15623 results about "Power battery" patented technology

An unsupervised learning-based power battery anomaly detection system, comprising: a battery parameter association reasoning module, configured to design a dynamic learnable graph neural network algorithm, and explore a potential association of battery parameters; a time sequence feature dependency analysis module, configured to model long-term and short-term time sequence dependency relationships on the basis of a temporal learning network; a coupling temporal-spatial neural architecture module, configured to perform coupling representation on the temporal learning network and a spatial learning network to integrate spatial-temporal association features; and a battery anomaly inference and evaluation module, configured to realize battery state prediction and evaluation by designing an adaptive threshold selection strategy and an experimental stage evaluation mechanism. The present application determines whether a power battery is in a normal operating state by analyzing a change mode of the key parameters of the power battery in a production process, and promptly stops the production process of a battery in an abnormal state, thereby preventing occurrence of accidents, and reducing resource loss in the production process.

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

The invention relates to the technical field of power battery packs, and discloses a power battery pack state evaluation method and system, and the method comprises the steps: collecting an operation characteristic data set of a power battery pack under various working conditions, and constructing a hybrid evaluation model; performing parameter configuration on the double-extended Kalman filter to obtain an improved filter; acquiring real-time voltage, current and temperature data, and inputting the real-time voltage, current and temperature data into the improved filter for state calculation to obtain a charge state value and an internal parameter value; inputting the charge state value and the internal parameter value to perform multi-branch adversarial neural network analysis to obtain a fault mode type and a fault severity; according to the method, the multi-factor health index is calculated, particle swarm optimization fitting is carried out on the degradation curve of the power battery pack, the residual service life prediction value is obtained, more accurate residual service life prediction is further achieved, the precision in a wide temperature range is remarkably improved, and the difference between monomers in the power battery pack can be effectively processed.

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 relates to the technical field of power batteries, in particular to a battery aging state detection system, method and device. The method comprises the following steps: obtaining battery pack structure design data of the unmanned aerial vehicle; according to the battery pack structure design data, carrying out single battery health monitoring, and generating a multi-parameter feature vector; establishing a data mapping relation of the battery monomer indexes through the multi-parameter feature vectors, thereby obtaining initial health state data of the battery pack; collecting charging and discharging sample data of the unmanned aerial vehicle under different flight conditions; a dynamic current excitation sequence is constructed based on charging and discharging sample data, and current load changes in different flight stages are simulated, so that a multi-working-condition current disturbance sequence is generated. By simulating the load change of the unmanned aerial vehicle in different flight stages and combining the dynamic response capture capability of the high-frequency current sensor, the electrochemical characteristics of the battery can be extracted in real time, and the aging state of the battery under the complex working condition can be accurately identified.

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 battery management, and discloses an equalizing charging method, device and equipment for a power battery pack. The method comprises the following steps: performing parameter acquisition on a power battery pack, measuring terminal voltage values, temperature values and charge and discharge current values of each single battery, and calculating a state-of-charge value and an internal resistance change rate of each single battery; inputting into a multi-head self-attention network for feature extraction, and generating a state correlation feature matrix; constructing an Actor network and a Critic network based on the state correlation characteristic matrix, and outputting an equalization current value, a constant-current or constant-voltage equalization mode and equalization time through the Actor network; according to the equalization current value, the equalization mode and the equalization time, the DC-DC converter in the main equalization loop is controlled to generate constant current output, or the Buck-Boost converter in the secondary equalization loop is controlled to generate constant voltage output, the equalization efficiency of the power battery pack is guaranteed, meanwhile, energy loss is reduced, and temperature self-adaptive control over the equalization circuit is achieved.

