10232 results about "Power battery" patented technology

The invention provides a power battery charging and discharging management optimization method and system based on deep learning, and the method comprises the steps: firstly obtaining real-time battery state data including a battery temperature sequence, a voltage fluctuation sequence and a current change sequence during the charging and discharging of a target power battery, and inputting the real-time battery state data into a pre-trained deep learning model; a battery temperature time-varying characteristic, a voltage fluctuation periodic mode and a current change abnormal fluctuation interval are analyzed through different branches of a deep learning model, and a dynamic optimization strategy which is matched with a current charging and discharging stage and contains a charging and discharging rate adjustment threshold value and the like is generated based on a fusion analysis result of the characteristics. And finally, charging and discharging parameters are adjusted in real time according to the dynamic optimization strategy, so that the charging and discharging parameters meet preset battery health degree constraint conditions and energy efficiency maximization is realized, thereby intelligently optimizing charging and discharging management of the power battery, improving the performance of the battery and prolonging the service life of the battery.

The invention provides a square power battery shell visual defect rapid detection method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a high-resolution multi-view image sequence, outputting standardized image data, carrying out front visual scratch detection, outputting scratch defect positions and features, and outputting defect candidate regions containing confidence scores; and recognizing bubble defects, summarizing recognition results of the defect candidate areas, and generating a labeling image. The method solves the problems that in the prior art, due to the fact that multi-view image information is not fully fused, traditional scratch detection depends on a single edge feature, and a main defect recognition model is insufficient in recognition capacity for tiny defects such as bubbles, defect detection accuracy is low, missing detection and false detection phenomena are serious, and detection efficiency is high. According to the method, the problems that the defect is difficult to comprehensively and accurately locate and classify in the prior art are solved, the multi-view information fusion capability and the identification accuracy and detection efficiency of multi-class defects are improved, and high-precision, comprehensive and real-time quality monitoring of defect detection results is realized.

The invention belongs to the technical field of battery monitoring, and discloses an electric vehicle power battery health condition real-time monitoring method and system, and the method comprises the steps: obtaining multi-parameter data such as voltage, current, temperature, internal resistance and the like through a battery management system, and carrying out the synchronous collection of the data through a preset sampling frequency, and obtaining a multi-dimensional time series data set; aiming at the multi-dimensional time sequence data set, adopting a feature extraction algorithm to separate dynamic change features of voltage, current, temperature and internal resistance to obtain a multi-parameter feature set; if the deviation of the battery health state output by the dynamic mapping relation model exceeds a preset threshold value, analyzing a fluctuation mode in the multi-parameter feature set through an anomaly detection algorithm, and judging whether a potential degradation risk exists or not; and according to the comprehensive representation of the battery health state, a time sequence prediction algorithm is adopted to analyze the future change trend of the multi-parameter feature set, and a short-term prediction value of the battery health state is generated.

The invention belongs to the technical field of power batteries, and particularly relates to a power battery attenuation prediction method based on a depth auto-encoder, which comprises the following steps: firstly, collecting new energy automobile power battery sample data, pre-processing, and constructing a depth auto-encoder model; adjusting the parameters of the depth auto-encoder model according to the gradient information of the loss function by adopting an optimization algorithm until the training of the depth auto-encoder model is completed; then collecting real-time operation data of a current target new energy automobile as original data, inputting the original data into the depth auto-encoder model, outputting reconstruction data, calculating reconstruction errors, associating the reconstruction errors with the health state of the power battery, and representing the reconstruction errors as a health index of the power battery; and finally, based on the time sequence data of the power battery health index, predicting the decline trend and the residual life of the power battery by adopting a bidirectional LSTM + Attention mechanism. The problem of low accuracy of a power battery life prediction technology in the prior art can be solved.

