2010 results about "Battery management systems" patented technology

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

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

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.

The invention discloses an energy storage system and a parallel operation control method thereof. The system comprises a battery cluster, a battery management system and an energy storage converter. The battery management system is respectively connected with the battery clusters and the energy storage converter, and the plurality of battery clusters are connected with the energy storage converter through the coupling circuit. Wherein a pre-charging equalization loop is arranged between the battery cluster and the energy storage converter, and the battery management system is used for controlling connection or disconnection of a circuit. The battery management system is also provided with a threshold module which is integrated with a plurality of parallel operation thresholds and is used for judging the switching-on or switching-off time of the contactor. By constructing a coupling system containing a pre-charging equalization loop, the problems of impact current and device damage caused by large inter-cluster voltage difference in the parallel operation process of a traditional multi-battery energy storage system are solved; and meanwhile, through a strategy of setting multiple parallel operation threshold values, targeted current balancing can be carried out according to different dynamic working conditions and electric energy parameter differences, the impact current is prevented from being too high, components are protected, and the operation stability of the system is improved.

The invention discloses an electric vehicle BMS battery state accurate monitoring system, and relates to the technical field of battery state monitoring, and the method comprises the steps: obtaining a multi-dimensional battery operation parameter set; inputting the multi-dimensional battery operation parameter set into a preset multi-physical field coupling model, and extracting a battery state correlation characteristic quantity according to electrochemical reaction characteristics and a heat conduction rule; calculating to obtain SOC, SOH and SOE initial parameters of the battery; the estimation deviation of battery aging is eliminated, and calibrated core parameters are obtained; identifying a fault type and a corresponding risk level; and synchronizing the calibrated core parameters, fault information and risk early warning signals to the vehicle-mounted terminal and the cloud platform in real time. According to the invention, a multi-physics field coupling modeling and estimation system is constructed, aging and temperature dual calibration of SOC and SOE is realized through SOH derivation characteristics, fault traceability and a double-end synchronization mechanism are linked, and accurate monitoring of the battery state under a complex working condition is achieved through modular collaborative design.

The invention relates to a lithium battery parameter identification method based on a physical information neural network, and belongs to the field of battery health and management. The method comprises the following steps: firstly, acquiring a data set, receiving battery charging data through a battery management system, then preprocessing the data, calculating label capacity through an ampere-hour integral method, and performing an experiment by using an electrochemical workstation to obtain specific electrochemical parameter data. The method comprises the steps that firstly, a single-particle model with an electrolyte is constructed, then an encoder is used for extracting multi-scale time characteristics of battery data, a decoder is introduced to accurately capture key information of the battery data, and finally, the single-particle model with the electrolyte is fused with a whole neural network model to construct a physical information neural network, so that the identification precision and efficiency of the whole framework on internal parameters of the lithium battery are improved. Experimental results show that the method can effectively identify six battery internal key parameters including a solid phase diffusion coefficient, electrical resistivity of an EI film, liquid phase lithium ion concentration, an electrolyte diffusion coefficient, effective electrolyte conductivity and transfer number.

The invention discloses a low-temperature exposed lithium battery aging damage diagnosis method based on electrochemical nondestructive characterization, which comprises the following steps: firstly, acquiring electrochemical response data of a to-be-tested battery through electrochemical impedance spectroscopy (EIS) test and incremental capacity analysis (ICA); secondly, key characteristic parameters capable of representing ohmic internal resistance, charge transfer resistance and electric double-layer capacitance of the battery and phase change peak position, peak area, half-peak width and the like are extracted; and then, training a joint prediction model based on machine learning by using the extracted multi-dimensional features to judge whether the to-be-detected battery is subjected to over-low-temperature exposure or not and judge the current aging damage degree of the to-be-detected battery. According to the method, unique aging paths introduced by normal aging and low-temperature exposure can be effectively distinguished through a nondestructive testing means, accurate'traceability 'of historical damage of the battery and quantitative evaluation of the current state are achieved, and reliable technical support is provided for intelligent decision making of a battery management system (BMS), battery safety early warning and echelon utilization.

In some examples, a vehicular battery management system (BMS) comprises a set of battery cells and a secondary network node coupled to the set of battery cells. The secondary network node is configured to wirelessly receive, in a first slot of a super frame, a unicast downlink packet from a primary network node, the unicast downlink packet addressed to the secondary network node. The secondary network node is also configured to wirelessly transmit, in a second slot of the super frame and responsive to the unicast downlink packet, an uplink packet to the primary network node.

