimensional heterogeneous data in a battery operation process through a multi-sensor data acquisition module; carrying out standardization processing and preprocessing on the multi-dimensional heterogeneous data, extracting a comprehensive feature vector and carrying out multi-sensor data fusion; a multi-sensor data fusion algorithm is adopted to carry out weight distribution on the fused comprehensive feature vector, a dynamic weight coefficient is calculated according to the sensitivity and reliability of each parameter to the health state of the battery, and a fused battery state feature descriptor is obtained; and performing mode recognition and state classification on the fused battery state feature descriptors by using a pre-established evaluation model to realize quantitative evaluation of the health degree of the battery. According to the invention, real-time monitoring, evaluation and early warning of the health state of the battery in the whole life cycle are realized, and a reliable basis is provided for safe operation and maintenance decision of a battery system.

The invention relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

The invention discloses a battery fault identification method based on probability labels and identification feature learning. The method is suitable for modeling and discrimination of various fault states in small sample and weak label scenes. The method comprises the following steps: firstly, acquiring key parameters such as voltage, current and temperature in an operation process of a battery system, and constructing standardized time sequence characteristic data; secondly, three types of pseudo labels are generated based on multi-source information such as alarm time difference, prediction residual error and mahalanobis distance, and a unified abnormal probability label is obtained through weighted fusion; constructing positive and negative sample pairs according to the difference between the tags, and introducing difficult samples with similar features but large tag difference to enhance the discrimination ability of the model; then constructing a twin neural network structure composed of shared parameter sub-networks, inputting positive and negative sample pairs for comparative learning, and extracting low-dimensional embedding features with clustering and distinguishability; and finally, through calculating a space distance between a new sample embedding vector and a known fault type, identification of a current fault type and evaluation of an abnormal degree are realized.

The invention relates to the field of battery fault detection, in particular to a battery system health state evaluation method and system based on multi-dimensional feature fusion, and the method comprises the steps: S1, obtaining original operation data of a battery system, and carrying out the preprocessing of the original operation data to obtain a standardized data matrix; s2, based on the standardized data matrix, extracting health state features of a plurality of preset dimensions to form an original feature set; s3, constructing an incidence relation model among the features in the original feature set, and performing nonlinear fusion processing based on the incidence relation model to generate a low-dimensional fusion feature vector; s4, performing principal component analysis based on the low-dimensional fusion feature vector to extract a principal component, calculating a T2 statistical magnitude and an SPE statistical magnitude, and constructing a comprehensive health index; and S5, when the comprehensive health index exceeds a preset threshold value, analyzing the contribution degree of each feature in the original feature vector to the comprehensive health index, and positioning the fault single battery according to the contribution degree. The problems of insensitive fault symptoms, inaccurate fault positioning and high false report and missing report rate are solved.

The invention discloses a thermal management strategy management method and system based on an energy storage battery system, and relates to the technical field of battery thermal management, and the method comprises the steps: collecting temperature data, carrying out the preprocessing, generating and recording a measurement result and a convective heat transfer coefficient, taking the measurement result and the convective heat transfer coefficient as the input of a Kalman filter, and outputting an optimal estimation result of a battery. The temperature data refer to the environment temperature and the core temperature of the single battery, based on the optimal estimation result of the battery, establishing a fuzzy rule base for iteration and updating, outputting a dynamic temperature difference threshold value, constructing a three-dimensional thermodynamic potential function, calculating the cosine similarity of a cooling driving force vector and a cooling mode, and correcting the cosine similarity to obtain a cooling driving force vector. The mode switching list is generated, the cooling equipment decision mode is generated, starting judgment and optimization of the cooling mode are carried out, and the intelligent level, safety and energy efficiency ratio of thermal management of the energy storage battery system are remarkably improved.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case has at least one USB port and at least two access ports, at least two leads, or at least one access port and at least one lead. The portable power case is operable to supply power to an amplifier, a radio, a wearable battery, a mobile phone, and a tablet. The portable power case is operable to be charged using solar panels, vehicle batteries, AC adapters, non-rechargeable batteries, and generators. The portable power case provides for modularity that allows the user to disassemble and selectively remove the batteries installed within the portable power case housing.

Systems, methods, and articles for a portable power case are disclosed. The portable power case is comprised of at least one battery and at least one PCB. The portable power case has at least one USB port and at least two access ports, at least two leads, or at least one access port and at least one lead. The portable power case is operable to supply power to an amplifier, a radio, a wearable battery, a mobile phone, and a tablet. The portable power case is operable to be charged using solar panels, vehicle batteries, AC adapters, non-rechargeable batteries, and generators. The portable power case provides for modularity that allows the user to disassemble and selectively remove the batteries installed within the portable power case housing.