The invention discloses a thermal management control system and method for a power battery pack, and belongs to the technical field of electric automobiles, and the system comprises the following modules: a multi-parameter acquisition module which is used for acquiring parameter data of the battery pack in real time; the data preprocessing and fusion module is used for generating a high-quality and uniform-format data set; the dynamic prediction modeling module is used for realizing accurate temperature trend prediction; the intelligent decision module is used for generating an optimal heat management strategy; the thermal management execution module is used for realizing accurate temperature regulation and thermal management; the system monitoring and diagnosis module is used for ensuring safe operation of the system; a man-machine interaction module; the intelligent thermal management system is used for realizing man-machine collaborative intelligent thermal management. According to the thermal management control system and method for the power battery pack, the particle swarm optimization algorithm is utilized, the power battery thermal management system of a thermal management strategy is dynamically adjusted, more accurate temperature prediction and control are achieved, energy consumption is optimized, and the service life of the battery is prolonged.

The invention relates to the technical field of BMS control, and discloses a power battery BMS control temperature equalization method and device, and the method comprises the steps: carrying out the collection and thermodynamic characteristic analysis of the temperature data and heat loss power of N single batteries in a power battery pack, and obtaining the dynamic characteristic parameters of the temperature of the battery pack and a temperature field distribution prediction track; performing estimation compensation on thermal coupling interference and temperature measurement noise among the battery monomers to obtain actual temperature data of each battery monomer; performing battery pack temperature mean value calculation on the actual temperature data of each battery monomer, generating a temperature equalization target track, and performing multi-target optimization solution to obtain a temperature equalization control quantity of each battery monomer; and calculating the target heat dissipation power of each battery monomer, and outputting a regulation and control instruction of a radiator of each battery monomer. According to the invention, through real-time adjustment of the heat dissipation power correction coefficient, reasonable distribution of heat dissipation resources is realized, and the heat dissipation efficiency and the temperature equalization effect are improved.

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

The invention relates to the technical field of 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.

The invention relates to the technical field of forklift power battery pack management, and discloses a forklift power battery pack management method, device and equipment and a storage medium and system, and the forklift power battery pack management system comprises a dynamic load prediction module, a multi-mode heat management module and a self-adaptive current control module. A dynamic load prediction model optimized through edge calculation is deeply coupled with a multi-mode thermal management system to form a real-time cooperative closed loop, and the problem of lagging of load change and battery temperature rise response is solved. And through closed-loop optimization of data streams among the modules, adaptive adjustment under multiple working conditions is realized, and the service life of the battery and the energy efficiency are remarkably improved under the hardware condition with limited resources. The mechanism of experimental calibration of cooling coefficient and real-time load coupling adjustment effectively balances cooling energy consumption and temperature control requirements, and significantly reduces system energy consumption. And meanwhile, the intelligent adaptability of the system is enhanced by long-term data recording and optimization iteration supported by the cloud platform, so that the system has the capability of continuously optimizing the performance in a complex operation environment.

The invention discloses a preparation method of a high-compaction-density lithium iron phosphate material, through grading of large and small particle lithium iron phosphate, optimization of a sintering process and reasonable selection of a carbon source and a titanium dopant, the compaction density and the electrochemical performance of the material are remarkably improved, and the preparation method specifically comprises the following steps: mixing iron phosphate, lithium carbonate, the carbon source and auxiliary materials; the preparation method comprises the following steps: carrying out coarse grinding and fine grinding, respectively preparing large-particle lithium iron phosphate and small-particle lithium iron phosphate (a material A and a material B) by adopting spray drying and high-temperature calcination processes, mixing the material A and the material B according to a ratio, adding auxiliary materials, grinding, further carrying out spray drying, calcining and crushing to obtain a final lithium iron phosphate product, and the maximum compaction density of the prepared material can reach 2.712 g / cm < 3 >. According to the present invention, the maximum 1C discharge specific capacity can achieve 140.5 mAh / g, the maximum 1C 3.2 V discharge platform retention rate is 91.4%, and the prepared lithium ion battery negative electrode material has characteristics of excellent conductivity, high discharge capacity and good cycle stability, and is suitable for power batteries and energy storage batteries.

