9283 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.

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 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.

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 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.

A machine learning model may be trained for a target battery system. Reference initialization data may be generated by a reference battery system sharing one or more characteristics with the target battery system. Synthetic training data may be generated for the target battery system by determining simulated input parameters and simulated output parameters by supplying the simulated input parameters to a physics model. Upon determining that a subset of the reference initialization data matches a subset of the synthetic training data, a trained machine learning model based on the synthetic training data is determined. The trained machine learning model may be trained to predict a state of the target battery system based at least in part on one or more observed input parameters generated by the target battery system.

Examples relate to vehicle battery systems comprising a base unit connectable to a vehicle's electrical system, a removable modular replaceable battery, and a secondary battery within the base unit. The base unit is configured to interchangeably use modular replaceable batteries of different chemistries. Control circuits optimize charging and discharging parameters based on battery chemistries. The secondary battery provides power when the modular battery is removed. The system includes thermal management, compatibility with lead-acid charging systems, and mobile base unit functionality. The modular battery can power external devices and can foreseeably store energy up to and beyond 1 kWh. The system operates at various voltages suitable for different vehicle types.

The invention provides a lithium ion battery health state estimation method based on a TSLANet network, and belongs to the technical field of power batteries, and the method comprises the steps: obtaining multi-dimensional time sequence data of a lithium ion battery in an actual operation process, and carrying out the preprocessing of the multi-dimensional time sequence data; constructing a battery health state estimation model by using the preprocessed multi-dimensional time sequence data based on a TSLANet network and in combination with a neural network; inputting the operation condition of the current lithium ion battery system into a battery health state estimation model, and estimating the health state of the current lithium ion battery; the method is based on the TSLANet network, combines the Kalman filtering algorithm, the IC curve, the health factor and the neural network, constructs the battery health state estimation model to estimate the health state of the lithium ion battery, correctly reflects the long-term change trend of the battery performance, is excellent in noise data processing performance, and reduces the calculation complexity and the resource consumption.

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 provides a distributed BMS battery active equalization type safety management method and system, and the method comprises the steps: obtaining the state information and performance change trend corresponding to each single battery through real-time monitoring, constructing a target battery pack topological structure model, combining a reinforcement learning algorithm, generating an intelligent agent model, and employing the intelligent agent model and a fuzzy logic controller to achieve the active equalization type safety management of a battery pack. Determining an energy flow management scheme; based on the feedback data and historical data of the energy flow management scheme, using a deep belief network to construct an anomaly detection model, based on the anomaly detection model, generating a risk assessment report, and constructing a security record platform based on the block chain technology to obtain security record information; optimizing each model parameter based on the risk assessment report and the security record information, and generating a security management system in combination with a transfer learning algorithm and a quantum computing method; and high-precision monitoring, intelligent electric quantity balance regulation and control, anomaly detection, risk management and continuous optimization of the distributed BMS battery system are realized.

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 discloses a deep integrated high-voltage system of an extended-range vehicle and a power-on and power-off control method, and relates to the field of new energy high-voltage control. The system comprises a power domain controller, a range extender system, a driving system, a power battery system, a charging system, a thermal management system and a high-voltage auxiliary driving system; the power domain controller is used for integrating control functions of a whole vehicle controller, a battery management controller, a range extending controller and a motor controller on a vehicle so as to realize control management of the whole vehicle; the range extender system is used for executing a control instruction of the power domain controller so as to charge the power battery; the driving system is used for driving the vehicle and recovering braking energy; the power battery system is used for providing electric energy input for the whole vehicle; and the charging system is used for charging the power battery system. The hardware cost of the whole vehicle can be reduced, and later maintenance and upgrading are facilitated.

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 relates to the technical field of lithium battery thermal management, and discloses a lithium battery thermal management regulation and control method, device and equipment and a computer program product, and the method comprises the steps: collecting the temperature data of a lithium battery cell in real time through a plurality of embedded temperature sensors, and forming a temperature matrix; calculating heat flux density between the battery cells based on a Fourier heat conduction equation to generate a heat flux matrix; forming a cooling demand parameter matrix; and dynamically adjusting the flow velocity of the cooling liquid based on a particle swarm optimization algorithm, and automatically adjusting the flow velocity of the cooling liquid through a closed-loop feedback mechanism. Compared with the prior art that performance reduction or service life shortening of a lithium battery system is easily caused by overheating under high-power use or extreme environment conditions, and particularly, effective and uniform heat dissipation cannot be realized when the temperature difference of battery cells is relatively large, accurate heat management is realized by combining temperature real-time monitoring and adjustment of the flow rate of cooling liquid, and the service life of the lithium battery system is prolonged. Therefore, the problem of overheating or non-uniform local cooling is avoided, and the stability and safety of the battery system are improved.