The invention provides a lithium ion battery health state prediction method, apparatus and device, and a storage medium, and relates to the technical field of lithium ion batteries, and the method comprises the steps of collecting real-time voltage, current and temperature data of a target battery system; inputting the real-time voltage, current and temperature data of the target battery system into a battery health state prediction model, and dynamically predicting the health state of the target battery system; wherein the battery health state prediction model is obtained by performing transfer learning fine tuning on a global pre-training model based on a private data set of the target battery system; the global pre-training model is obtained by performing distributed training through a federated learning framework based on operation data of a plurality of client lithium batteries under different charging strategies, federated learning and transfer learning technologies are combined, battery health prediction in a scene with privacy protection requirements can be processed, and the method and the system have high practicability. Moreover, the problem of data scarcity can be solved, and the real-time evaluation of the health state of the battery in a charging scene is realized.

The embodiment of the invention provides a battery system discharge control method, a battery system, a power utilization device and a battery management system. The possibility of over-discharge of a battery in the discharge process can be reduced. The battery system comprises a first battery and a second battery, the battery system comprises independently arranged energy areas, the first battery and the second battery are arranged in different energy areas, and the method comprises the following steps: acquiring a first state parameter and a second state parameter; determining first allowable discharge power of the first battery according to the first state parameter, and determining second allowable discharge power of the second battery according to the second state parameter; and determining target allowable discharge power of the battery system according to the first allowable discharge power and the second allowable discharge power, and controlling the battery system to discharge according to the target allowable discharge power.

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.

A thermal architecture of an electric vehicle includes a drivetrain system having a drivetrain, a cabin air system for providing conditioned air to an occupant compartment, a battery system having cooling components, and a refrigeration system for providing cooling to the cabin air system and the battery system. Control circuitry is configured to manage cooling or heating of components of the thermal architecture. For example, the control circuitry selects from among a mode for cooling a battery system and another mode for heating the battery system. In the heating mode, the control circuitry causes heat to be generated by the drive system, which may include one or more electric motors, and transferred to the battery system. The control circuitry receives a plurality of sensor signals from a sensor interface, generates one or more control signals, and cause valves and other components to achieve one or more cooling modes.

A battery system includes a lithium ion battery configured to couple to an electrical system, and a battery management system configured to electrically couple to the lithium ion battery and to control one or more recharge parameters of the lithium ion battery. The battery management system is programmed with an electrochemical model, and the battery management system is configured to monitor parameters of the lithium ion battery, and to control the one or more recharge parameters of the lithium ion battery based on the electrochemical model and the one or more monitored parameters. The electrochemical model determines lithium plating reaction kinetics at an anode of the lithium ion battery, determines a quantity of plated lithium at the anode of the lithium ion battery, or both, and indicates a relationship between the one or more monitored parameters and the lithium plating reaction kinetics, the quantity of plated lithium, or both.

The invention provides a super-computing center battery charging and discharging strategy optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a real-time operation data set of a super-computing center battery system in real time, and carrying out the load feature extraction of the real-time operation data set; the method comprises the steps of generating a load feature set of a battery system, inputting the load feature set into a pre-trained reinforcement learning strategy network, performing charge and discharge strategy priority matching processing on the load feature set, generating a priority ranking result of a target charge and discharge strategy, adjusting a current charge and discharge strategy set according to the priority ranking result, generating an optimized charge and discharge strategy set, and outputting the optimized charge and discharge strategy set. And performing charging and discharging operation based on the optimized charging and discharging strategy set, and collecting battery performance feedback data after the charging and discharging operation so as to iteratively update strategy matching parameters of the reinforcement learning strategy network. According to the method, the problems of over-charging and over-discharging and power oscillation caused by lagging response of environmental parameters in a traditional fixed strategy are solved, and autonomous decision-making of multi-target optimization can be realized without depending on artificial experience rules.

Presented are high-voltage (HV) electrical system architectures for vehicle-to-vehicle (V2V) charging of vehicle batteries, methods for making / using such systems, and vehicles equipped with such systems. An HV electrical system for a motor vehicle includes a main HV bus that electrically connects the vehicle's traction motor(s) to the vehicle's battery pack(s), and a charging inlet port that electrically mates with and receives direct-current (DC) power from DC fast-charging (DCFC) cables of a donor vehicle and a charging station. A DC-to-DC (DC-DC) converter is interposed between and electrically connects the charging inlet port to the main HV bus. The DC-DC converter is operable in a bypass mode, in which the DC-DC converter passes therethrough DC power received from the DCFC cable of the charging station, and a vehicle-to-vehicle (V2V) mode, in which the DC-DC converter modulates DC power received from the DCFC cable of the donor vehicle.