A method for online diagnosis of power battery voltage fault based on entropy algorithm, in which voltage time-series values of individual cells in a battery pack of a to-be-diagnosed vehicle are obtained to construct a first voltage data matrix; the first voltage data matrix is intercepted by a sliding time window to form a second voltage data matrix; voltage values of the second voltage data matrix are subjected to data exclusion and reconstruction to form a third voltage data matrix; Shannon entropy values of cells are calculated; each Shannon entropy value is transformed into an abnormal fluctuation evaluation coefficient of individual cells; whether there is a abnormal cell is determined; if yes, position and occurrence time of the abnormal cell are determined, and an abnormality degree is evaluated; otherwise, the time window is moved downward, and the above steps are repeated for the next iteration.

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 provides an energy consumption prediction and anomaly detection method and system based on digital twinning, and the method comprises the steps: collecting physical parameters in real time through a sensor, and the physical parameters comprise the cell temperature, the total current of a battery pack, the total voltage of the battery pack, the gradient, the vehicle speed and the mass; collecting battery state parameters by using a battery management system, wherein the battery state parameters comprise real-time energy consumption; the collected physical parameters are input into a Simulink and Cruise combined model for simulation, and model output prediction energy consumption is obtained; wherein the Simulink is used for establishing an equivalent circuit model of a power battery, and the control system, the sensor simulation module and the Cruise simulation model are used for establishing a whole-vehicle simulation model of the pure electric vehicle; comparing the model output energy consumption with the real-time energy consumption, and obtaining an energy consumption deviation value; whether the energy consumption deviation value exceeds a preset range or not is judged, and if yes, energy consumption is judged to be abnormal; and if not, evaluating the energy consumption of the whole vehicle so as to solve the technical problem that the actual energy consumption analysis, energy management and endurance mileage evaluation of the whole vehicle are difficult.

The invention discloses a power battery fault early warning method, solves the problem of difficulty in timely early warning of faults caused by consistency difference of a power battery pack, and belongs to the field of power battery management. Comprising the following steps: preprocessing collected operation data to obtain target data; determining the length of a sliding window based on the evaluation function, segmenting the target data according to a time sequence by using the length of the sliding window, and extracting features for representing battery consistency and fluctuation anomaly characteristics in each sliding window to form a feature matrix; dynamically adjusting a fault detection threshold according to the deviation between the median and the absolute median of the target data in the sliding window; calculating a fluctuation anomaly score through a local outlier factor algorithm; if the fluctuation abnormity score exceeds a fault detection threshold value, positioning a target single battery corresponding to the fluctuation abnormity score, determining a fault type according to a characteristic matrix of the target single battery, and outputting graded early warning prompt information; according to the invention, high-precision detection and early warning of power battery faults are realized.

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

The invention discloses a power battery fault detection method based on a bidirectional Mama architecture, and the method comprises the steps: firstly collecting the voltage, current, temperature and internal resistance of a power battery as original data, and carrying out the preprocessing of the original data, and generating a data set; secondly, the bidirectional Mama feature extraction network and the weighted reconstruction network are connected in series to construct a bidirectional Mama network model, bidirectional representation modeling of the time dimension and the feature dimension is carried out on the power battery data in the data set, and reconstruction output is carried out through the weighted reconstruction network. And finally, taking the time sequence data of the power battery under the normal working condition as the input of the bidirectional Mamba network for training. And according to a reconstruction error between the output data and the input data of the weighted reconstruction network, a fault threshold is set, and fault detection is realized. The key information in the time sequence data of the power battery is accurately extracted, potential faults in the power battery are efficiently and comprehensively detected, and the safety of the power battery is improved.

The invention discloses a hybrid power combine harvester and a control method and a controller thereof, and relates to the technical field of agricultural machinery. The harvester comprises a hybrid power system which comprises a power battery and a range extender composed of an engine and a generator; a working system including a plurality of working members and a plurality of working motors that enable each working member to be driven by the motor; the walking system comprises walking wheels and walking motors used for driving the corresponding walking wheels; the control system comprises a whole vehicle controller and a plurality of motor controllers corresponding to the operation motors, the walking motors and the generators respectively. Wherein the hybrid power system responds to a power control strategy of the control system and provides power for corresponding motors in the operation system and the walking system so as to drive the harvester to walk and operate. The full-electric-drive hybrid power scheme of the harvester is achieved, and the efficiency problem caused by multi-stage mechanical transmission of a traditional harvester is avoided.

