New energy automobile battery device and control method thereof

Abstract

The invention relates to the technical field of battery management, in particular to a new energy automobile battery device and a control method thereof. The method comprises the following steps: obtaining new energy automobile structure data, and carrying out battery shell structure feature extraction according to the new energy automobile structure data so as to obtain battery shell structure data; performing battery heat dissipation structure analysis according to the battery shell structure data to obtain new energy automobile battery shell heat dissipation data; performing battery pack structure feature extraction according to the new energy automobile structure data so as to obtain battery pack structure data; performing battery voltage equalization analysis according to the battery pack structure data to obtain battery voltage equalization data; performing battery short-circuit simulation according to the heat dissipation data of the battery shell of the new energy automobile so as to obtain new energy battery short-circuit data; according to the invention, a battery health control early warning task is realized based on a battery management technology.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to a new energy vehicle battery device and a control method thereof. Background Art

[0002] As the world pays more and more attention to environmental protection and energy conservation, new energy vehicles, as an emerging clean energy means of transportation, have received widespread attention. One of the core components of new energy vehicles is its battery system, especially lithium-ion batteries, which are widely used for their high energy density and long service life. However, with the rapid growth of the new energy vehicle market, the management and control technology of battery systems is also facing severe challenges. Traditional battery management systems (BMS) mainly rely on static battery monitoring and simple balancing strategies, which show certain limitations under the dynamic load conditions of battery packs. In the case of battery overheating or short circuit, the overheat protection measures of traditional systems often cannot respond in time, resulting in battery performance degradation and even safety hazards. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a new energy vehicle battery device and a control method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for a new energy vehicle battery device comprises the following steps:

[0005] Step S1: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data; performing battery heat dissipation structure analysis according to the battery shell structural data, thereby obtaining new energy vehicle battery shell heat dissipation data;

[0006] Step S2: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data; performing battery voltage balancing analysis according to the battery pack structure data, thereby obtaining battery voltage balancing data;

[0007] Step S3: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data; performing a battery heat distribution dynamic adjustment according to the new energy battery short circuit data and the battery voltage balancing data, thereby obtaining the battery heat distribution dynamic adjustment data;

[0008] Step S4: acquiring new energy vehicle environmental sensor data, and performing temperature effect influencing factor analysis on new energy battery short circuit data according to the new energy vehicle environmental sensor data, thereby obtaining a temperature effect influencing factor set;

[0009] Step S5: Perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data, and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

[0010] By extracting the structural feature data of the battery shell, the present invention can fully understand the design and material characteristics of the battery shell, which provides a basis for subsequent heat dissipation analysis. Battery heat dissipation structure analysis can identify potential problems in heat dissipation design, such as unreasonable arrangement of heat dissipation holes, insufficient design of heat dissipation fins, etc., thereby optimizing the heat dissipation design, improving the heat dissipation efficiency of the battery system, and reducing safety hazards caused by overheating. Good heat dissipation management can effectively control the battery operating temperature, delay battery aging, and improve the service life and reliability of the battery. Battery pack structural feature extraction and voltage balance analysis help ensure that the voltage of each battery cell in the battery pack remains consistent, prevent voltage differences from causing performance degradation or safety hazards. Through voltage balance analysis, more efficient energy utilization can be achieved, energy loss in the battery pack can be reduced, and the endurance of the entire vehicle can be improved. Voltage balance data can help adjust the battery pack's charging and discharging strategy, improve system stability, and reduce the risk of battery pack failure due to voltage imbalance. Battery short-circuit simulation can identify the heat changes and potential risks of the battery system under short-circuit conditions in advance, so as to formulate more effective response strategies. Dynamically adjusting the battery heat distribution can adjust the thermal management strategy in real time according to the actual battery status, optimize the battery heat distribution, prevent local overheating, and improve the safety and performance of the battery. Through simulation and adjustment, the overheating of the battery under short-circuit conditions can be effectively prevented and alleviated, reducing the risk of fire or explosion and improving overall safety. The analysis of environmental sensor data combined with short-circuit data can accurately understand the impact of different environmental factors (such as temperature, humidity, etc.) on battery performance. Through the analysis of temperature effect influencing factors, a more accurate battery health warning model can be established to detect potential problems early. Understanding the temperature effect can help improve battery design, improve the adaptability of batteries under different environmental conditions, and enhance their reliability. Battery health warning based on temperature effect and heat distribution data can monitor the status of the battery in real time and detect potential health problems in time. Through active warning, maintenance or adjustment can be carried out before the problem worsens, reducing the risk of battery failure, improving the safety and reliability of the vehicle, and uploading the warning data to the new energy vehicle management platform to achieve centralized monitoring and intelligent management, and improve the management efficiency and response speed of the battery system.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data;

[0013] Step S12: performing a battery heat dissipation path structure analysis according to the battery housing structure data, thereby obtaining the battery heat dissipation path structure data;

[0014] Step S13: performing a battery shell material structure analysis according to the battery shell structure data, thereby obtaining the battery shell material structure data;

[0015] Step S14: Merging the new energy battery structure according to the battery heat dissipation path structure data and the battery shell material structure data, thereby obtaining the new energy vehicle battery shell heat dissipation data.

[0016] The present invention obtains the structural data of new energy vehicles and extracts the structural features of the battery shell, so as to understand the design parameters of the battery shell and its relationship with the overall vehicle structure in detail, and provides the basic data of the battery shell design, which helps to optimize the structure of the battery shell in the design stage to adapt to different working environments and operating conditions, so that the battery shell can be customized according to the specific vehicle model and use requirements, meet the heat dissipation and structural requirements of different vehicles, and improve the performance of the whole vehicle. The heat dissipation path structure analysis helps to identify the heat dissipation bottlenecks and potential problems inside the battery shell, ensure that the heat can be effectively dissipated from the battery system, and by optimizing the heat dissipation path design, the heat dissipation efficiency of the battery can be improved, the risk of overheating can be reduced, and the use safety and stability of the battery can be improved. The basic data for optimizing the heat dissipation path is provided, which helps to formulate a more effective thermal management strategy and enhance the heat dissipation performance of the battery under high load. The material structure analysis can understand the performance, thickness and processing method of the materials used in the battery shell in detail, so as to evaluate its performance in practical applications, provide a basis for optimizing material selection and improvement suggestions, help to select more suitable materials in the battery shell design, so as to improve thermal management and safety performance, and through in-depth analysis of the material structure, it can identify the defects or deficiencies of the material, solve potential problems in advance, and improve the overall reliability and durability of the battery shell. By integrating the battery heat dissipation path structure data and the battery shell material structure data, the overall heat dissipation design of the battery system can be optimized and the thermal management performance can be improved. The combined data helps to establish a more accurate battery heat dissipation model, thereby effectively improving the heat dissipation efficiency of the battery in practical applications and reducing performance degradation caused by overheating. Effective heat dissipation data analysis can help prevent safety hazards caused by battery overheating and ensure the safe operation of new energy vehicles under various working conditions.

[0017] Optionally, step S12 is specifically:

[0018] Step S121: extracting the characteristics of the heat dissipation holes of the battery and the characteristics of the heat dissipation fins of the battery according to the battery shell structure data, thereby obtaining the data of the heat dissipation holes of the battery and the data of the heat dissipation fins of the battery;

[0019] Step S122: constructing a battery thermal flow model according to the battery heat dissipation hole data, thereby obtaining a battery thermal flow model;

[0020] Step S123: performing heat dissipation hole blockage analysis according to the battery thermal flow model, thereby obtaining heat dissipation hole blockage data;

[0021] Step S124: Calculating the convection heat transfer coefficient according to the battery cooling fin data, thereby obtaining the cooling fin convection heat transfer coefficient data;

[0022] Step S125: performing battery heat dissipation path analysis according to the heat dissipation hole blockage data and the heat dissipation fin convection heat transfer coefficient data, thereby obtaining battery heat dissipation path structure data.