The application discloses a battery pack and electrical apparatus. The battery pack includes a box body, a battery module, a battery management system, a high-voltage distribution box, and a sensor located inside the box body. The high-voltage distribution box includes a high-voltage distribution box body and an integrated wire harness. A plurality of wire harnesses are integrated in the integrated wire harness, and the wire harnesses extend from the high-voltage distribution box body in a direction toward the battery management system. The battery management system is connected to the battery module, the high-voltage distribution box, the sensor, and a port of the box body through the wire harnesses.

The invention discloses a double-sided heat dissipation common drain module packaging structure, and belongs to the technical field of power semiconductor device packaging. The module comprises a common drain electrode upper copper layer, two drain electrode copper terminals, two power chips, two source electrode copper blocks, two L-shaped source electrode lower copper layers, two driving grid electrode copper terminals, two driving source electrode copper terminals and a bonding wire. Drain electrodes of the two power chips are welded on the common drain electrode upper copper layer, and source electrodes of the two power chips are respectively welded with the L-shaped source electrode lower copper layer through source electrode copper blocks. And the bonding wire is connected with the grid electrode and the source electrode of the chip and the driving copper terminal to realize Kelvin connection. Thermal resistance is reduced through a double-sided heat dissipation structure, parasitic inductance is reduced through a three-dimensional commutation loop, power density and reliability are improved, and the common drain geminate transistor is suitable for common drain geminate transistor application in a battery management system.

The invention discloses a physical constraint multi-scale deep learning battery SOC estimation method and device and a medium. The method comprises the following steps: performing multi-scale time sequence feature extraction by using multi-branch separable convolution, and modeling fast transient response and long-term trend dynamics; a cross-scale feature enhancement strategy is configured, the problems of feature redundancy and network degradation in the multi-branch fusion process are solved, and cross-scale integration is achieved in combination with a dimension self-adaptive residual path and a channel attention mechanism; a physical information constraint module is embedded, soft charging and discharging constraint, zero-current constraint and hard SOC boundary constraint are fused, dynamic consistency and physical reasonability are ensured, multi-scale feature representation and physical information constraint are combined, the model generalization ability and field consistency are remarkably enhanced, SOC estimation precision is improved, and the method is suitable for large-scale SOC estimation. And a reliable solution is provided for a battery management system.

The invention belongs to the technical field of battery management systems, and particularly relates to a battery state-of-charge estimation method for resisting a voltage sensor fault, which comprises the following steps: S1, data acquisition and system initialization; s2, state prediction; s3, measurement updating and online compensation are carried out; and S4, recursive circulation is carried out. According to the method, the direct-current bias quantity generated by a voltage sensor due to zero drift is widened into a state variable of a battery model, and the state variable, ohm internal resistance, polarization internal resistance, polarization voltage and charge state of a battery jointly form an expansion state vector; and performing real-time recursive estimation on the extended state vector by adopting an unscented Kalman filtering algorithm, and simultaneously obtaining an estimated value of the offset of the sensor and an estimated value of the SOC of the battery subjected to deviation compensation. Through combination of state extension and unscented Kalman filtering, fault bias of the voltage sensor can be identified and compensated online, non-Gaussian noise interference is effectively suppressed, and accuracy and robustness of battery SOC estimation in a complex industrial environment are remarkably improved.

The invention relates to the technical field of new energy automobile heat management, and discloses a battery inlet water temperature-based dual-threshold decision-making method, which is realized through the following components: an intelligent collaborative decision-making center module, a multi-source data sensing acquisition module, a waste heat recovery and efficient heat exchange module, a precise execution driving module and a multi-level safety guarantee module. The intelligent collaborative decision center module comprises a heat management controller and an AI intelligent control unit; the multi-source data sensing and collecting module comprises a battery management system, a whole vehicle controller, an air conditioner controller and a full-dimension parameter monitoring sub-module. The waste heat recovery and efficient heat exchange module comprises a double-effect Chiller, an electronic expansion valve, a three-way valve and a four-way valve; the invention aims to solve the problems that energy consumption is increased and efficient circulation of energy cannot be realized due to the fact that waste heat generated in the operation process of components such as a motor cannot be fully excavated and reused and temperature control requirements are met by depending on additional heating or refrigerating components in the heat management of the existing new energy automobile.

