10190results about "Electric devices" patented technology

An electrified fire fighting vehicle includes a chassis, a front axle coupled to the chassis, a rear axle coupled to the chassis, a water pump, an energy storage system, an engine, an electric motor coupled to the engine and the energy storage system, and a power divider positioned between the electric motor, the water pump, and the rear axle. The power divider facilitates selectively driving the water pump and the rear axle with the engine and the electric motor.

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 field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

The invention relates to the technical field of battery management, and discloses a forklift battery charging method and device, and the method comprises the steps: collecting the real-time multi-dimensional physical state data, including vibration data, of a forklift battery; dynamically reconstructing a multi-physical field coupling model matched with the current working condition in a preset model library according to the data; performing predictive multi-target optimization based on the model to determine an optimal charging equalization strategy considering charging time, energy loss, health attenuation and thermal risk; charging is executed according to the strategy, the deviation between physical response and model prediction is compared in real time, and online self-correction is conducted on model parameters. The system comprises a state acquisition module, a model reconstruction module, a strategy optimization module and a charging control and correction module. According to the method, the digital twin model capable of self-evolution is constructed and closed-loop feedback correction is carried out, so that the charging strategy can actively adapt to working condition changes caused by factors such as vibration and temperature, and safer and more efficient charging management of the forklift battery is realized.

The invention provides a vehicle anti-skid control method, device and system and a vehicle. According to at least one driving wheel of the vehicle, the chassis wheel speed, the actual wheel speed, the wheel end actual torque, the adhesion coefficient, the total wheel load, the target slip rate, the wheel radius and the vehicle speed are obtained; a feedforward control torque is determined according to an attachment coefficient, a total wheel load and a wheel radius, a wheel speed deviation is determined according to a target slip rate, a chassis wheel speed and an actual wheel speed, a torque deviation is adjusted through a vehicle speed, the attachment coefficient and the wheel speed deviation to obtain a feedback control torque, and the feedback control torque and the attachment coefficient are superposed to serve as an output torque for vehicle anti-skid control. Dynamic wheel load changes and wheel load changes caused by the vehicle on the ramp road surface are considered, the total wheel load is estimated in real time, feed-forward control is introduced to obtain the feed-forward control torque, feedback control is compensated, and the stability and accuracy of anti-skid control are enhanced when the ramp road surface and the loads change.

The invention relates to the technical field of charging control, in particular to a new energy automobile charging safety regulation and control method and system, and the method comprises the steps: collecting a multi-dimensional feature parameter set; acquiring battery health state and charge state information, and collecting multi-point temperature information on the surface of the battery; constructing the multi-dimensional feature parameter set into a multi-dimensional time sequence, performing statistical analysis on the multi-dimensional time sequence, and extracting dynamic feature indexes; performing structured fusion on the dynamic characteristic indexes, thermal field gradient parameters calculated according to multi-point temperature information and a multi-dimensional characteristic parameter set, and projecting the dynamic characteristic indexes, thermal field gradient parameters and the multi-dimensional characteristic parameter set to a key characteristic subspace through principal component analysis to obtain a charging response vector; generating a multi-dimensional security hyperplane through the dynamic security boundary model; continuously comparing the charging response vector with the multi-dimensional safety hyperplane, and calculating a safety margin scalar in real time; and inputting the safety margin scalar into the self-adaptive regulation and control model, and adjusting the charging current output. According to the invention, active predictive charging safety regulation and control are realized through multi-dimensional perception.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention discloses a fault diagnosis and early warning method for an intelligent thermal management system of a power battery, and relates to the technical field of new energy automobile power battery safety. Comprising the following steps executed in sequence: S1) based on real-time monitoring data of a distributed temperature sensor in a battery module, constructing a dynamic space-time neural field model through an adaptive graph neural network, and generating a thermal field fingerprint representing a thermal propagation topology mode; according to the fault diagnosis and early warning method for the intelligent thermal management system of the power battery, a dynamic space-time neural field model is constructed, a sub-DEG C weak thermal abnormal signal is accurately captured, and a micro short circuit fault is recognized in advance in combination with electric heating delay analysis, so that the problem that a fault precursor signal is submerged by a complex working condition is solved; a double-channel verification mechanism depends on double mutual verification of physical law rigid constraint and intelligent residual feature recognition, the misjudgment risk caused by cooling system fluctuation and sensor noise is eliminated, and the early warning accuracy is improved.

A vertical take-off or landing (VTOL) aircraft is disclosed that includes a propulsion system comprising at least four tilting propulsion assemblies and two or more lifting propulsion assemblies. Actuation of the tilting mechanisms can convert the aircraft from a hover configuration, in which all propellers produce thrust generally upward to counteract the aircraft's weight, to a forward flight configuration, in which the four or more tilting propellers produce thrust generally in a forward direction parallel to the fuselage to overcome drag in cruise flight and the lifting propellers produce no thrust or a level of thrust generally less than that required in the hover configuration.