[0023] The present invention can understand the heat dissipation design of the battery housing in detail by extracting the characteristic data of the heat dissipation holes and heat dissipation fins. This helps to identify the key components and their design defects in the heat dissipation system, provides detailed data on the heat dissipation holes and heat dissipation fins, and provides a basis for improving the heat dissipation design. This can help optimize the heat dissipation performance, reduce the risk of battery overheating, ensure that the design and function of the heat dissipation holes and heat dissipation fins meet the requirements, and help improve the overall heat dissipation efficiency of the battery system, thereby improving the safety and life of the battery. Constructing a battery thermal flow model helps to simulate the heat flow inside the battery and fully understand how heat is transferred in the battery system. Through the thermal flow model, the area where heat accumulates can be identified, which is of great significance for improving the heat dissipation design and optimizing the battery structure. The model can predict the heat dissipation effect of the battery under different working conditions, thereby providing a scientific basis for optimizing the heat dissipation system and ensuring the safe and stable operation of the battery under various working conditions. Through the heat dissipation hole blockage analysis, the blockage problem of the heat dissipation hole can be found. This helps to identify and solve the problem areas that cause poor heat dissipation, provides specific data for heat dissipation hole blockage, and provides a basis for subsequent improvements to the heat dissipation system, ensuring that the heat dissipation system can maintain effective heat dissipation performance. Timely discovery and treatment of heat dissipation hole blockage problems can reduce the risk of overheating caused by poor heat dissipation, thereby improving the safety and reliability of the battery. By calculating the convective heat transfer coefficient of the heat dissipation fin, the performance of the heat dissipation fin in the heat transfer process can be accurately evaluated, thereby optimizing its design. Providing convective heat transfer coefficient data helps to optimize the design of the heat dissipation fin and improve its thermal management effect in the battery system. Accurate convective heat transfer coefficient calculation can help improve the thermal conduction efficiency of the heat dissipation fin, thereby improving the overall heat dissipation effect and enhancing the safety and performance of the battery system. By analyzing the heat dissipation path based on the heat dissipation hole blockage data and the convective heat transfer coefficient data of the heat dissipation fin, the heat dissipation path design of the battery can be optimized to ensure that heat can be effectively dissipated from the battery system. Analyzing the heat dissipation path structure helps to improve the thermal management efficiency and ensure that the heat dissipation performance of the battery system in actual applications meets the design requirements. By optimizing the heat dissipation path structure, the battery overheating problem can be effectively prevented, the battery safety and service life can be improved, and potential safety hazards caused by heat dissipation problems can be reduced.

[0024] Optionally, step S13 is specifically:

[0025] Step S131: performing statistics on the types of battery shell materials according to the battery shell structure data, thereby obtaining battery shell metal material data and battery shell composite material data;

[0026] Step S132: Calculating the thermal conductivity of the metal material data of the battery shell to obtain the thermal conductivity data of the material;

[0027] Step S133: calculating the material heat capacity of the battery shell composite material data, thereby obtaining the material heat capacity data;

[0028] Step S134: performing battery charge and discharge simulation according to the material thermal conductivity data and the material heat capacity data, thereby obtaining battery charge and discharge simulation data;

[0029] Step S135: Perform finite element structural analysis of the battery casing according to the battery charge and discharge simulation data, thereby obtaining the battery casing material structure data.

[0030] The present invention helps to evaluate the performance of different types of battery shell materials (such as metal materials and composite materials) in terms of heat dissipation and structural strength by counting different types of battery shell materials (such as metal materials and composite materials). By obtaining the data of metal materials and composite materials of the battery shell, the composition of the battery shell can be fully understood, and accurate data support can be provided for subsequent material performance analysis and optimization. Understanding the material type and characteristics can provide a reference for the optimal design of the battery shell, ensuring that the materials used meet the heat dissipation requirements and safety standards. Calculating the thermal conductivity of metal materials helps to understand their performance in battery heat dissipation. Materials with high thermal conductivity can conduct heat more effectively and reduce the risk of battery overheating. By obtaining the thermal conductivity data of metal materials, the battery shell design can be optimized, and suitable materials can be selected to improve the heat dissipation effect, thereby improving the safety and stability of the battery system, ensuring that the thermal conductivity of the material meets the design requirements, and effectively preventing the battery from overheating under high load conditions. Calculating the thermal capacity data of composite materials can understand the characteristics of the material in terms of heat storage and release. High heat capacity materials can effectively absorb and slowly release heat, which helps to stabilize the temperature of the battery. By understanding the heat capacity characteristics of the material, the thermal management design of the battery shell can be optimized to improve the overall heat dissipation effect and battery performance, ensuring that the material heat capacity data meets the design requirements, which helps to reduce the temperature fluctuation of the battery during operation, thereby improving the safety and reliability of the battery system. Through charge and discharge simulation, the distribution and flow of heat in the actual use of the battery can be evaluated. This helps to identify the thermal management needs of the battery during the charge and discharge process. According to the simulation data, the heat dissipation strategy of the battery can be optimized and the design can be adjusted to ensure that the battery maintains an appropriate operating temperature during the charge and discharge process. Simulating the thermal behavior during the charge and discharge process can detect potential thermal problems in advance and provide data support for the improvement and safety protection of the battery system. Finite element structural analysis can provide structural performance evaluation of the battery shell under different heat load conditions, help identify potential structural weaknesses and stress concentration areas, and optimize the design to enhance the structural strength and durability by analyzing the material structure data of the battery shell to ensure the stability of the battery under various operating conditions. Finite element analysis can predict the performance of the battery shell under extreme conditions, so as to take measures in advance to prevent failure and safety hazards.

[0031] Optionally, step S135 is specifically:

[0032] Extract the battery charge and discharge heat characteristics according to the battery charge and discharge simulation data, so as to obtain the battery charge and discharge heat data;

[0033] Calculate the battery charge and discharge thermal effect according to the battery charge and discharge simulation data, so as to obtain the battery charge and discharge thermal effect data;

[0034] Draw a heat conduction distribution map of the battery heat flow model according to the battery charge and discharge heat data and the battery charge and discharge thermal effect data, so as to obtain a heat conduction distribution map;

[0035] Calculate the thermal runaway risk based on the heat conduction distribution diagram to obtain thermal runaway risk data;

[0036] Extract the thermal runaway position features of the battery casing based on the thermal runaway risk data, thereby obtaining the thermal runaway position data of the battery casing;

[0037] The material structure analysis is performed based on the thermal runaway position data of the battery casing to obtain the material structure data of the battery casing.

[0038] The present invention can help accurately understand the heat distribution generated by the battery during the charging and discharging process by taking the heat characteristics during the battery charging and discharging process, which helps to identify the heat concentration area and provide data support for the optimization of the thermal management strategy. Through the obtained heat data, the heat dissipation design of the battery system can be optimized to ensure that the battery can effectively dissipate heat during the charging and discharging process to avoid overheating problems. Understanding the heat distribution characteristics helps to detect the risk of thermal runaway early, so as to take corresponding preventive measures and enhance the safety of the battery system. Calculating the thermal effect during the battery charging and discharging process can evaluate the impact of heat on battery performance, including thermal expansion, material fatigue, etc., so as to more comprehensively understand the thermal response of the battery in practical applications. By understanding the thermal effect data, the battery design and material selection can be improved to adapt to the changes in thermal effects and improve the long-term stability and reliability of the battery. Understanding the thermal effect helps to predict the behavior of the battery under extreme conditions, so as to take measures in advance to prevent thermal runaway problems. The heat conduction distribution map provides a visualization of the heat flow inside the battery, which helps to more intuitively understand how the heat is distributed and transferred inside the battery. By analyzing the heat conduction distribution map, the heat dissipation path and cooling strategy of the battery can be optimized, the hot spot area can be reduced, and the heat dissipation efficiency can be improved. The heat conduction distribution map helps to adjust the thermal management plan in the design stage to ensure that the thermal management effect of the battery in actual use meets the expectations. Calculating the thermal runaway risk can help evaluate the runaway of the battery under high temperature or overheating conditions. This is crucial to ensure the safety of the battery system. By identifying potential thermal runaway risks, preventive and response measures can be formulated in a timely manner to reduce the probability of thermal runaway events. Understanding the thermal runaway risk data helps to improve the overall reliability of the battery system and ensure that the battery can operate stably under various operating conditions. Extracting the thermal runaway position features can accurately locate the areas in the battery casing where thermal runaway occurs, which helps to make targeted improvements and enhancements. By understanding the thermal runaway location, the design of the battery casing can be optimized to enhance the heat dissipation capacity and structural strength of these areas, reduce the occurrence of thermal runaway, and improve the safety and stability of the battery system under extreme conditions. Material structure analysis can evaluate the performance of battery casing materials to ensure that they can effectively withstand thermal stress under high temperature or thermal runaway conditions. By analyzing material structure data, suitable materials can be selected for battery casing design to improve the battery's heat dissipation capacity and heat resistance, and ensure that the battery casing material structure can effectively deal with thermal runaway problems, thereby enhancing the overall structural reliability and safety of the battery system.

[0039] Optionally, step S2 specifically includes:

[0040] Step S21: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data;

[0041] Step S22: Counting battery cell voltages according to the battery pack structure data, thereby obtaining battery cell voltage data;

[0042] Step S23: Calculating the voltage balance degree according to the battery cell voltage data, thereby obtaining battery voltage balance degree data;

[0043] Step S24: performing battery voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data;

[0044] Step S25: performing voltage passive balancing according to the battery voltage imbalance data, thereby obtaining voltage passive balancing data;

[0045] Step S26: performing active voltage balancing according to the battery voltage imbalance data, thereby obtaining active voltage balancing data;

[0046] Step S27: integrating the voltage balancing data according to the passive voltage balancing data and the active voltage balancing data, so as to obtain the battery voltage balancing data.