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

The invention discloses a lithium battery health state prediction method and system based on an improved artificial travel mouse optimization algorithm, and relates to the technical field of lithium ion battery health state prediction. The historical operation data is preprocessed; a CNN-LSTM-Self-Attention hybrid prediction model is constructed, and the CNN-LSTM-Self-Attention Performing hyper-parameter optimization on the CNN-LSTM-Self-Attention hybrid prediction model by adopting an improved artificial travel mouse algorithm to obtain an optimization model; and deploying the optimization model to a battery management system, inputting operation data, outputting a health state prediction value, and triggering a maintenance instruction when the health state prediction value is lower than a threshold value. The invention provides a more efficient and more robust automatic hyper-parameter optimization framework so as to fully release the potential of the deep learning model in battery SOH prediction.

The invention discloses a new energy automobile charging method and charging pile combining overcharging and high-voltage charging technologies, and the method comprises the following steps: after an electric connection is established between an automobile and the charging pile, executing multi-stage identification and capability negotiation, obtaining a voltage grade, a maximum allowable current and a supported fast charging protocol of an automobile battery management system, and carrying out the fast charging of the automobile battery management system; generating an initial capability file in combination with the output capability of the charging pile; and based on the initial capability file, operating a multi-mode battery state estimation algorithm, fusing vehicle BMS data, field real-time measurement data and short-time environment prediction data, and generating a real-time battery health and rechargeable window. According to the invention, through pile end programmable hybrid energy routing and handshake negotiation, dynamic compatibility and efficient charging of different platforms such as 400V / 800V are realized; through multi-mode battery state estimation and a strategy engine, the advantages of overcharging and high-voltage fast charging can be combined under the safety constraint, the short-term energy complementing rate is maximized, and meanwhile the service life of the battery is protected.

The present disclosure relates to a wake-up function included in a battery management system (BMS) and a BMS wake-up method using the same. The BMS includes a micro-controller unit (MCU) configured to manage a battery, and a protection circuit configured to monitor the battery. When the protection circuit detects an abnormality in the battery while the BMS is in one of a shutdown mode and a sleep mode, the protection circuit outputs a wake-up signal to the BMS.

The invention provides an energy storage station trusted supply chain and command physical consistency verification method and device. The method comprises the following steps: setting a trusted platform module or a security element in a key device of the energy storage station, and generating a device trusted state identifier; adding a time quality identifier and a source signature to the measurement and state data; identity authentication, protocol analysis, object address and parameter range verification and rate constraint verification are carried out on the remote control instruction; generating a feasible region according to state estimation and equipment constraint, and performing executable judgment on the instruction; when any link does not meet the preset condition, switching to a verifiable security state and blocking a remote write-in path; and when the command firewall and the physical consistency check are both passed, a control instruction is issued to the converter control device and the battery management system, and power, reactive power and grid-connected and off-grid control of the energy storage station is executed. According to the application, supply chain credible closed loop, metering linkage access and double-loop instruction verification can be realized, and failure safety and verifiable evidence obtaining are supported.

The invention provides a charging activation device, an energy storage system and an activation method thereof, and belongs to the technical field of energy storage systems. The charging activation device comprises a standby battery pack, a boost inverter circuit, a voltage acquisition circuit and a microcontroller unit. Wherein the standby battery pack is used for providing a power supply for the boost inverter circuit and the microcontroller unit; the boost inverter circuit is used for boosting the input voltage to an activation voltage threshold of a battery management system of the energy storage system; the voltage acquisition circuit is used for acquiring the output voltage of an energy storage battery of the energy storage system; when the microcontroller unit detects that the energy storage system is in a locked state, the microcontroller unit can control to open the boost inverter circuit so as to boost an input voltage of the boost inverter circuit to an activation voltage threshold of a battery management system of the energy storage system and output the input voltage, so that the battery management system recovers a charging and discharging loop, and the energy storage system is activated. Automatic wakeup of the energy storage battery in the dormant state can be achieved, manual intervention is not needed, and dependence on manpower or external equipment is eliminated.