A thermal management system adaptive to super fast charging and a vehicle. The thermal management system comprises a refrigerant circulation, a motor coolant circulation and a battery coolant circulation. The refrigerant circulation comprises a compressor (1), an outside heat exchanger (4), a motor heat exchanger (10), and a battery heat exchanger (12). When a power battery is fast charged, a gaseous refrigerant flows out from an outlet of the compressor (1), passes through the outside heat exchanger (4) for heat dissipation, and then passes through the motor heat exchanger (10) for heat dissipation, to dissipate heat to the motor coolant circulation; and when passing through the battery heat exchanger (12), the refrigerant absorbs the heat of the battery coolant circulation, so as to cool the power battery. Therefore, by optimizing existing thermal management system architectures, the refrigerant circulation is associated with the coolant circulation, and radiators in the circulations are fully utilized, so that the heat dissipation efficiency is effectively improved when the power battery is fast charged.

The invention discloses a new energy automobile lithium battery thermal runaway monitoring system and method. The system comprises a vehicle-mounted lithium battery, a multi-source data acquisition module, a multi-source data fusion module, a hidden Markov model monitoring module, a state identification module, a data analysis processing module, an early warning module, an emergency processing module, a wireless communication module, a cloud platform and a battery management system. The multi-source data acquisition module is responsible for acquiring multi-dimensional data such as voltage, temperature and internal resistance of a lithium battery, and the data fusion module processes the data and then transmits the data to the hidden Markov model monitoring module for state monitoring and prediction. According to the system, a high-order hidden Markov model and a hidden semi-Markov model are adopted, state retention time modeling and a self-adaptive online learning mechanism are combined, and conversion of a battery from a normal state to an abnormal state and a thermal runaway state can be accurately recognized. The system remarkably improves the safety and stability of the lithium battery of the new energy automobile, and is suitable for power battery management of the new energy automobile.

One example provides a chassis for an electric snowmobile including a battery pack. The battery pack includes a battery pack housing defining an enclosure for housing a number of battery modules for powering an electric motor of the electric snowmobile, the battery pack housing having a length extending in a longitudinal direction of the snowmobile, the battery pack housing including a bottom surface. A pair of opposing side panels extends downwardly from and along at least a portion of the length of the battery pack housing, the opposing panels and at least portions of the bottom surface of the battery pack housing together forming a rear structure extending in the longitudinal direction of the electric snowmobile.

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 belongs to the technical field of soft package battery modules, and particularly relates to a soft package power battery module intelligent processing production line and a processing production method.The processing production line comprises a battery cell processing section, a module stacking section and a module welding section; the battery cell processing section comprises a cutting and bending mechanism, a dog ear folding mechanism and a foam pasting mechanism which are sequentially arranged in the processing direction, and the module stacking section comprises a battery module stacking device and a battery module extruding and bundling device which are sequentially arranged in the processing direction; the module welding section comprises a busbar mounting station, a tab bending and rolling mechanism and a tab welding mechanism which are sequentially arranged in the machining direction. According to the invention, automation of the whole-line production process of the soft package battery module is realized.

The invention provides a thermal-mechanical coupling topological optimization method for a thin-wall non-uniform filling structure, and the method comprises the steps: 1, building a finite element model according to design requirements, and determining a non-design domain and a design domain; 2, initializing a design variable, and then obtaining a local volume fraction polymerization field through one-step filtering; 3, describing the distribution of a material in a design domain; 4, thermal coupling finite element calculation is carried out, and a structural displacement field U is solved; step 5, performing corresponding sensitivity analysis; 6, submitting the sensitivity to a moving asymptote algorithm solver to update an overall topology field and a dot matrix density field; and step 7, checking whether an iteration stop condition is met or not. According to the invention, the lightweight design of the power battery module bracket structure facing the thermal elasticity problem is realized; according to the power battery module support with the thin-wall non-uniform filling structure characteristic, the structural rigidity is improved, and meanwhile, the good heat dissipation performance is shown.