[0047] The present invention can fully grasp the physical layout and design information inside the battery pack by extracting the structural features of the battery pack, ensuring that the analysis of voltage and balance in subsequent steps is more accurate. Through the battery pack structural data, the layout design of the battery pack can be optimized, the heat dissipation performance, durability and overall performance of the battery system can be improved, and basic data support is provided for the analysis and calculation of voltage balance, ensuring the effectiveness of the subsequent balance strategy. Through the statistics of battery cell voltage, the voltage state of each battery cell can be grasped in real time, which is helpful to detect battery cells with abnormal voltage. Accurate battery cell voltage data provides accurate input for subsequent balance calculations, ensuring the effect of balance adjustment. Timely identification of voltage deviation can extend the service life of the battery pack and reduce unnecessary losses. Through voltage balance calculation, the voltage difference between each battery cell can be quantified, providing data support for determining whether balance is needed. The voltage balance data provides direction for subsequent balance operations, ensuring that targeted voltage balance strategies can effectively reduce voltage differences. Reducing voltage imbalance between battery cells helps reduce battery losses and improve overall battery performance. Voltage imbalance data can accurately display the difference in voltage distribution in the battery pack, helping engineers identify areas that need adjustment. Based on the imbalance data, a more effective battery health management plan can be developed to prevent premature aging of battery cells due to imbalance. By statistically analyzing the voltage imbalance data, high-voltage or low-voltage cells in the battery pack can be accurately located to improve the efficiency of the balancing process. Passive balancing is a relatively simple and low-energy voltage adjustment method that achieves the effect of battery voltage balancing by dissipating excess energy. Passive balancing helps prevent overcharging of battery cells with higher voltages and prolongs the life of the battery pack. This process has little structural interference with the battery system and is suitable for occasions with small voltage deviations, which helps to ensure the safety of the battery pack. Active balancing balances the voltage inside the battery pack through energy transfer. It is more efficient and accurate than passive balancing, and is particularly suitable for situations with large voltage differences. Active balancing does not dissipate energy like passive balancing, but improves energy efficiency and extends the driving range through energy allocation. Active balancing can balance the voltage differences in the entire battery pack and ensure the stability and performance consistency of the battery pack under high load conditions. Integrating the data of active balancing and passive balancing can more effectively optimize the voltage balancing strategy and ensure that the battery pack can maintain a balanced state under different operating conditions. By integrating the data, the voltage fluctuation between battery cells can be reduced, thereby extending the overall service life of the battery pack. The integration of balancing data can help engineers better understand the balanced state of the battery pack and formulate more reasonable maintenance and operation plans.

[0048] Optionally, step S25 is specifically:

[0049] Step S251: performing high voltage battery cell statistics according to the battery voltage imbalance data, thereby obtaining high voltage battery cell data;

[0050] Step S252: Connecting consumption resistors according to the high voltage battery cell data to obtain consumption resistor data;

[0051] Step S253: dissipating the energy of the high voltage battery cell data according to the consumption resistance data, thereby obtaining the energy dissipation data of the high voltage battery cell;

[0052] Step S254: setting a battery voltage balancing threshold value according to the battery voltage balancing degree data, thereby obtaining battery voltage balancing threshold data;

[0053] Step S255: performing a battery voltage balancing threshold comparison on the battery voltage balancing threshold data according to the high voltage battery unit energy dissipation data, thereby obtaining battery voltage balancing degree comparison data;

[0054] Step S256: Perform passive balancing judgment according to the battery voltage balancing degree comparison data, so as to obtain voltage passive balancing data.

[0055] The present invention can quickly identify battery cells with abnormally high voltages by analyzing the battery voltage imbalance data, provide data support for subsequent processing, and timely discover high-voltage battery cells to help avoid damage to these cells due to overcharging, thereby protecting the overall health of the battery pack. By statistically analyzing the high-voltage battery cell data, voltage balancing can be performed more effectively to improve system energy efficiency. By connecting the consumption resistor, the voltage of the high-voltage battery cell can be effectively reduced to a safe level to prevent the battery from overheating or overcharging. The use of the consumption resistor can dissipate excess electrical energy, which helps to maintain the stability of the battery system and reduce potential energy waste. Through a reasonable energy dissipation method, it is ensured that the high-voltage battery cell will not cause safety problems such as thermal runaway. Through the energy dissipation method, the voltage of the high-voltage battery cell is accurately adjusted to the ideal value to ensure the voltage balance of the battery system. Appropriate energy dissipation helps prevent the voltage of the battery cell from being too high and prolongs the service life of the battery. Through energy dissipation, it can ensure that each battery cell works in a relatively balanced state, thereby improving the overall performance of the battery pack. By setting the equalization threshold, it is possible to provide a clear standard for judging the imbalance of battery voltage, ensuring that the automatic adjustment of the battery system is more accurate. The voltage equalization threshold setting helps prevent unnecessary equalization operations, thereby improving the equalization efficiency and reducing energy consumption. By setting a reasonable equalization threshold, potential voltage anomalies can be discovered in time to avoid safety problems caused by voltage fluctuations. By comparing the energy dissipation data with the voltage equalization threshold, the voltage equalization situation can be monitored in real time to determine whether further equalization is needed. By comparing the voltage balance degree data, the equalization strategy can be adjusted according to the actual situation to ensure that the voltage of each battery cell is within the ideal range. The comparison process helps to avoid the phenomenon of excessively high or low voltage, prevent the battery cells in the battery pack from being unbalanced, and ensure the long-term stable operation of the system. By comparing the data, the system can automatically determine whether passive equalization operations are needed, reduce manual intervention, and improve the automation of the control system. Passive equalization can achieve the purpose of voltage equalization through simple energy dissipation without additional energy consumption, thereby improving the energy efficiency of the battery pack. Passive equalization can effectively prevent overcharging or discharging of battery cells, thereby reducing battery loss and extending the service life of the battery pack.

[0056] Optionally, step S26 is specifically:

[0057] Step S261: performing battery cell voltage statistics according to the battery voltage imbalance data, thereby obtaining high voltage battery cell data and low voltage battery cell data;

[0058] Step S262: performing inductive charging according to the high voltage battery cell data, thereby obtaining inductive charging data;

[0059] Step S263: storing magnetic energy according to the inductor charging data, thereby obtaining inductor magnetic energy storage data;

[0060] Step S264: performing inductive discharge on the low-voltage battery cell data according to the inductive magnetic energy storage data, thereby obtaining inductive discharge data;

[0061] Step S265: performing battery cell voltage detection on the inductor charging data and the inductor discharging data, thereby obtaining battery cell voltage data;

[0062] Step S266: Perform active voltage balancing control according to the battery cell voltage data, thereby obtaining active voltage balancing data.

[0063] The present invention can quickly identify abnormal voltage units, especially high voltage and low voltage units, by statistically analyzing the battery voltage imbalance data, and statistically analyzing the high and low voltage battery unit data, which is helpful to accurately locate the problem unit, improve the overall management and maintenance efficiency of the battery pack, provide accurate data support for subsequent voltage balancing operations, and help optimize the overall performance of the battery pack. By charging the high voltage battery unit through inductance, the excess electric energy can be effectively transferred to the energy storage device, providing guarantee for subsequent discharge. Inductive charging can avoid direct dissipation of the electric energy of the high voltage unit, improve energy utilization efficiency, and the inductive charging process can avoid overcharging of the high voltage unit by storing the electric energy of the high voltage battery, thereby ensuring the stable operation of the system. The electric energy in the inductor is converted into magnetic energy storage, which can realize temporary and efficient energy storage in the battery pack, reduce direct electric energy loss, and provide energy support for the subsequent discharge operation of the low voltage unit, which helps to balance the distribution of energy in the battery pack. Magnetic energy storage can prevent the high voltage battery unit from frequently dissipating energy, thereby extending the service life of the battery unit. Through inductive discharge, the stored energy is released to the low-voltage battery cells, realizing energy redistribution within the battery pack and improving the voltage balance of the system. After the low-voltage battery cells are replenished with energy, they can be restored to the ideal working voltage, enhancing the unit performance and stability, and timely replenishing the energy of the low-voltage cells, effectively reducing the risk of excessive discharge or damage of the cells caused by voltage imbalance. By detecting the voltage changes during the inductive charging and discharging process, the voltage status of each battery cell is monitored in real time to ensure timely detection of problems, providing feedback data for the voltage balancing process, helping to evaluate the balancing effect of the charging and discharging process, and optimizing the control strategy. Real-time detection data helps to more accurately control the voltage balancing operation and ensure that the voltage of each cell is always within a reasonable range. Through dynamic monitoring and active control of the battery cell voltage, the system can accurately achieve voltage balancing without consuming excess energy. Active balancing control ensures that the voltage of each battery cell remains within the ideal range, maximizing the overall energy efficiency of the battery pack.

[0064] Optionally, step S3 specifically includes:

[0065] Step S31: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data;

[0066] Step S32: performing voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data;

[0067] Step S33: Calculating the battery heat distribution according to the battery voltage imbalance data, thereby obtaining the battery heat distribution data;

[0068] Step S34: performing battery local temperature rise distribution detection according to the new energy battery short circuit data, thereby obtaining battery local temperature rise distribution data;

[0069] Step S35: dynamically adjusting and cooling the battery according to the battery heat distribution data and the battery local temperature rise distribution data, thereby obtaining the battery dynamic adjustment and cooling data;

[0070] Step S35: Perform battery heat distribution analysis according to the battery dynamic adjustment cooling data, so as to obtain battery heat distribution dynamic adjustment data.