The invention relates to the technical field of new energy automobile thermal management, in particular to a direct cooling and heating management system and method in an automobile power battery. The method comprises the following steps: acquiring vehicle real-time operation condition data and battery pack state monitoring data; working condition mode recognition processing is conducted according to the vehicle real-time operation working condition data, and vehicle working condition mode feature data are generated; performing thermal load demand analysis on the battery pack state monitoring data based on the vehicle working condition mode characteristic data to obtain dynamic thermal load demand data of the battery pack; and according to the dynamic thermal load demand data of the battery pack, refrigerant demand quantity calculation and analysis are carried out, the refrigerant supply capacity of the current direct cooling system is evaluated, and refrigerant supply and demand matching feature data are obtained. The specific requirements of three heat sources of the battery, the motor and the cockpit for the refrigerant flow are quantified, and a refrigerant distribution strategy is formed according to the proportional relation, so that the safety of the battery is improved, and the driving comfort and the working stability of the electric driving system are also guaranteed.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

The invention relates to the technical field of energy, and discloses a distributed electric power energy storage system and method, and the system comprises a decommissioned battery sorting and modularization module which is used for carrying out the performance evaluation, grading and standardized modularization processing of decommissioned power batteries; the energy storage unit module is used for assembling the retired batteries into an energy storage unit and storing and releasing electric energy; the energy management module is used for monitoring the running state of the energy storage unit and optimizing energy distribution; the communication network module is used for carrying out data transmission and cooperative control between the modules; and the power grid and user interface module is used for interacting the energy storage system with a power grid and a user side. According to the method, the energy scheduling unit can optimize the charging and discharging strategy of the energy storage unit in real time by combining reinforcement learning and federated learning algorithms, and meanwhile, the adaptability of a scheduling model in multiple scenes is improved by utilizing cooperative training of distributed nodes.

The invention discloses spherical aluminum oxide and a silicone rubber heat conduction composite material thereof, and belongs to the technical field of functional polymer materials. The spherical aluminum oxide is subjected to surface modification through an amphiphilic fluorosilicone heat conduction interface synergist; comprising the following raw materials in parts by weight: 100-200 parts of vinyl silicone oil, 1-6 parts of side hydrogen-containing silicone oil, 1-6 parts of end hydrogen-containing silicone oil, 0.2-2 parts of a platinum catalyst, 0.01-0.1 part of an inhibitor and 2500-6000 parts of amphiphilic fluorine-silicon heat-conducting interface synergistic spherical aluminum oxide powder. Through surface modification of spherical aluminum oxide, the prepared silicone rubber heat-conducting composite material realizes synergistic gain of heat conduction, insulation and flexibility, and can meet the requirements of multiple fields such as power battery pack heat dissipation gaskets, high-frequency circuit packaging and wearable equipment.

A temperature control method for a lithium ion power battery of a new energy automobile comprises the steps that a lumped parameter thermal model of the lithium ion power battery is established, parameter identification is conducted on the lumped parameter thermal model through an eagle and African vulture mixed optimization algorithm, and model parameters are obtained; constructing a maximum correlation entropy tracking extended Kalman filter, and inputting the model parameters into the maximum correlation entropy tracking extended Kalman filter; a multi-target grey wolf algorithm improved based on a deep reinforcement learning algorithm is introduced to optimize the state noise covariance and the observation noise covariance of the maximum correlation entropy strong tracking extended Kalman filter, so that the internal temperature of the battery is estimated; and establishing a model prediction controller based on the estimated internal temperature of the battery, improving the model prediction controller, and controlling the internal temperature of the battery through the improved model prediction controller. According to the invention, accurate online estimation and efficient control can be carried out on the internal temperature of the battery.