The invention discloses a porous ejector, a use method thereof and a fuel cell system, and belongs to the technical field of fuel cells. Comprising a shell, a cavity is formed in the shell, and the cavity comprises a suction cavity, a mixing chamber, a second flow stabilizing chamber and a diffusion section which are sequentially arranged; a nozzle body is mounted in the suction cavity and is used for accelerating and depressurizing the driving fluid and spraying the accelerated and depressurized driving fluid into the suction cavity to form a negative pressure area; an ejection opening is formed in the side wall, corresponding to the suction cavity, of the shell and used for introducing an ejection fluid, and the ejection fluid and the driving fluid subjected to acceleration and pressure reduction are mixed in the mixing chamber; a nozzle assembly is arranged at the end, close to the mixing chamber, of the nozzle body and comprises at least one nozzle, and driving fluid is accelerated through a hole channel of the nozzle. The invention provides a porous ejector, a use method thereof and a fuel cell system, and solves the technical problems of low fuel injection efficiency and insufficient energy utilization of a single-hole ejector used in a traditional hydrogen supply system.

The invention relates to the field of lithium battery detection, and particularly discloses a lithium ion battery fault detection method, which comprises the following steps of: firstly, acquiring total current data of a battery pack in real time, and judging the current running state of a battery system according to the total current of the battery pack and the duration time; and when the battery system is in a standing state, calculating the voltage drop rate and the temperature rise rate of each single battery in a preset time window, and comparing the voltage drop rate and the temperature rise rate with corresponding thresholds to realize self-discharge detection and fault identification. And when the battery system is in a charging or discharging state, a deep learning algorithm is further introduced, temperature-pressure coupling time sequence fluctuation characteristics of each single battery are extracted, and temperature-pressure coupling time sequence fluctuation state consistency analysis is performed on each single battery, so that intelligent detection of faults of the single batteries in the charging and discharging process is realized. According to the method, the abnormal temperature and pressure fluctuation of the single battery in the charging and discharging process can be effectively captured, so that early warning and accurate positioning of a fault are realized.

The invention discloses a battery thermal runaway early warning method based on multi-sensing information fusion, which is suitable for safety management of a lithium ion battery. A fiber bragg grating sensor is embedded into a battery, temperature and strain multi-dimensional physical parameters of the battery are monitored in real time, external electrochemical parameters are combined, and a multi-source sensing information fusion system is constructed. And by analyzing a square curve of an internal strain derivative, temperature gradient change and voltage drop characteristics, a thermal runaway early-stage signal is extracted, and graded early warning is realized. According to the method, the hysteresis of traditional surface parameter monitoring is broken through, an early warning signal is provided before thermal runaway, and the safety and reliability of a battery system are remarkably improved.

The invention provides a robot battery intelligent temperature control method based on environment temperature and user behaviors, which comprises the following steps: in combination with a pre-established user behavior habit model, analyzing and classifying a user behavior mode to obtain a behavior classification result; aiming at the behavior classification result and the environment temperature data in the original data set, constructing an association mapping relation between the temperature and the behavior mode to obtain a temperature-behavior mapping model; performing dynamic matching on the current environment temperature data and the user behavior mode through a temperature-behavior mapping model, and adjusting a charging and discharging strategy in combination with the battery state data to obtain a charging and discharging adjustment scheme; joint optimization is carried out on the charging and discharging adjustment scheme, control is carried out on a robot battery system, real-time monitoring is carried out, algorithm parameters are dynamically adjusted according to execution feedback data, a parameter set is obtained, and a final optimization control model is generated; the service life of the battery can be effectively prolonged, and the working efficiency and adaptability of the robot in different environments are improved.

The invention discloses an online EIS battery pack performance parameter estimation algorithm, and relates to the technical field of battery management, and the algorithm comprises the steps: generating a basic frequency conversion excitation signal through a digital signal generator, and loading the basic frequency conversion excitation signal to a working battery pack through a self-adaptive frequency sweeping strategy; acquiring a response signal, analyzing an impedance real part and an imaginary part of the battery pack at each frequency point through a digital orthogonal synchronous demodulation technology, and constructing an electrochemical impedance spectrum; extracting multi-dimensional health features from the electrochemical impedance spectrum, inputting the multi-dimensional health features into a fusion deep learning model, and estimating the state of charge and the state of health of the battery; and generating a system optimal control signal based on the estimated state of charge and state of health. According to the algorithm, portable, online and real-time detection of the performance parameters of the battery system in the working state is realized, and the application scene limitation of traditional EIS equipment is broken through; and through the optimized signal processing and intelligent algorithm, the detection stability and response speed are improved.

The charging and / or discharging of a set of swappable battery modules in a battery system is carried out automatically using a set of charging and / or discharging control switches linked in a sequential charging and / or a discharging control chain. The charging and discharging of the set of battery modules can be done sequentially or in parallel, and is hardware-based with minimal software / firmware involvement. The system is scalable and automatically reconfigurable for use in an infrastructure and / or in an electric vehicle. The swappable battery modules may be positioned in the battery slots or butted together in the battery system.