[0071] The present invention simulates the battery short circuit by analyzing the heat dissipation performance of the battery shell, which helps to identify the thermal runaway risk of the battery under abnormal conditions in advance. It provides a data basis for subsequent thermal management measures to avoid safety hazards caused by short circuits. By analyzing the voltage balance of the battery, the voltage difference between each battery cell is evaluated, and the area with voltage imbalance in the battery pack is identified. In this way, the battery pack can be better regulated to improve the overall performance and battery life. Battery voltage imbalance often leads to local heating. This step helps to identify the area with abnormal temperature by calculating the battery heat distribution, providing a scientific basis for subsequent thermal management and cooling regulation, thereby optimizing the heat dissipation of the battery. By analyzing the local temperature rise when the battery is short-circuited, the impact of the short circuit on the heat distribution of the battery is determined. This step can prevent local overheating, prevent potential thermal runaway risks, and improve the safety of the system. Combined with the heat distribution and local temperature rise, the cooling system is dynamically adjusted to ensure that the battery is always within the optimal temperature range. This dynamic adjustment cooling mechanism can improve the working efficiency of the battery and reduce the performance degradation caused by overheating. After the cooling adjustment, the battery heat distribution is further analyzed to ensure that the cooling effect can effectively reduce the heat concentration point and optimize the battery thermal management system. This step can further improve the safety and operating stability of new energy vehicle batteries.

[0072] The present specification also provides a new energy vehicle battery device, which includes a new energy vehicle battery device main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the new energy vehicle battery device main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the new energy vehicle battery device main body and control the new energy vehicle battery device main body, and the electrical control unit includes:

[0073] Heat dissipation structure analysis module: used to obtain new energy vehicle structure data, and extract the battery shell structure features based on the new energy vehicle structure data, so as to obtain the battery shell structure data; perform battery heat dissipation structure analysis based on the battery shell structure data, so as to obtain the new energy vehicle battery shell heat dissipation data;

[0074] Voltage balancing analysis module: used to extract the structural features of the battery pack according to the structural data of the new energy vehicle, so as to obtain the structural data of the battery pack; perform battery voltage balancing analysis according to the structural data of the battery pack, so as to obtain the battery voltage balancing data;

[0075] Heat distribution dynamic adjustment module: used to simulate battery short circuit according to the heat dissipation data of the battery shell of new energy vehicles, so as to obtain new energy battery short circuit data; dynamically adjust the battery heat distribution according to the new energy battery short circuit data and battery voltage balancing data, so as to obtain battery heat distribution dynamic adjustment data;

[0076] Temperature effect influencing factor module: used to obtain new energy vehicle environmental sensing data, and perform temperature effect influencing factor analysis on new energy battery short circuit data based on the new energy vehicle environmental sensing data, so as to obtain a temperature effect influencing factor set;

[0077] Health control warning module: used to perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

[0078] A new energy vehicle battery device of the present invention can implement any new energy vehicle battery control method, and is used to combine the operation and signal transmission medium between various modules to complete a new energy vehicle battery control method. The internal modules of the device cooperate with each other to achieve the battery health control early warning task. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0080] Figure 1A schematic flow chart of the steps of the control method of the new energy vehicle battery device of the present invention;

[0081] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0082] Figure 3 Detailed step flow diagram of step S12 in the present invention;

[0083] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0084] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] To achieve this, please refer to Figures 1 to 3 The present invention provides a control method for a new energy vehicle battery device, the method comprising the following steps:

[0088] Step S1: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data; performing battery heat dissipation structure analysis according to the battery shell structural data, thereby obtaining new energy vehicle battery shell heat dissipation data;

[0089] In this embodiment, the structural data of the new energy vehicle is obtained, and the body, chassis and battery part of the new energy vehicle are accurately scanned using a laser scanner or 3D modeling technology. Based on the scanned data, the battery shell is modeled and structurally analyzed using a three-dimensional modeling software (such as SolidWorks or CATIA), and the structural features such as the material properties, thickness, surface area and heat dissipation design of the shell are extracted. The battery shell structural data includes but is not limited to the detailed parameters such as the geometry of the shell, the heat dissipation channel, and the surface treatment. After obtaining the shell structural data, the heat dissipation structure analysis of the battery shell is performed using computational fluid dynamics (CFD) software (such as ANSYS Fluent), and the heat dissipation effect of the battery when it is running is simulated by setting parameters such as the initial temperature, the thermal conductivity of the material, and the ambient temperature, and the shell heat dissipation data is generated. The data includes information such as the heat dissipation efficiency of the battery shell, the airflow distribution of the cooling channel, etc., for subsequent heat management and short circuit simulation.

[0090] Step S2: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data; performing battery voltage balancing analysis according to the battery pack structure data, thereby obtaining battery voltage balancing data;

[0091] In this embodiment, based on the structural data of new energy vehicles, the structural features of the battery pack are extracted by using finite element analysis software (such as HyperMesh). The content of feature extraction includes the arrangement of battery cells, the connection method between battery cells, the heat dissipation path and the mechanical connection points of the battery module. The voltage balance is analyzed by combining the collected battery cell voltage data with the built-in voltage detection module of the BMS system. The battery voltage balance is quantitatively analyzed, the threshold range of the voltage difference is defined, and the voltage difference between the battery cells is calculated by data analysis software (such as Matlab), and the voltage deviation value and overall voltage balance of each battery cell are obtained. The analysis results generate a battery voltage balance report, which contains parameters such as the voltage deviation value of each battery cell and the trend of balance change.

[0092] Step S3: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data; performing a battery heat distribution dynamic adjustment according to the new energy battery short circuit data and the battery voltage balancing data, thereby obtaining the battery heat distribution dynamic adjustment data;

[0093] In the present embodiment, according to the heat dissipation data of the battery shell of the new energy vehicle, the battery short circuit simulation is performed using the battery thermal analysis software (such as COMSOL Multiphysics). Initial conditions are set, such as the short-circuit resistance inside the battery, the ambient temperature, the heat dissipation boundary conditions, etc., to simulate the accumulation process of the heat inside the battery under the short-circuit condition, and obtain the new energy battery short-circuit data, and the data include the temperature rise curve after the short circuit and the distribution of the local hot spots. On this basis, in combination with the battery voltage balancing data obtained in step S2, the distribution of the heat inside the battery is dynamically adjusted by the heat management system. The adjustment mode includes optimizing the operating parameters of the cooling system (such as the power of the air-cooled or liquid-cooled system, the coolant flow rate, etc.), and adjusting the cooling measures according to the real-time heat distribution data to ensure that the temperature of each part of the battery is uniform. The final output battery heat distribution dynamic adjustment data is used to optimize the safety and performance of the battery.

[0094] Step S4: acquiring new energy vehicle environmental sensor data, and performing temperature effect influencing factor analysis on new energy battery short circuit data according to the new energy vehicle environmental sensor data, thereby obtaining a temperature effect influencing factor set;

[0095] In this embodiment, the external environment data of the new energy vehicle is collected by on-board environmental sensors (such as temperature sensors, humidity sensors, and pressure sensors). The sensor data is processed using a data analysis system (such as LabVIEW) to analyze the impact of environmental factors on battery heat dissipation and short-circuit simulation. During the analysis process, the sensor data is first pre-processed to remove noise and abnormal data, and then the temperature effect influencing factors are subjected to principal component analysis (PCA) to extract environmental parameters that have a greater impact on temperature (such as external temperature, humidity, air velocity, etc.). According to the short-circuit data of new energy batteries, a multi-factor coupling model is established to analyze the impact of short circuits on battery temperature effects under different environmental conditions, and to generate a set of temperature effect influencing factors. The content of the temperature effect influencing factor set includes the contribution rate of each influencing factor to the local temperature rise of the battery and its degree of influence on the risk of thermal runaway.

[0096] Step S5: Perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data, and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

[0097] In this embodiment, based on the temperature effect influencing factor set and the dynamic adjustment data of the battery heat distribution, a battery health management system (such as a built-in diagnostic module of the BMS) is used to comprehensively analyze the overall operating status of the battery pack. The system monitors the voltage, temperature, current and other operating parameters of the battery in real time, establishes a battery health status model, and analyzes the battery health status under the current temperature and voltage conditions. Use a prediction algorithm (such as a random forest algorithm or a neural network algorithm in machine learning) to perform a warning analysis on the future battery health status to determine whether the battery is at risk of overheating, short circuit or over-discharge. Generate battery health control warning data based on the analysis results, and the warning content includes the health status score of the battery pack, risk level, recommended operations, etc. The warning data is uploaded to the new energy vehicle management platform through the on-board communication module. The platform can automatically adjust the vehicle's operating strategy or perform battery maintenance and replacement operations according to the warning task to ensure the safety and long-term stable operation of the battery pack.

[0098] Optionally, step S1 specifically includes:

[0099] Step S11: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data;

[0100] In this embodiment, the structural data of the new energy vehicle is obtained, and the vehicle is scanned in detail in three dimensions using a high-precision laser scanner to obtain complete structural data including the body, chassis and battery components. These data are imported into a three-dimensional modeling software (such as SolidWorks or CATIA) for processing. The feature extraction tool of the software is used to extract the features such as the geometry, structural details and internal component layout of the battery shell. During the feature extraction process, the thickness distribution of the battery shell, the position and size of the heat dissipation holes, the support structure and the material distribution are analyzed. These extracted battery shell structural data will include the spatial layout of the battery shell, material properties and heat source location, etc., as the basis for subsequent analysis.