The invention provides a new energy automobile high-voltage system dynamic risk assessment method and device based on multi-source data fusion, and is applied to the technical field of data processing. According to the method, data such as high-voltage component operation parameters, battery states, environment perception, fault history and whole vehicle control instructions are obtained. Pre-processing the first working condition label, the second working condition label and the third working condition label, analyzing a CONTROLSIGNAL field to obtain a working mode of the high-voltage system, and generating a dynamic working condition label; combining a Pearson's correlation coefficient and XGBoost feature importance, screening risk sensitive features from the two, and forming a risk related feature subset; the method comprises the following steps of: establishing an improved Bayesian network model, training a subset by using an improved Bayesian network model, establishing an exclusive risk assessment model for different working conditions, reasoning real-time data by using the model to obtain a probability value of each risk dimension, and generating a dynamic risk assessment result and an early warning signal in combination with grade standard judgment.

The invention relates to the technical field of electricity metering anomaly monitoring, and provides a distributed resource access-oriented lightweight electricity metering anomaly monitoring method, which comprises the following steps of: acquiring electrical data and environment associated data of distributed access points at an edge side, and constructing a fluctuation fingerprint feature vector; when it is detected that the fluctuation fingerprint feature vector meets a preset fluctuation triggering condition, based on Pearson correlation analysis between the electrical data and the environment associated data, performing decoupling and elimination on false anomalies caused by environmental factors to obtain a fluctuation fingerprint feature vector of non-environmental suspicious anomalies; based on historical normal operation data, constructing a normal working condition mode library by adopting a clustering analysis method, and determining a feature center and a dynamic safety radius of each normal working condition mode; and calculating the feature distance between the non-environmental suspicious abnormal fluctuation fingerprint feature vector and each normal working condition feature center at the edge side, and judging whether the electricity consumption metering is abnormal or not according to the relationship between the feature distance and the dynamic safety radius.

Disclosed is a BMS-based battery energy storage safety management system. The system comprises a BMS module, a control center module, an evaluation analysis module, a coupling analysis module, a prediction module, and a data storage module. The BMS module collects monitoring data of a battery under different operation states and then, according to the monitoring data, performs initial monitoring of a real-time health state of the battery. The evaluation analysis module, according to a change in data indicators in the monitoring data, evaluates and analyzes all fault analysis models in the BMS module to obtain an evaluation analysis result. Then the coupling analysis module combines the evaluation analysis result with the data indicators of the monitoring data to retrieve multidimensional spatio-temporal data, and constructs a coupling field model to perform coupling analysis on battery operation state. The prediction module then constructs a fault prediction model by using a coupling data set as training data, accurately locates coupling faults of different coupling surfaces, and implements comprehensive analysis and location of coupling faults.

The invention discloses an electric vehicle charging monitoring method and device based on a heterogeneous feature fusion network, and the method comprises the steps: S01, collecting various time domain feature parameters and high-frequency key current signals of an electric vehicle battery in different charging states, and building a sample database; step S02, constructing an electric vehicle charging state prediction model, wherein the model comprises a time-frequency feature fusion network, a CNN-LSTM feature fusion module and a predicted value output module which are connected in sequence; step S04, the edge device receives the time domain characteristic parameters and the high-frequency key current of the tested electric vehicle battery in real time, and calls the deployed electric model for prediction to obtain a charging state prediction value; and S05, judging the abnormal charging state of the detected electric vehicle according to the state prediction value. The method has the advantages of being simple in implementation method, low in cost, high in monitoring precision, fast in early warning response, easy to apply on a large scale and the like.

This disclosure provides an improved universal interchangeable battery for electric vehicles forming part of a battery swapping ecosystem that includes electric vehicles, swappable batteries, a charging / swap station, a battery adaptor for reliable charging and communication, and a mobile application for managing battery swapping within a fleet and a network of charging stations, enabling efficient and user-friendly battery exchanges.

The invention relates to the technical field of automobile energy supply, in particular to a solid hydrogen storage-fuel cell integrated power assembly system based on dual-mode hydrogen supply, which comprises a hydrogen supply path identification module, a hydrogen storage unit state monitoring module, a linkage path switching module, a fuel cell load matching module and a safe hydrogen discharge regulation and control module. According to the method, the hydrogen supply pressure difference value is obtained by dynamically comparing the pressure of the cavity of the hydrogen storage tank with the pressure of the hydrogen inlet of the fuel cell, the current hydrogen supply path state can be accurately recognized in the early stage, rapid judgment of the hydrogen supply mode and accurate control of path switching are achieved, whether the risk of hydrogen supply interruption exists or not can be effectively screened, and the reliability of hydrogen supply is improved. The switching reliability of a hydrogen supply channel is improved through cross comparison of key state signals of a main channel and a standby channel, and instant response and energy consumption optimization of hydrogen discharge safety regulation and control are guaranteed through a linkage feedback mechanism between the hydrogen concentration change rate in unit time and catalytic converter control power.