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.

A discharge control method and system for a power battery system, and an electronic device and a storage medium. The method comprises: upon detecting an upper high-voltage request instruction, acquiring a voltage value of a first battery pack (110) and a voltage value of a second battery pack (120) (S301); determining whether the difference between the voltage value of the first battery pack (110) and the voltage value of the second battery pack (120) is less than a first voltage threshold value (S302); when the difference is less than the first voltage threshold value, controlling a power battery system to enter a parallel discharge mode (S303); when the difference is greater than or equal to the first voltage threshold value, controlling the power battery system to enter a partial discharge mode (S304); and during the process of the partial discharge mode, if the difference is less than a second voltage threshold value, controlling the power battery system to switch to the parallel discharge mode (S305). The method is used for solving the problem of the consistency between different battery packs being relatively poor.

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.

Provided in the present invention is an interference-factor-based high-safety and stable control method for an onboard magnetically suspended flywheel battery system. The method comprises: in the case of a safe operating condition interference, obtaining the difference between a displacement reference signal and an actual displacement signal, and inputting the difference into a Butterworth-filter-fused fuzzy active disturbance rejection control module, so as to obtain control currents, wherein the control currents comprise a radial control current, a torsion control current and an axial control current; the radial control current and the torsion control current are subjected to Clark inverse transformation, and the axial control current is subjected to power amplification, and the three currents are then compensated for, such that a three-phase control current of a radial coil, a three-phase control current of a torsion coil, and a control current of an axial coil are obtained; the control currents control the corresponding coils, and are also input into a multi-information extended Kalman filtering prediction module, such that the actual displacement signal is output; and in the case of a dangerous operating condition interference, cutting off an output signal from an operating condition recognition and safety determination module. The present invention can improve the stability, robustness and anti-interference capability of a system.

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 provides a special lithium ion battery system for a UPS (Uninterrupted Power Supply) based on intelligent management, and relates to the technical field of UPSs, the system comprises a battery state monitoring and predicting module, an intelligent charging management module and a fault diagnosis and fault-tolerant control module of a battery system, and the SOH and SOC of a battery are accurately predicted through a support vector machine, a random forest and a particle filter algorithm. A basis is provided for reasonable charging and discharging, the service life of the battery is prolonged, the intelligent charging management module dynamically adjusts charging parameters according to the SOC and the temperature of the battery through a fuzzy logic control algorithm, balance of rapid charging and battery protection is achieved, the intelligent charging management module adapts to different environments, and the fault diagnosis and fault-tolerant control module detects faults in time with the help of a support vector machine algorithm. A model prediction control algorithm optimizes control input when a fault occurs, it is ensured that the UPS system still operates normally under the battery fault, a data acquisition and transmission module and a system monitoring and display module monitor the battery state and provide historical data query analysis, and a user can conveniently master the battery condition and make a decision.

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 invention discloses a solid-state hydrogen storage fuel cell system and a control method. The solid-state hydrogen storage fuel cell system comprises a fuel cell stack and a solid-state hydrogen storage module, the solid hydrogen storage module provides hydrogen for the fuel cell stack; the solid hydrogen storage module is provided with a first heat dissipation mechanism, and the fuel cell stack is provided with a second heat dissipation mechanism; the first heat dissipation mechanism and the second heat dissipation mechanism exchange heat through the first heat exchanger; an inlet of the first heat dissipation mechanism is provided with a PTC1 heater; an outlet of the first heat dissipation mechanism is provided with a first temperature sensor; the first heat dissipation mechanism is connected with the air compressor through a cooling liquid circulation pipeline; the air compressor, the intercooler, the humidifier and the tail gas silencer are sequentially connected through air ducts; the humidifier interacts gas with the fuel cell stack; heat generated by the fuel cell stack and waste heat of the air compressor are used for supplying heat to solid hydrogen storage and release, so that a radiator of the air compressor is omitted, and the integration difficulty of the solid hydrogen storage fuel cell system is reduced.