[0101] Step S12: performing a battery heat dissipation path structure analysis according to the battery housing structure data, thereby obtaining the battery heat dissipation path structure data;

[0102] In this embodiment, the battery housing structure data is used to perform battery heat dissipation path structure analysis through computational fluid dynamics (CFD) software (such as ANSYS Fluent). First, a three-dimensional model of the battery housing is established in the CFD software, and heat dissipation paths such as heat dissipation holes, cooling channels, and air ducts are defined. Boundary conditions are set, including battery operating temperature, heat dissipation power, and ambient temperature. Then run a heat flow simulation to analyze the heat flux distribution and temperature gradient under different heat dissipation paths. Through numerical calculation, the battery heat dissipation path structure data is generated, including the heat flux density, heat dissipation efficiency, and temperature rise area of ​​each heat dissipation channel. These data are used to evaluate the effectiveness of the heat dissipation path and optimize the heat dissipation design.

[0103] Step S13: performing a battery shell material structure analysis according to the battery shell structure data, thereby obtaining the battery shell material structure data;

[0104] In this embodiment, the battery shell material structure analysis is performed based on the battery shell structure data. The material type, thickness, and hierarchical structure of the battery shell are extracted. The thermal performance of the shell material is tested using material mechanics analysis software (such as ABAQUS). The thermal physical property parameters such as thermal conductivity, specific heat capacity, and density of the material are input, and the actual operating temperature range is set. A heat conduction analysis is performed to obtain the thermal response of the battery shell material under different temperature conditions. This includes the thermal conductivity, heat capacity, and thermal stability of the material under extreme conditions. The result forms the battery shell material structure data, which provides thermal management performance information of the material under actual working conditions.

[0105] Step S14: Merging the new energy battery structure according to the battery heat dissipation path structure data and the battery shell material structure data, thereby obtaining the new energy vehicle battery shell heat dissipation data.

[0106] In this embodiment, the new energy battery structure is merged according to the battery heat dissipation path structure data and the battery shell material structure data. The battery heat dissipation path structure data is combined with the battery shell material structure data, and integrated analysis is performed using a comprehensive design tool (such as Autodesk Fusion 360). First, the heat flow data of the heat dissipation path is combined with the thermal conductivity of the material to create a comprehensive thermal management model. Perform a full system thermal analysis of the model to evaluate the heat dissipation effect under actual operating conditions. By setting appropriate heat loads, ambient temperatures, and working conditions, the heat dissipation performance under different working conditions is simulated. Finally, comprehensive heat dissipation data for new energy vehicle battery shells is generated, including the thermal management effect of the entire battery system, potential thermal problems, and optimization suggestions. These data are used to guide improvements in battery system design to ensure the safety and performance stability of the battery in actual use.

[0107] Optionally, step S12 is specifically:

[0108] Step S121: extracting the characteristics of the heat dissipation holes of the battery and the characteristics of the heat dissipation fins of the battery according to the battery shell structure data, thereby obtaining the data of the heat dissipation holes of the battery and the data of the heat dissipation fins of the battery;

[0109] In this embodiment, the battery housing structure data is obtained, and the battery housing is modeled in detail using high-resolution digital photography technology and laser scanning equipment. The acquired data is imported into computer-aided design (CAD) software, such as SolidWorks or CATIA. The geometric shape of the heat dissipation holes and heat dissipation fins of the battery housing is analyzed by the feature extraction tool in the software. Specifically, the number, position, diameter, shape and other features of all heat dissipation holes are first identified in the CAD model to generate battery heat dissipation hole data. Subsequently, the geometric features of the heat dissipation fins, including the size, shape and arrangement of the fins, are identified and extracted to generate battery heat dissipation fin data. These data will be used for subsequent thermal flow and heat dissipation analysis.

[0110] Step S122: constructing a battery thermal flow model according to the battery heat dissipation hole data, thereby obtaining a battery thermal flow model;

[0111] In this embodiment, a battery thermal flow model is constructed based on the battery heat dissipation hole data. First, a three-dimensional geometric model of the battery heat dissipation holes is established in a computational fluid dynamics (CFD) software (such as ANSYS Fluent or COMSOL Multiphysics). Define the heat source, boundary conditions (including ambient temperature, operating temperature, etc.) and material properties (such as thermal conductivity, specific heat capacity, etc.). Grid division is performed to obtain calculation accuracy, and the simulation time step and calculation parameters are set. Run the thermal flow simulation to obtain the temperature distribution and heat flow path inside the battery, and generate a battery thermal flow model. This model reflects the transfer of heat in the battery heat dissipation hole area, and provides basic data for subsequent heat dissipation hole blockage analysis.

[0112] Step S123: performing heat dissipation hole blockage analysis according to the battery thermal flow model, thereby obtaining heat dissipation hole blockage data;

[0113] In this embodiment, the heat dissipation hole blockage analysis is performed based on the battery thermal flow model. First, the battery thermal flow model is modified in the CFD software to simulate different degrees of heat dissipation hole blockage scenarios (such as partial blockage, complete blockage, etc.). Set heat dissipation holes with different blockage ratios, and recalculate the heat flow and temperature distribution. Use the post-processing tools provided by the software to analyze data such as temperature rise and heat flow reduction in the case of blockage to generate heat dissipation hole blockage data. Specifically, record the temperature distribution diagram, heat flow change, and hot spot area for each blockage situation. These data help evaluate the impact of heat dissipation hole blockage on the overall heat dissipation performance of the battery.

[0114] Step S124: Calculating the convection heat transfer coefficient according to the battery cooling fin data, thereby obtaining the cooling fin convection heat transfer coefficient data;

[0115] In this embodiment, the convection heat transfer coefficient is calculated based on the battery cooling fin data. Use heat conduction calculation software (such as HTC or ANSYS) to establish a three-dimensional model of the battery cooling fin. Enter the geometric parameters of the fin, including length, width, thickness, and spacing between the fins. Set fluid mechanics parameters such as fluid velocity, fluid temperature, and fluid properties. By simulating the convection heat transfer process, the convection heat transfer coefficient of each cooling fin is obtained. The specific steps include the determination of the convection heat transfer coefficient, the calculation of the temperature difference, and the analysis of the heat flow. The convection heat transfer coefficient data finally obtained is used to evaluate the heat dissipation efficiency of the fins in actual work.

[0116] Step S125: performing battery heat dissipation path analysis according to the heat dissipation hole blockage data and the heat dissipation fin convection heat transfer coefficient data, thereby obtaining battery heat dissipation path structure data.

[0117] In this embodiment, the battery heat dissipation path analysis is performed based on the heat dissipation hole blockage data and the heat dissipation fin convection heat transfer coefficient data. The heat dissipation hole blockage data and the heat dissipation fin convection heat transfer coefficient data are integrated into the CFD analysis model. Set the actual working conditions of the battery (such as load conditions, ambient temperature, etc.) and define the heat dissipation path, including heat dissipation holes, heat dissipation fins and airflow channels. Run a full system thermal flow simulation to analyze the heat distribution and heat dissipation effect of different heat dissipation paths. By comparing the heat dissipation performance under different blockage conditions, the battery heat dissipation path structure data is generated. This data includes optimization suggestions for heat dissipation efficiency, heat flow distribution and heat dissipation path, which are used to guide the improvement of the battery cooling system.

[0118] Optionally, step S13 is specifically:

[0119] Step S131: performing statistics on the types of battery shell materials according to the battery shell structure data, thereby obtaining battery shell metal material data and battery shell composite material data;

[0120] In this embodiment, the geometry and material information of the battery shell are extracted from the battery shell structure data. These data are usually obtained by a three-dimensional scanner or a digital measurement tool to ensure accurate geometric data. By analyzing these data, different types of materials used for the battery shell are identified, including metals and composite materials. Statistical classification is performed using data management software (such as Microsoft Excel or a database management system), and metal materials and composite materials are counted respectively to generate battery shell metal material data and battery shell composite material data. For example, the number and proportion of metal materials such as aluminum alloy and steel in the battery shell, as well as the use of composite materials such as carbon fiber and glass fiber, are counted. The distribution of various materials is recorded to provide basic data for subsequent heat conduction calculations.

[0121] Step S132: Calculating the thermal conductivity of the metal material data of the battery shell to obtain the thermal conductivity data of the material;

[0122] In this embodiment, the thermal conductivity of the metal material in the battery casing is calculated. The specific type of metal material, such as aluminum, copper or stainless steel, is determined using the metal material data of the battery casing. These metal samples are experimentally tested using standard thermal conductivity testing instruments, such as a laser flash thermal conductivity meter (LFA) or a heat flow meter. The test process involves heating the sample and measuring the change in its heat flow to calculate its thermal conductivity. The physical properties of the material (such as density, thickness) and the thermal conductivity values ​​obtained from the test are input to generate thermal conductivity data for the metal material of the battery casing. These data are used to evaluate the thermal management performance of the battery casing.