The invention discloses a lithium ion power battery thermal management online regulation and control method comprising the following steps: obtaining battery pack temperature data, and carrying out statistical analysis on the battery pack temperature data to obtain temperature fluctuation distribution characteristics; according to the temperature fluctuation distribution characteristics, cooling power output information is obtained through a feedback control method; obtaining a power allocation weight according to the cooling power output information, and carrying out prediction simulation on the temperature field according to the power allocation weight to obtain predicted temperature field distribution; the heat conductivity coefficient of a variable thermal interface material in the cooling system is adjusted according to the predicted temperature field distribution, a thermal diffusion path optimization scheme is obtained, a dynamic thermal channel network is constructed according to the thermal diffusion path optimization scheme, and a cooling strategy is obtained through matching according to the dynamic thermal channel network in combination with the running state of the battery; hierarchical control parameters are extracted from the cooling strategy, and an energy distribution optimization scheme is obtained; and according to the energy distribution optimization scheme, controlling a cooling system of the lithium ion power battery to realize thermal management regulation and control.

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 discloses a power battery health scoring method and system based on multi-feature fusion. The method comprises the following steps: preprocessing and classifying obtained power battery operation data; constructing a consistency evaluation index system; determining a consistency evaluation index weight based on a combination weighting method of the game theory; calculating a total evaluation score of the power battery by combining each consistency evaluation index and the weight and the threshold value thereof; obtaining each data set and carrying out feature extraction, screening and construction to obtain an optimal feature set of each power battery; the optimal feature set is divided into a training set, a verification set and an evaluation set to be used for training a BP neural network model and a LightGRM model respectively, then evaluation weights of the two models are calculated, and finally a battery health evaluation score is calculated. According to the hybrid modeling method based on the lightweight space-time diagram convolutional network, the precision and robustness of battery health state estimation are remarkably improved by fusing physical mechanism constraints and data driving advantages.

The invention relates to the technical field of extended-range electric vehicle control, and discloses an engine starting control method and system for an extended-range electric vehicle, and the method comprises the following steps: obtaining the operation information of the extended-range electric vehicle, the operation information comprises the actual temperature of an engine, the actual rotating speed of a range extender, the state of charge (SOC) of a power battery and the maximum dischargeable power; when the starting request mark position of the engine is set, the generator is controlled to enter a torque control mode, and the engine enters a starting mode; and based on the actual temperature of the engine and the actual rotating speed of the range extender, the pre-control torque of the generator is activated through the enabling flag bit. By accurately controlling the rotating speed and the torque of the generator, smooth starting of the engine is achieved, the requirement for starting the engine in various vehicle using scenes is met, meanwhile, the actual temperature of the engine, the SOC of a power battery and the like are considered, the target rotating speed of the started engine and the starting torque of the generator are flexibly controlled, and the starting efficiency is improved. The starting performance of the engine is improved.

The invention provides a power generation control method and device for an extended-range hybrid power loader. According to the scheme, the current working mode of the range extending type hybrid power loader is determined according to the real-time operation parameters and the driving operation information; the current working mode of the range extender is determined according to the current working mode of the range-extended hybrid power loader and the charge state of the power battery, and the current required power generation power of the range extender is determined based on the current working mode of the range extender, the required power of the range-extended hybrid power loader and the charge state of the power battery; according to the current required power generation power of the range extender, a preset range extender output electric power curve and a preset engine fuel efficiency characteristic curve, the target rotating speed of a generator and the target torque of the engine are determined, and according to the current working mode of the range-extended hybrid power loader, the target rotating speed of the generator and the target torque of the engine; and controlling the operation states of the engine and the generator. According to the scheme, the problem of low energy efficiency of an extended-range hybrid power loader is solved.

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 a thermal runaway monitoring method and related equipment for a new energy automobile power battery, and the method comprises the following steps: carrying out the internal temperature monitoring of the new energy automobile power battery, and obtaining the three-dimensional thermal field distribution data; thermal characteristics are analyzed, thermal runaway precursor characteristics are identified, the thermal runaway probability is calculated, and a probability distribution diagram is generated; in combination with the heat spreading prediction model, obtaining heat spreading path prediction data; and determining the out-of-control risk level of the battery according to the data, and generating a corresponding early warning signal, thereby solving the technical problems that the existing monitoring method mostly depends on threshold judgment of a single parameter, and complex thermal field change in the power battery of the new energy automobile is difficult to comprehensively capture.