The invention relates to the technical field of charging pile control, in particular to an intelligent charging control method and system for a charging pile. The method includes: acquiring running state information of a power component in the charging pile; when the charging pile is switched from the constant-current charging mode to the constant-voltage charging mode, generating a power regulation strategy instruction executed by the charging pile according to the health state evaluation value, and transmitting the running state information and the power regulation strategy instruction to a battery management system of the vehicle; and the battery management system of the vehicle receives the operation state information and the power adjustment strategy indication, and adjusts the judgment logic of the battery management system on the power output fluctuation of the charging pile according to the health state evaluation value, the expected power fluctuation range and the power adjustment strategy indication. The problems that in the long-term operation of an existing charging pile, performance degradation of internal power components causes charging stability decline, and especially when a constant-voltage mode is switched to a constant-voltage mode, misjudgment of a vehicle battery management system is likely to happen, and charging is interrupted are solved.

The invention relates to the technical field of electric digital data processing and energy storage system safety control, in particular to an optical storage and charging integrated station energy storage system. Comprising a data acquisition module which is used for acquiring a state feature vector of an energy storage system, planned scheduling power of an upper-layer energy management system, an external power grid interference index, and a real-time safety state and an economic value index of a battery management system in real time; the risk assessment module is used for generating a predictive risk score; the decision threshold module is used for calculating a dynamic intervention threshold; the arbitration decision module is used for generating a negative sign; the instruction correction module is used for determining a derating factor in response to the override mark and generating a correction scheduling instruction; or when the negative sign is not generated, outputting the planned scheduling power as a corrected scheduling instruction. According to the invention, early warning and active prevention of the potential thermal runaway risk are realized, and the intrinsic safety level and the operation reliability of the system are obviously improved.

The invention relates to the technical field of energy storage battery management, and provides an energy storage battery management system based on Bayesian fusion and model prediction control, and the system comprises a data collection and preprocessing module which collects the voltage, current and temperature data of an energy storage battery pack in real time; the electrochemical model prediction module is used for updating parameters by adopting a recursive least square method based on a second-order RC equivalent circuit model to obtain a capacity prediction value and prediction uncertainty; the data driving model prediction module is used for obtaining a capacity prediction value and prediction uncertainty through Monte Carlo dropout reasoning; the Bayesian fusion module is used for calculating an evidence weight based on the prediction uncertainty and carrying out Bayesian fusion on the capacity prediction value; and the model prediction control module is used for solving an optimal equalization strategy based on the fusion capacity prediction value and generating a control instruction to realize intelligent equalization scheduling of the energy storage battery pack. According to the invention, the accuracy of energy storage battery capacity prediction is improved, and adaptive battery management under complex working conditions is realized.

A battery management system includes a sensing unit to generate a sensing signal indicating a battery voltage and a battery current of a battery, a memory unit to store a charge map recording a correlation between first to nth reference state of charge (SOC) ranges, first to nth reference currents and first to nth reference voltages for multi-stage constant-current charging, and a control unit to change to constant voltage charging using a kth reference voltage corresponding to a kth reference SOC range in response to the battery voltage having reached the kth reference voltage during constant current charging using a kth reference current corresponding to the kth reference SOC range to which an SOC of the battery belongs. The control unit updates the kth reference current of the charge map based on a time-series of the battery current in a charging period of the constant voltage charging.

The present application relates to a control circuit, a wake-up and sleep method, a battery management system, and a vehicle. The control circuit includes a controller area network (CAN) transceiver and a signal circuit connected to each other, and the signal circuit is connected to both an external controlled device and a controller; the CAN transceiver is configured to output a first control signal to the signal circuit according to a CAN bus message; and the signal circuit is configured to output a wake-up signal to the controlled device according to the first control signal, and output a sleep signal to the controlled device according to a second control signal output by the controller.

The invention provides a power battery health state evaluation method and device based on data driving, a medium, a terminal and a program product. The method comprises the following steps: respectively obtaining and screening operation data and laboratory data in a low SOC interval; establishing a dimensional unified mapping relationship between the accumulated discharge amount of the operation data and the cycle index of the laboratory data; calculating to obtain a minimum voltage average value corresponding to each piece of SOC data, and calculating to obtain a voltage change difference value; constructing an actual attenuation model according to the voltage change difference value to extract an actual minimum voltage average value; a reference attenuation model is constructed according to laboratory data, and a standard minimum voltage average value is obtained; comparing and judging based on a preset deviation direction judgment condition to obtain that the SOH value reported by the battery management system is too high or too low; and correcting the deviation of the SOH value based on a preset correction model. The method can adapt to SOH evaluation of a real running scene of the vehicle, the correction precision of the SOH deviation is improved, and the test cost is reduced.