[0123] Step S133: calculating the material heat capacity of the battery shell composite material data, thereby obtaining the material heat capacity data;

[0124] In this embodiment, the heat capacity of the composite material in the battery housing is calculated. Data of the composite material of the battery housing, such as carbon fiber or glass fiber reinforced plastic, is obtained. A differential scanning calorimeter (DSC) is used to perform a heat capacity test. The sample is heated in the DSC, and the change in its heat flow is recorded to calculate the heat capacity. During the test, the sample needs to be pretreated, including removing moisture and other impurities. The specific heat capacity data of the material is obtained, and the heat capacity data of the composite material is calculated in combination with the mass and volume of the material. These data provide the necessary parameters for subsequent thermal analysis and charge and discharge simulation.

[0125] Step S134: performing battery charge and discharge simulation according to the material thermal conductivity data and the material heat capacity data, thereby obtaining battery charge and discharge simulation data;

[0126] In this embodiment, the thermal conductivity data and heat capacity data of the battery shell material are used to simulate the battery charge and discharge. First, a mathematical model of battery charge and discharge is established in a thermal analysis software (such as ANSYS or COMSOL Multiphysics). The thermal conductivity and heat capacity data of the battery shell material are input to define the heat generated during the battery charge and discharge process (calculated by the current and voltage changes). During the simulation process, it is necessary to set boundary conditions, such as ambient temperature and the initial temperature of the battery shell, and define the charge and discharge cycle. Run the simulation analysis to obtain the temperature change and heat distribution data of each part during the battery charge and discharge process. These simulation data reflect the thermal management performance of the battery during the charge and discharge process.

[0127] Step S135: Perform finite element structural analysis of the battery casing according to the battery charge and discharge simulation data, thereby obtaining the battery casing material structure data.

[0128] In this embodiment, a finite element structural analysis of the battery shell is performed based on the battery charge and discharge simulation data. The simulation data is imported into a finite element analysis (FEA) software (such as ABAQUS or ANSYS). In the FEA software, a three-dimensional model of the battery shell is established, and material properties (including stiffness, strength and thermal conductivity) and boundary conditions (such as fixed constraints and loading conditions) are defined. The model is meshed to improve the calculation accuracy. The structural analysis is run to evaluate the thermal stress distribution and deformation of the battery shell during the charge and discharge process. The analysis results provide structural response data of the battery shell in an actual working environment, including stress concentration areas and potential structural failure points. These data are used to optimize the design of the battery shell to improve its thermal management performance and reliability.

[0129] Optionally, step S135 is specifically:

[0130] Extract the battery charge and discharge heat characteristics based on the battery charge and discharge simulation data, so as to obtain the battery charge and discharge heat data;

[0131] In this embodiment, when extracting the thermal characteristics of battery charging and discharging, detailed data, including current and voltage, are collected from the battery charging and discharging simulation system. The battery testing system monitors the data in real time through current sensors and voltage sensors, and records the current intensity, voltage value and timestamp during charging and discharging. The data of each time step is processed to calculate the heat of the battery during the charging and discharging process. Specifically, the heat of the battery is calculated by the product of current and voltage and the internal resistance of the battery. The internal resistance value of the battery can be obtained from the technical specifications of the battery or determined experimentally. Using data analysis tools, such as data processing libraries in MATLAB or Python, these data are statistically analyzed and visualized to obtain the thermal characteristic data of the battery at different charging and discharging stages.

[0132] Calculate the battery charge and discharge thermal effect according to the battery charge and discharge simulation data, so as to obtain the battery charge and discharge thermal effect data;

[0133] In this embodiment, the battery charge and discharge thermal effect calculation is based on the battery charge and discharge heat data and the thermal properties of the battery. The specific heat capacity and thermal conductivity are obtained from the data of the battery material, which are usually provided by the material supplier or measured by experiment. By setting the thermal effect model, inputting the initial temperature, charge and discharge rate, heat source intensity, thermal conductivity and specific heat capacity of the material of the battery, the thermal effect of the battery during the charge and discharge process is calculated. The thermal effect calculation can be performed by thermal analysis software (such as ANSYS or COMSOL), which can simulate the thermal response of the battery under different working conditions and generate thermal effect data during the battery charge and discharge process. These data include information such as battery temperature change, heat distribution and heat affected area.

[0134] Draw a heat conduction distribution map of the battery heat flow model according to the battery charge and discharge heat data and the battery charge and discharge thermal effect data, so as to obtain a heat conduction distribution map;

[0135] In this embodiment, when drawing the heat conduction distribution map, the geometric model of the battery needs to be input into the thermal flow simulation software, such as ANSYS Fluent or COMSOL Multiphysics. The heat source position and thermal conductivity parameters of the battery need to be set in the model, and the heat data during the charging and discharging process is used as input. The software will calculate the temperature distribution in different areas based on these data and the geometric structure of the model to generate a heat conduction distribution map. The heat conduction distribution map will show the temperature changes of the battery during the charging and discharging process, focusing on marking the areas with higher temperatures to help analyze the flow path and accumulation of heat in the battery. The generated distribution map provides basic data for further thermal runaway risk calculations.

[0136] Calculate the thermal runaway risk based on the heat conduction distribution diagram to obtain the thermal runaway risk data;

[0137] In this embodiment, the thermal runaway risk calculation relies on the heat conduction distribution map. Areas with temperatures above the safety threshold are identified from the heat conduction distribution map, which represent potential thermal runaway points. The thermal runaway risk calculation is evaluated by inputting the temperature data of these areas into the thermal runaway risk model. Model considerations include the rate of temperature rise, the degree of heat accumulation, the thermal stability of the material, etc. These data are calculated using a professional thermal runaway simulation tool to obtain the risk data for thermal runaway. These risk data provide the location where thermal runaway occurs and its risk level, helping to identify potential safety hazards in battery design.

[0138] Extract the thermal runaway position features of the battery casing based on the thermal runaway risk data, thereby obtaining the thermal runaway position data of the battery casing;

[0139] In this embodiment, the thermal runaway position feature extraction of the battery casing is based on the thermal runaway risk data. The specific location where the thermal runaway occurs is determined from the thermal runaway risk data, which are usually displayed as areas with significantly increased temperatures. Image processing techniques (such as edge detection and regional analysis) are applied to extract the features of these high-risk areas from the heat conduction distribution map. Use tools such as the image processing toolbox in OpenCV or MATLAB to accurately mark the location of the thermal runaway and extract relevant features. These features provide thermal runaway position data for the battery casing to help further analyze and improve the design.

[0140] The material structure analysis is performed based on the thermal runaway position data of the battery casing to obtain the material structure data of the battery casing.

[0141] In this embodiment, the material structure data analysis of the battery housing is based on the thermal runaway position data. The thermal runaway position data is obtained to identify the weak areas of the battery housing. Finite element analysis tools such as ABAQUS or LS-DYNA are used to input the geometric model of the battery housing and its material properties (including thermal conductivity, specific heat capacity, and mechanical strength). Finite element simulation is performed to analyze the structural response of the battery housing under high temperature conditions, including stress distribution, strain, and its effect on the material. Through the simulation results, the material structure data of the battery housing is obtained, including the structural stability and performance under thermal runaway conditions. These data help optimize the design of the battery housing and improve its heat resistance and safety.

[0142] Optionally, step S2 specifically includes:

[0143] Step S21: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data;

[0144] In this embodiment, the process of extracting the structural features of the battery pack begins by obtaining the geometric and electrical characteristic data of the battery pack from the design documents and actual structural data of the new energy vehicle. These data include the arrangement and connection of the battery cells, the overall size of the battery pack, and the electrical connection points. Through CAD tools such as AutoCAD or SolidWorks, the design drawings of the battery pack are input to extract the size, position, and connection method of the battery cells. The extracted data is processed using a graphic analysis tool to generate detailed structural data of the battery pack. These data include the arrangement, connection mode, and structural configuration of the battery cells, which are helpful for subsequent voltage statistics and balance calculations.

[0145] Step S22: Counting battery cell voltages according to the battery pack structure data, thereby obtaining battery cell voltage data;

[0146] In this embodiment, the battery cell voltage statistics involve obtaining the actual voltage value of each battery cell from the battery pack. This process is accomplished by installing voltage sensors on each cell of the battery pack, which record the voltage of the battery cell in real time. Use a data acquisition system (such as NI LabVIEW) to centralize the voltage data into a database. Then, the battery cell voltage is statistically analyzed through data analysis software (such as the NumPy library in MATLAB or Python). The average voltage, maximum voltage, and minimum voltage of each battery cell are calculated to obtain the battery cell voltage data. These data provide the basis for subsequent voltage balance calculations.

[0147] Step S23: Calculating the voltage balance degree according to the battery cell voltage data, thereby obtaining battery voltage balance degree data;

[0148] In this embodiment, the voltage balance calculation is based on the battery cell voltage data. The standard deviation and average value of the voltage are calculated using the battery cell voltage data to evaluate the balance of the voltage of each cell in the battery pack. Statistical analysis tools (such as the SciPy library in MATLAB or Python) are used to calculate the balance index of the battery pack voltage, such as the voltage deviation coefficient. Through these calculations, the battery voltage balance data can be obtained to show whether the voltage in the battery pack is within the allowable range. The balance calculation results will help identify potential problem areas of voltage imbalance.