The invention relates to a silicon-carbon composite material and a preparation method and application thereof.The preparation method comprises the steps that a porous graphite raw material is subjected to acid pickling and smashing to obtain pretreated porous graphite, then the pretreated porous graphite is soaked in a carbon precursor solution in a vacuum mode, carbonization treatment is conducted after centrifugal separation, pores of the porous graphite are filled with carbides, physical activation is conducted, and the silicon-carbon composite material is obtained; the preparation method comprises the following steps: forming pores in a carbide to form a graded pore structure to obtain a porous graphite composite material, then carrying out silicon deposition in a fluidized bed to obtain a uniform nano silicon layer, and then carrying out carbon coating treatment to obtain a silicon-carbon composite material; the graphite with relatively low cost is adopted as a matrix, high capacity and long cycle performance of the graphite-based silicon-carbon negative electrode material are realized through hierarchical pore structure design, carbonization-activation synergistic pore forming and interface deposition process optimization, and the graphite-based silicon-carbon negative electrode material can be applied to the fields of power batteries and high-energy-density energy storage, especially the field of lithium ion batteries and has wide application prospects. The expansion rate of the battery can be reduced, and the specific capacity, cycling stability and safety of the lithium ion battery can be improved.

The invention provides a range extending type tractor power system and a control method, and solves the problems that in the prior art, a tractor power system is low in energy conversion rate, a stepless speed change structure is complex to achieve, walking drive PTO drive cannot be coupled, and multiple controllers are difficult to cooperate. The range extender is electrically connected with the power battery and can provide electric energy for the power battery; the motor assembly comprises a driving motor and a PTO motor; the control assembly comprises a vehicle control unit, a range extender controller and an all-in-one controller, the range extender controller and the all-in-one controller are connected with the vehicle control unit, the range extender controller is electrically connected with the range extender, the all-in-one controller is electrically connected with the power battery, the range extender and the motor assembly, and the vehicle control unit controls the range extender controller and the all-in-one controller according to specific working conditions. The range extender is controlled by the range extender controller to supply power to the power battery or / and the all-in-one controller, and then the all-in-one controller is used for carrying out power distribution on the driving motor and the PTO motor.

The invention relates to the technical field of power batteries, in particular to a power battery pack health state detection method and system. The power battery pack health state detection method comprises the following steps: acquiring open-circuit voltage change data and terminal voltage fluctuation data of a battery under micro current pulse excitation, recording a change curve of voltage along with time through high-precision time sequence acquisition equipment, and obtaining an initial data set of voltage dynamic response; acquiring magnetic field intensity distribution and magnetic field gradient change data of the surface of the battery in a weak alternating magnetic field, and acquiring spatial magnetic field information through a magnetic sensor array to obtain a three-dimensional distribution diagram reflecting magnetic field nonuniformity; through a three-dimensional distribution diagram of magnetic field intensity distribution and magnetic field gradient change, a defect detection algorithm is adopted to analyze a mapping relation between magnetic field non-uniformity and internal micro cracks, and correlation distribution of defect positions and internal resistance growth rates is determined.

The invention provides a gear switching method, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that in the process that the vehicle runs based on a direct drive mode, if it is determined that the vehicle needs to switch gears, the whole vehicle driving power corresponding to the direct drive mode and the discharging power of a power battery are determined; based on the whole vehicle driving power, the discharging power and the gear change state of the vehicle, a target torque reduction gradient is determined; based on the target torque reduction gradient, the actual torque of the engine is controlled to be reduced to the target torque; and under the condition that the actual torque of the engine is equal to the target torque, the clutch is controlled to be in an open state, so that the vehicle is shifted under the condition that the clutch is in the open state. The method can ensure that the power is not interrupted in the vehicle gear shifting process, the vehicle stability is improved, and the user driving experience is improved.