The invention relates to the technical field of energy storage equipment monitoring, and discloses an energy storage power station equipment state monitoring and maintenance decision system. The system comprises an equipment topology overview construction module, a multi-source monitoring module and a health state compensation module. Wherein the equipment topology overview construction module generates an equipment topology overview containing electrical connection parameters and working condition parameters based on a physical connection relationship of an energy storage power station; the multi-source monitoring module collects equipment operation state data streams from the distributed monitoring nodes and obtains environment disturbance data in real time; and the health state compensation module performs missing node compensation on the equipment operation state data stream in the equipment state fusion monitoring mode to obtain a health state index of the energy storage equipment. According to the system, equipment topology information and multi-source data are integrated, accurate assessment of the equipment health state is realized, support is provided for maintenance decision of energy storage power station equipment, and equipment operation and maintenance efficiency can be improved.

The invention relates to the technical field of control and regulation, in particular to a new energy automobile battery health state prediction system which comprises a data acquisition module, a quantum feature evolution module, a digital twinning prediction module, a dynamic control module, a federal learning evolution module and a quantum verification module. The data acquisition module generates a spatio-temporal data base. The quantum feature evolution module outputs an ion desolventizing agent energy barrier change gradient, and the digital twinborn prediction module eliminates prediction offset through compensation and correction when the environment changes suddenly. The dynamic control module generates an environmental adaptation degradation suppression strategy. The federal learning evolution module encrypts transmission attenuation data and updates global model parameters, and the quantum verification module verifies the causality of a control instruction and rolls back to a safe steady state when the control instruction fails. The system forms quantum sensing, environment modeling, prediction compensation, risk suppression, model evolution and a safety verification closed loop, the battery life prediction precision in a dynamic environment is improved, and safety maintenance lag caused by environment sudden change is eliminated.

The invention relates to the technical field of intelligent automobiles, in particular to a battery performance test method and system for an intelligent automobile, and the method comprises the steps: obtaining the operation data of each single battery of the intelligent automobile under the current driving working condition and the working condition statistical data of the automobile, and constructing an input feature set, standardizing the input feature set, and inputting the standardized input feature set into a battery performance evaluation model to obtain key performance indexes of the single battery; dynamically adjusting a battery charging and discharging control strategy according to the key performance index and a real-time load demand under the current driving condition, verifying the accuracy of the key performance index by comparing the voltage fluctuation amplitude and the temperature change rate of the battery before and after adjustment, comparing with a preset safety threshold value, and if the threshold value range is exceeded, determining that the battery is in a charging state. If yes, generating corresponding performance early warning information; the method can adapt to complex and changeable driving working conditions of the intelligent automobile, and the precision and the real-time performance of battery performance evaluation are remarkably improved.

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

A battery thermal energy early warning control method, a system (1) and a new energy vehicle. The control method comprises: 1) setting a feedback current matrix of a battery module (2); 2) controlling charging and discharging of the battery module (2) on the basis of the feedback current matrix, and performing temperature control on a battery pack on the basis of the ambient temperature; 3) during the process of step 2), training and updating an SOC curve local model, fitting an SOC curve, when the SOC curve converges, determining that a battery is normal, and when the SOC curve diverges, determining that the battery is abnormal and sending out a first early warning signal; meanwhile, monitoring the temperature rise of the battery module (2), when the temperature rise speed is less than a third preset value, determining that the battery is normal, and when the temperature rise speed is greater than or equal to the third preset value, determining that the battery is abnormal and sending out a second early warning signal; when the first early warning signal and the second early warning signal are both valid, stopping charging and discharging, and sending out a fault early warning; and 4) during the process of step 2), if a working state exceeds a safety protection range, sending out a fault early warning, and initiating fire extinguishing and explosion prevention measures.

A system for pre-charging short circuit detection in electric aircraft. The system includes a charging connector and a controller communicatively connected to the charging connector. The charging connector includes a housing, at least a conductor, and at least a control signal conductor. The housing is configured to mate with an electric aircraft port of an electric aircraft. The at least a conductor is configured to conduct a charging current to the electric aircraft. The at least a control signal conductor is configured to conduct a control signal. The controller is configured to receive a circuit health datum, through the at least a control signal conductor, prior to initiation of charging of the electric aircraft, and to disable initiation of charging of the electric aircraft if the circuit health datum comprises a short circuit datum. A method for pre-charging short circuit detection in electric aircraft is also provided.