[0149] Step S24: performing battery voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data;

[0150] In this embodiment, the battery voltage imbalance data statistics involve extracting detailed information of imbalance from the voltage balance data. First, the deviation of the battery cell voltage is determined based on the voltage balance data. A data analysis tool (such as MATLAB or Excel) is used to count the voltage deviation and calculate the voltage difference of each battery cell. A battery voltage imbalance data report is generated to record the voltage deviation and imbalance degree of each battery cell. These data are used to evaluate the voltage stability of the battery pack under different operating conditions and identify the battery cells that need to be balanced.

[0151] Step S25: performing voltage passive balancing according to the battery voltage imbalance data, thereby obtaining voltage passive balancing data;

[0152] In this embodiment, passive voltage balancing achieves battery voltage balancing by adjusting the voltage difference of the battery pack. The passive balancing circuit in the battery management system is used to transfer the energy of the battery cell with higher voltage to the battery cell with lower voltage. Passive balancing usually adopts the method of resistor shunting, using balancing resistors to convert the excess power of the battery cell into heat. By monitoring the current and voltage changes in the battery pack, the value of the resistor is adjusted in real time to achieve passive balancing of the battery voltage. This process ensures that the voltage of each cell in the battery pack tends to be consistent.

[0153] Step S26: performing active voltage balancing according to the battery voltage imbalance data, thereby obtaining active voltage balancing data;

[0154] In this embodiment, active voltage balancing is achieved by using an active balancing circuit in a battery management system. The voltage of each battery cell is measured to identify battery cells with higher and lower voltages. The active balancing function of the battery management system is used to transfer excess power from cells with higher voltages to cells with lower voltages. The active balancing circuit includes a power transfer module and a controller for accurately controlling the power transfer process. This balancing method effectively reduces the voltage difference of the battery pack and improves the overall efficiency and life of the battery pack.

[0155] Step S27: integrating the voltage balancing data according to the passive voltage balancing data and the active voltage balancing data, so as to obtain the battery voltage balancing data.

[0156] In this embodiment, voltage balancing data integration involves merging voltage passive balancing data and voltage active balancing data. The data obtained during passive balancing and active balancing are input into a data integration tool, such as a data processing module in MATLAB or a Pandas library in Python. The data is compared and analyzed to identify and record the effect of each balancing method. The two types of balancing data are merged to generate a comprehensive voltage balancing data report. The integration result provides the voltage balancing status of the battery pack under different balancing methods and evaluates the final voltage balancing effect.

[0157] Optionally, step S25 is specifically:

[0158] Step S251: performing high voltage battery cell statistics according to battery voltage imbalance data, thereby obtaining high voltage battery cell data;

[0159] In this embodiment, high-voltage battery cell statistics involve identifying battery cells with voltages higher than a specific threshold from battery voltage imbalance data. The voltage value of each battery cell is extracted from the battery voltage imbalance data. Using data analysis tools, such as MATLAB or the Pandas library in Python, a voltage threshold is set, such as the average value of the battery pack voltage plus a standard deviation. By writing a screening algorithm, battery cells with battery voltages higher than the threshold are listed. The specific information of these high-voltage battery cells includes their location, actual voltage value, and relative deviation, which form high-voltage battery cell data and provide basic data for subsequent resistance connection and energy dissipation calculations.

[0160] Step S252: Connecting consumption resistors according to the high voltage battery cell data to obtain consumption resistor data;

[0161] In this embodiment, the process of connecting the consumption resistor includes applying a consumption resistor to the high-voltage battery cell to achieve power consumption and voltage reduction. Select an appropriate consumption resistor, whose resistance is determined according to the voltage level of the battery cell and the expected current consumption. The resistance connection is usually completed through a resistor and a wiring assembly in a battery management system. The selection of the resistor is based on the calculated power demand to ensure that it can effectively consume the excess energy of the battery. The consumption resistor is connected to the electrode of the high-voltage battery cell, and the current is directed to the ground or other loads through the resistor to obtain the consumption resistor data. These data include the resistance value, current intensity and energy dissipation of the resistor, providing necessary information for subsequent energy consumption calculations.

[0162] Step S253: dissipating the energy of the high voltage battery cell data according to the consumption resistance data, thereby obtaining the energy dissipation data of the high voltage battery cell;

[0163] In this embodiment, the energy dissipation calculation quantifies the energy consumption of the high-voltage battery cell based on the consumption resistance data. The electric power formula is used to calculate the electric energy consumption by measuring the voltage and current values ​​across the consumption resistance. The voltage across the resistor is obtained by a precision voltage measuring instrument (such as a digital multimeter), and the current value is measured by a current sensor. During the calculation process, the voltage and current values ​​are substituted into the formula to obtain the energy consumption data of each battery cell. The energy consumption data is summarized, and the heat and energy dissipation released by the battery cell during the consumption process are analyzed to generate the energy dissipation data of the high-voltage battery cell. These data are used to evaluate the efficiency of energy consumption and the thermal management requirements of the battery pack.

[0164] Step S254: setting a battery voltage balancing threshold value according to the battery voltage balancing degree data, thereby obtaining battery voltage balancing threshold data;

[0165] In this embodiment, the battery voltage equalization threshold setting determines the equalization standard based on the battery voltage equalization data. The battery voltage equalization data is analyzed to calculate the voltage range and standard deviation of the battery pack. Based on these statistical data, the upper and lower limit thresholds of the battery voltage equalization are set. For example, the standard deviation value of the battery voltage can be added to the average voltage as the upper limit of the equalization, and the standard deviation value can be subtracted as the lower limit of the equalization. These thresholds are calculated and set by data analysis software (such as MATLAB or Python). The set threshold data will be used to determine whether the battery pack voltage meets the equalization requirements and provide a benchmark for subsequent voltage equalization comparisons.

[0166] Step S255: performing a battery voltage balancing threshold comparison on the battery voltage balancing threshold data according to the high voltage battery unit energy dissipation data, thereby obtaining battery voltage balancing degree comparison data;

[0167] In this embodiment, the battery voltage balancing degree comparison is analyzed based on the battery voltage balancing threshold data and the high voltage battery unit energy dissipation data. The battery voltage balancing threshold data is compared with the energy dissipation data. Through a data analysis tool (such as MATLAB or Python), the voltage value of the high voltage battery unit is matched with the balancing threshold to identify the battery unit that exceeds the threshold. The energy dissipation data is analyzed to evaluate the energy consumption of these battery units in the process of reaching the balancing threshold. The battery voltage balancing degree comparison data is generated to show the performance and efficiency of each battery unit in the voltage balancing process, which helps to evaluate the effectiveness of the voltage balancing measures.

[0168] Step S256: Perform passive balancing judgment according to the battery voltage balancing degree comparison data, so as to obtain voltage passive balancing data.

[0169] In this embodiment, the passive balancing determination process is performed based on the battery voltage balancing comparison data. By analyzing the battery voltage balancing comparison data, it is determined whether there is voltage imbalance in the battery pack. If the data shows that despite the voltage balancing measures, there are still large differences in the voltage of the battery cells, a passive balancing determination is performed. The passive balancing function in the battery management system is used to adjust the voltage difference of the battery cells by consuming resistors and the like. Based on the determination results, voltage passive balancing data is generated to record the application of passive balancing measures and their effects. This process helps ensure the voltage balancing performance of the battery pack in actual applications.

[0170] Optionally, step S26 is specifically:

[0171] Step S261: performing battery cell voltage statistics according to the battery voltage imbalance data, thereby obtaining high voltage battery cell data and low voltage battery cell data;

[0172] In this embodiment, the process of battery cell voltage statistics includes extracting the voltage value of the battery cell from the battery voltage imbalance data to classify high-voltage and low-voltage battery cells. Obtain battery voltage imbalance data, including the voltage measurement value of each battery cell. Use data analysis tools, such as the Pandas library in Python, to set a voltage threshold, which is usually the average value of the battery pack voltage plus a standard deviation. Compare the voltage value of the battery cell with the threshold to identify battery cells with voltages higher than the threshold (high-voltage battery cell data) and battery cells with voltages lower than the threshold (low-voltage battery cell data). The high-voltage and low-voltage battery cell data generated in this step provide basic data for subsequent inductive charging and discharging operations.

[0173] Step S262: performing inductive charging according to the high voltage battery cell data, thereby obtaining inductive charging data;

[0174] In this embodiment, inductive charging involves applying an inductor to a high-voltage battery cell to achieve the charging process. Select a suitable inductor whose inductance value is determined according to the voltage of the battery cell and the required charging current. Connect the inductor to the high-voltage battery cell and control the charging process through a battery management system. Monitor the voltage and current values ​​across the inductor and use a current sensor and a voltage measuring instrument (such as a digital multimeter) for real-time measurement. Through the inductive charging process, inductive charging data is generated to record the current intensity, charging time, and working status of the inductor during the charging process. These data are used to analyze the impact of the inductor on the battery cell during the charging process.