The invention discloses a new energy automobile battery health prediction method and system, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the real-time collection of the operation state parameters of a power battery pack corresponding to a new energy automobile, obtaining a battery state data set corresponding to the new energy automobile, carrying out the adaptive noise suppression processing of the battery state data set, and obtaining a new energy automobile battery health prediction result. Generating a de-noised battery state feature map, performing feature extraction on the de-noised battery state feature map to obtain a multi-dimensional coupling feature set, constructing a dynamic weight distribution matrix, performing feature fusion on the multi-dimensional coupling feature set, determining a battery health state comprehensive index, and triggering a grading early warning mechanism based on the battery health state comprehensive index; and constructing a battery health prediction model, carrying out long-term modeling on the battery health state comprehensive index, predicting a battery health state evolution trend in a future charging and discharging period, generating a balance maintenance strategy, and pushing the balance maintenance strategy to the vehicle-mounted terminal and the cloud management platform. The method has the effect of improving the accuracy of battery health prediction.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.

The invention discloses an electric vehicle power battery health degree assessment method and system based on cloud data analysis, relates to the technical field of battery health degree assessment, and enables battery health assessment to be more scientific by performing feature extraction on a health data set to obtain a residual capacity change rate delta Qre, a battery polarization degree Pba and a charging and discharging lag factor CD. In combination with the health factor HI, the system can automatically judge the health state of the battery, distinguish normal, decline, abnormal and other states, and identify possible fault modes, such as capacity attenuation, electrode aging, short circuit risk, thermal runaway trend and the like. Through cloud data analysis, the system can gather battery operation data of different vehicles under different working conditions, a larger-scale data set is formed, and higher generalization ability is provided for model training and health prediction. An operator can remotely monitor the battery health states of all vehicles through the cloud management platform, the motorcade scheduling is optimized, and the utilization rate of battery assets is improved.

The invention provides a battery health state evaluation method, device and equipment and a medium, and the method comprises the steps: collecting multi-source parameters of a power battery in a charge-discharge cycle process, the multi-source parameters comprising voltage, current, temperature and electrochemical impedance spectroscopy; performing feature engineering processing on the multi-source parameters based on a battery aging mechanism, and extracting key feature parameters; the key characteristic parameters are input into a multi-physics field coupling model to simulate thermoelectric coupling behaviors of the battery under a dynamic load, lithium ion concentration distribution and temperature distribution in the battery are obtained, and the multi-physics field coupling model is used for representing a coupling relation between an electrochemical field and a thermal field of the battery; and fusing the key characteristic parameters with data output by the multi-physics field coupling model, and determining a health state evaluation result of the power battery. According to the invention, the accuracy of power battery health state evaluation is improved.

The invention discloses a power battery active equalization control method, which comprises the steps of extracting characteristic information of a battery pack, generating quantitative state change trend description, and obtaining potential unbalanced risk point distribution; analyzing operation constraint conditions related to load changes, generating trigger weights of unbalanced risks, screening high-priority risk points, generating a preliminary equalization path planning scheme, calculating equalization operation current intensity of each single battery in different time periods in combination with battery pack state data collected in real time, and generating an equalization operation schedule; obtaining feedback state data in real-time execution, and comparing the feedback state data with the predicted risk point to obtain a final control decision instruction; and for the final control decision instruction, acquiring executed battery pack state update data, analyzing the consistency of the battery pack state update data and a target equalization state, judging whether an equalization path or time arrangement needs to be finely adjusted, and generating a continuously optimized equalization control scheme. Accurate prediction and balance control of the state of the battery pack are realized.