The invention discloses an energy storage system parallel operation circuit and a control method thereof. The energy storage system parallel operation circuit comprises a master battery cluster loop, a slave battery cluster loop and a battery cluster pre-charging equalization loop. And the pre-charging equalization loop is provided with a controllable resistor bypass which is connected in parallel with the contactor and is controlled by the battery management system. A charging and discharging parallel operation strategy is divided, the voltage of a battery cluster is collected, a pre-charging equalization loop is connected, and primary and secondary closing operations are carried out based on the voltage state. The problem of circuit and device damage caused by impact current due to large inter-cluster voltage difference is solved through the master-slave configuration pre-charge equalization loop; meanwhile, the problem of long parallel operation time caused by separation of a pre-charging time sequence and an equalization time sequence is solved by combining a control method; through the dynamic secondary closing operation, the problem that balance is easily damaged by voltage fluctuation in the parallel operation process, and parallel operation fails is solved, parallel operation can be achieved without system restart in a loop under a complex scene in cooperation with a circuit architecture, and the parallel operation reliability, the operation stability and the charging efficiency of the energy storage system are effectively improved.

The invention discloses a storage battery pack health state evaluation and fault prediction method and system based on big data, and the method comprises the steps: data collection: collecting multi-source heterogeneous operation data in real time or quasi real time from a battery management system, an environment sensor and an operation and maintenance database of a target storage battery pack; data preprocessing and feature engineering: preprocessing the collected original data, and extracting a multi-dimensional health state feature vector; constructing an integrated machine learning model based on the historical data set, and establishing a model updating mechanism; performing online evaluation and prediction, and outputting a quantitative health state value, fault early warning information and a residual service life prediction value; and visualization and decision support. According to the method, multi-source data such as electrical data, working condition data, environment data and historical maintenance data are fused, deep features such as voltage consistency, capacity attenuation rate and internal resistance change trend are extracted, more comprehensive and accurate quantitative evaluation of the health state of the storage battery pack is achieved, and the one-sidedness and hysteresis of traditional single parameter judgment are fundamentally overcome.

A battery pack includes: a sensor that generates sensor data indicating an operating parameter of the battery pack; and an electronic controller including a machine learning program. The electronic controller is configured to: receive the sensor data; process the sensor data using the machine learning program, where the machine learning program includes a trained neural network model; and use the machine learning program to generate an output based on the sensor data, where the output indicates at least one of a state of charge (SOC), a state of temperature (SOT), a state of health (SOH), and a state of power (SOP) of a cell. The effective utilization of a battery is improved.

The invention provides an optimization method and system of a distributed battery management system. The method comprises the following steps: acquiring battery cell parameters, scene parameters and historical charging and discharging data of a battery cell, and evaluating a battery aging stage; calling a matched battery cell state calculation algorithm according to the scene parameters and the battery aging stage; calculating battery cell state information of the battery cell according to the battery cell parameters, the scene parameters and a battery cell state calculation algorithm; according to the cell state information, the scene parameters and the battery aging stage, dynamically adjusting an equalization threshold and an equalization rate, and executing an equalization strategy to obtain equalization state information; periodically uploading the operation data to a cloud server, receiving an algorithm update package, and updating an adaptive algorithm library; and calculating the overall state information of the battery pack according to the cell state information and the equilibrium state information. The calculation precision of the battery cell state can be effectively improved, the cycle life of the battery is prolonged, and the use safety of the battery is improved.

The invention relates to the field of vehicle-mounted batteries, in particular to a lithium precipitation control method (100) for a low-voltage lithium battery (2) of a vehicle (1), and the lithium precipitation control method (100) comprises the following steps: S110, detecting electrochemical impedance data of the low-voltage lithium battery (2); step S120, comparing the detected electrochemical impedance data with a threshold value; and step S130, outputting a control signal for performing relaxation on the low-voltage lithium battery (2) based on a comparison result. The invention further relates to a battery management system (3) for a low-voltage lithium battery (2) of a vehicle (1) and to a vehicle (1). According to the scheme, the lithium precipitation risk of the low-voltage lithium battery can be efficiently and accurately identified, and corresponding relaxation measures are taken to reduce or even avoid the lithium precipitation risk, so that the service life of the low-voltage lithium battery is ensured, and the running safety and reliability of a vehicle are improved.