The invention provides a new energy vehicle fault diagnosis and prediction method based on deep learning, and the method comprises the steps: collecting the brake intensity sudden change data in the operation of a new energy vehicle in real time, and determining the trend and amplitude range of the current brake intensity change according to the brake demand signals under various working conditions in combination with the historical fault mode record analysis, thereby achieving the fault diagnosis and prediction of the new energy vehicle. Obtaining a preliminary braking demand characteristic value; according to the braking demand characteristic value, calling a pre-established deep learning prediction model, analyzing the relevance between a historical fault mode and current braking strength sudden change data, calculating the probability distribution of feedback energy anomaly detection, and determining the potential fault risk level of the new energy vehicle; and for the potential fault risk level, acquiring real-time data of surface temperature scanning of the brake disc, identifying a thermal load area, analyzing temperature distribution uniformity, judging whether a key area exceeds a preset threshold value or not, and obtaining a thermal load state evaluation result.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention relates to the technical field of power consumption management, in particular to a pump station unit energy consumption real-time analysis system which comprises a load feature extraction module, a frequency stability recognition module, a start-stop window recognition module, a voltage response regulation and control module and a signal filtering module. According to the method, a sliding trend sequence is constructed through load state data and current increment information, slope abrupt change points are identified, trend direction classification is carried out, accurate identification of a frequency operation stability section is realized in combination with frequency variation amplitude, and a regulation and control time window is constructed through trend consistency judgment of energy consumption and flow in the same time domain. A voltage input strategy is set according to the trend change rate, a mapping table is formed, signal filtering and recovery are achieved through trend continuity judgment in confidence evaluation of energy consumption labels, and the behavior segmentation precision, the adaptability of electric energy input regulation and control and the continuity and controllability of overall operation state recognition in the energy consumption monitoring process are effectively enhanced.

The invention relates to the technical field of aging detection, and discloses an inverter aging detection method and device based on multi-source data analysis, and the method comprises the steps: carrying out the multi-dimensional data set and synchronizing a timestamp, and obtaining a fusion data set; segmenting the fusion data set to calculate fluctuation amplitude, and when the fluctuation amplitude exceeds a threshold value, marking as an abnormal time period to generate a multi-dimensional feature vector; calculating a feature slope distribution weight, obtaining a weighted feature vector, calculating the similarity between the weighted feature vector and the feature pattern library, matching patterns of which the similarity is greater than a preset threshold value, extracting historical multi-dimensional feature vectors, and calculating parameters to obtain a mathematical description result; parameters in the mathematical description result are updated according to the feature vectors, and an early warning threshold value and a fault threshold value are calculated; when the feature slope in the weighted feature vector is greater than an early warning threshold but less than a fault threshold, generating an early warning signal; calculating a comprehensive health degree score distinguishing grade; and generating maintenance guidance information according to the health state level, and completing inverter detection. The method can solve the problem of resource waste.

The utility model provides a high-voltage interlocking detection system and a vehicle. The high-voltage interlocking detection system comprises a detection circuit, a plurality of signal processing modules and a control module. The detection circuit comprises a plurality of detection branches connected in series, the plurality of detection branches are correspondingly connected with the plurality of low-voltage detection connectors, and the detection branches output detection signals based on the connection states of the low-voltage detection connectors; the plurality of signal processing modules receive detection signals from the plurality of detection branches; and the control module determines the on-off state of the high-voltage interlocking loop and the connection state of the plurality of low-voltage detection connectors based on the detection signals processed by the plurality of signal processing modules. In the system, a plurality of detection branches are connected with a control module through different signal processing modules, wire harnesses are separated from one another, and a plurality of signal processing modules process a plurality of detection signals and then uniformly send the signals to the control module through a common node, so that the arrangement of the wire harnesses is reduced while reliable transmission of the signals is ensured, and the cost is reduced. And the whole circuit of the system is simplified.

The invention relates to an equalizing charge intelligent adjusting method for a power battery of a new energy automobile, which comprises the following steps of: acquiring a real-time state data set of each single battery from a power battery pack and carrying out filtering processing to acquire a state parameter sequence after filtering, the real-time state data set comprising voltage, temperature and charge state parameters; calculating a deviation value between each single battery and the group average value for the filtered state parameter sequence, and obtaining a deviation distribution matrix; if the deviation value of any single battery in the deviation distribution matrix exceeds a preset threshold value, the power battery pack is in an unbalanced state; performing battery state classification on the power battery pack in the unbalanced state, and obtaining an equalization strategy according to a classification result; and according to the equalization strategy control charging system and a preset equalization adjustment priority rule, carrying out equalization charging adjustment operation on the target single battery. According to the invention, the consistency of the battery pack can be improved, the service life is prolonged, the safety risk is reduced, and efficient and intelligent battery management is realized.