The invention relates to the technical field of preparation of a ceramic silica gel composite belt for a power battery of a new energy automobile, in particular to a super-high-temperature-resistant and impact-resistant ceramic silica gel composite belt for an automobile and a preparation method of the super-high-temperature-resistant and impact-resistant ceramic silica gel composite belt. The phase change toughening effect of the core-shell structure filler is generally utilized to generate pressure stress, inhibit crack propagation and improve fracture toughness through volume change during phase change, meanwhile, the core-shell structure filler is subjected to thermal expansion coefficient gradient structure design, interface stress in the core-shell structure filler at high temperature is relieved, thermal stress concentration is avoided, thermal shock resistance is improved, and the service life of the core-shell structure filler is prolonged. And the ceramic layer is prevented from cracking, so that the ceramic layer has good high-temperature resistance and flame retardance, flame spreading can be effectively prevented, and thermal runaway of the power battery is prevented.

The invention relates to the technical field of power batteries, and discloses a plastic uptake support CCS integrated busbar assembly, which comprises a plastic uptake support body, busbar bodies, a wiring harness assembly and a collection nickel sheet, the plastic uptake support body is provided with a plurality of positioning grooves and riveting columns, the positioning grooves are fixedly connected in series with the plurality of busbar bodies through the riveting columns, and the collection nickel sheet is connected with the wiring harness assembly. The busbar bodies comprise a series busbar, a bridging busbar, a positive leading-out electrode busbar and a negative leading-out electrode busbar, the blister support body is provided with a wire harness buckle, the wire harness buckle is located between the two groups of busbar bodies, a wire is fixed on the wire harness buckle, the acquisition nickel sheet is welded on the busbar bodies through laser, and the acquisition nickel sheet is upwarped in a non-breaking manner after being welded. According to the plastic uptake support CCS integrated busbar assembly, the wiring harness buckle can simply, conveniently and quickly fix the wiring harness and prevent the wiring harness from being separated from the plastic uptake support, a traditional mode of fixing the wiring harness through hot pressing or hot riveting is replaced, and the product cost is greatly reduced.

The invention discloses a battery health state estimation method and device, equipment, a storage medium and a product, and relates to the technical field of battery health management, and the method comprises the steps: carrying out the preprocessing of a collected retired lithium ion battery sample, and obtaining pulse voltage response data under different battery charging states; inputting the pulse voltage response data into a physical constraint diffusion model to generate pulse voltage response characteristic data; generating a battery health state estimation model according to the pulse voltage response characteristic data and the regression model; and performing health state estimation on the to-be-evaluated battery according to the estimation model. According to the method, the pulse voltage response data is extracted from the multi-dimensional retired lithium ion battery sample, then the battery health state estimation model is generated through the physical constraint diffusion model and the regression model, and then the battery health state is estimated according to the estimation model. According to the method, the SOH of the retired power batteries in different dimensions can be quickly and accurately estimated, and the energy consumption in the estimation process is low.

The invention discloses a real vehicle power battery capacity attenuation trajectory prediction method based on a de-noising diffusion model. According to the method, a diffusion generation model is applied to the field of battery capacity prediction for the first time, multi-modal features are constructed for charging and discharging data under a real vehicle working condition, feature expressions are extracted through Transform and CNN, and the features are integrated into high-dimensional embedded vectors. And then, a DDPM model based on a ContextUnet architecture is utilized to learn a dynamic change rule of capacity attenuation in a noise environment through a noise diffusion and reverse denoising process under the guidance of multi-modal features, so that high-precision capacity prediction is realized. The training process is further combined with an implicit denoising mechanism of DDIM, and the reasoning speed is increased while the prediction quality is improved. Finally, a capacity attenuation track confidence interval is obtained by performing multiple times of prediction sampling calculation on a predicted capacity attenuation result, and the capacity attenuation track confidence interval can be used for deducing key indexes such as the state of health (SOH) and the remaining service life (RUL) of the battery. The method has the characteristics of high prediction precision and strong adaptability, obviously enhances the intelligent level of the battery management system, and has important practical value for health assessment and life management of the new energy automobile battery.

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.