[0175] Step S263: storing magnetic energy according to the inductor charging data, thereby obtaining inductor magnetic energy storage data;

[0176] In this embodiment, the magnetic energy storage step quantifies the magnetic energy stored in the inductor based on the inductor charging data. The magnetic energy storage amount in the inductor is calculated by the current and voltage values ​​during the inductor charging process. The calculation of magnetic energy storage depends on the current and time of the inductor, as well as the inductance value of the inductor. Using appropriate instruments and calculation methods, the energy storage amount in the inductor is recorded as inductor magnetic energy storage data. The magnetic energy storage data includes the total amount of magnetic energy stored and the storage efficiency, which helps to understand the role of the inductor in the battery charging process.

[0177] Step S264: performing inductive discharge on the low-voltage battery cell data according to the inductive magnetic energy storage data, thereby obtaining inductive discharge data;

[0178] In this embodiment, the inductor discharge operation is based on the inductor magnetic energy storage data to discharge the low-voltage battery cell. First, the inductor is connected to the low-voltage battery cell, and the inductor is controlled to release the stored magnetic energy for discharge. The changes in current and voltage during the inductor discharge process are monitored, and the relevant data are recorded using a current sensor and a voltage measuring instrument. The inductor discharge data includes the discharge current intensity, discharge time, and voltage change. These data are used to evaluate the performance of the inductor during the discharge process and the impact on the low-voltage battery cell.

[0179] Step S265: performing battery cell voltage detection on the inductor charging data and the inductor discharging data, thereby obtaining battery cell voltage data;

[0180] In this embodiment, the battery cell voltage detection process includes measuring the battery cell voltage after the inductive charging and discharging operations. Use a high-precision voltage measuring instrument, such as a digital multimeter or a battery monitoring system, to record the voltage value of the battery cell after the charging and discharging operations. The collected voltage data is used to analyze the voltage changes of the battery cell. The data includes the voltage value of the battery cell, the voltage fluctuation during the charging and discharging process, and the voltage recovery. The goal of this step is to obtain the actual voltage data of the battery cell during the charging and discharging process to provide a basis for active voltage balancing control.

[0181] Step S266: Perform active voltage balancing control according to the battery cell voltage data, thereby obtaining active voltage balancing data.

[0182] In this embodiment, active voltage balancing control is performed based on battery cell voltage data. The battery cell voltage data is analyzed to identify the voltage imbalance in the battery pack. Through the active balancing algorithm in the battery management system, adjustment measures are applied to the battery cells with uneven voltage. A voltage regulation module, such as a voltage balancing circuit or a battery balancer, is used to actively adjust the voltage of the battery cell to achieve a balanced state. The voltage changes during the adjustment process are monitored, and the adjusted voltage data is recorded to generate active voltage balancing data. These data are used to evaluate the effectiveness of active balancing measures to ensure that the voltage of the battery pack reaches the desired balanced state.

[0183] Optionally, step S3 specifically includes:

[0184] Step S31: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data;

[0185] In this embodiment, the process of battery short circuit simulation includes simulating battery short circuit conditions under a controlled environment to obtain short circuit data of new energy batteries. The heat dissipation data of the battery shell of new energy vehicles is obtained, including heat dissipation holes, heat dissipation fins and material properties. Based on these data, a battery short circuit simulation experiment is set up, and the battery unit is short-circuited using a battery test bench and a short circuit simulator. During the short circuit process, current sensors and temperature sensors are used to monitor the current changes and temperature changes of the battery in real time. The current peak value, duration and temperature changes on the battery surface during the short circuit are recorded to generate new energy battery short circuit data. This data is used to analyze the thermal behavior and electrical performance of the battery under short circuit conditions.

[0186] Step S32: performing voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data;

[0187] In this embodiment, voltage balance statistics is an analysis process based on battery voltage balance data. Battery voltage balance data is collected, including the voltage value of each battery cell in the battery pack. These voltage values ​​are analyzed using statistical tools, such as statistical libraries in MATLAB or Python, to calculate the voltage balance index. The calculation steps include counting the voltage mean, standard deviation, and maximum and minimum voltage values ​​of the battery pack. Battery voltage imbalance data is generated through statistical analysis of battery voltages. These data are used to identify the degree of voltage imbalance in the battery pack and provide a basis for subsequent heat distribution calculations.

[0188] Step S33: Calculating the battery heat distribution according to the battery voltage imbalance data, thereby obtaining the battery heat distribution data;

[0189] In this embodiment, the battery heat distribution calculation is performed based on the battery voltage imbalance data. The battery voltage imbalance data is obtained, and combined with the thermal characteristic data of the battery pack (such as thermal conductivity and heat dissipation coefficient), the heat distribution calculation is performed using a heat conduction model. Apply the heat conduction formula to calculate the heat generation and dissipation of each unit in the battery pack. Using calculation tools such as ANSYS or other thermal conduction analysis software, input the voltage imbalance data as a heat source and simulate the heat distribution inside the battery pack. The generated battery heat distribution data shows the distribution of heat in the battery pack, revealing the heat concentration area and heat dissipation efficiency problems.

[0190] Step S34: performing battery local temperature rise distribution detection according to the new energy battery short circuit data, thereby obtaining battery local temperature rise distribution data;

[0191] In this embodiment, the local temperature rise distribution detection of the battery involves analyzing the battery short circuit simulation data to detect the local temperature rise of the battery. First, using the temperature data recorded in the battery short circuit experiment, an infrared thermal imager or a thermocouple is used to measure the temperature changes on the surface and inside of the battery. By combining the short circuit simulation data with the temperature data, the hot spot area of ​​local temperature rise of the battery is identified. Generate the local temperature rise distribution data of the battery and record the location and temperature value of the highest temperature point. These data are used to evaluate the thermal management capability of the battery under abnormal conditions and the risk of local temperature rise.

[0192] Step S35: dynamically adjusting and cooling the battery according to the battery heat distribution data and the battery local temperature rise distribution data, thereby obtaining the battery dynamic adjustment and cooling data;

[0193] In this embodiment, the battery dynamically adjusts the cooling based on the battery heat distribution data and the local temperature rise distribution data. In combination with the battery heat distribution data and the local temperature rise distribution data, a dynamic adjustment strategy for the cooling system is designed. Use a temperature sensor and a cooling control system to monitor the temperature changes of the battery in real time, and automatically adjust the working state of the cooling system according to the temperature data. Adjustment measures include turning on or adjusting the fan, liquid cooling system, or adjusting the working mode of the radiator. Record the cooling efficiency and battery temperature changes during the adjustment process to generate battery dynamic adjustment cooling data. These data are used to optimize the response strategy of the cooling system to ensure that the battery maintains a suitable temperature under different operating conditions.

[0194] Step S35: Perform battery heat distribution analysis according to the battery dynamic adjustment cooling data, so as to obtain battery heat distribution dynamic adjustment data.

[0195] In this embodiment, the dynamic adjustment of the battery heat distribution is performed based on the battery dynamic adjustment cooling data. The battery dynamic adjustment cooling data is used to analyze the battery heat distribution after the cooling system is adjusted. Use thermal analysis tools, such as CFD simulation software, to input the data after cooling adjustment and calculate the heat distribution changes of the battery pack. Record the heat distribution data after cooling adjustment and evaluate the impact of the cooling system on the battery heat distribution under different working conditions. By comparing the heat distribution data before and after adjustment, the cooling strategy is optimized, the thermal management effect of the battery is improved, and the stability and safety of the battery in actual use are ensured.

[0196] The present specification also provides a new energy vehicle battery device, which includes a new energy vehicle battery device main body, a power supply unit and an electrical control unit, wherein the power supply unit is installed inside the new energy vehicle battery device main body, the electrical control unit is electrically connected to the power supply unit, and the electrical control unit is used to charge the new energy vehicle battery device main body and control the new energy vehicle battery device main body, and the electrical control unit includes:

[0197] Heat dissipation structure analysis module: used to obtain new energy vehicle structure data, and extract the battery shell structure features based on the new energy vehicle structure data, so as to obtain the battery shell structure data; perform battery heat dissipation structure analysis based on the battery shell structure data, so as to obtain the new energy vehicle battery shell heat dissipation data;

[0198] Voltage balancing analysis module: used to extract the structural features of the battery pack according to the structural data of the new energy vehicle, so as to obtain the structural data of the battery pack; perform battery voltage balancing analysis according to the structural data of the battery pack, so as to obtain the battery voltage balancing data;

[0199] Heat distribution dynamic adjustment module: used to simulate battery short circuit according to the heat dissipation data of the battery shell of new energy vehicles, so as to obtain new energy battery short circuit data; dynamically adjust the battery heat distribution according to the new energy battery short circuit data and battery voltage balancing data, so as to obtain battery heat distribution dynamic adjustment data;

[0200] Temperature effect influencing factor module: used to obtain new energy vehicle environmental sensing data, and perform temperature effect influencing factor analysis on new energy battery short circuit data based on the new energy vehicle environmental sensing data, so as to obtain a temperature effect influencing factor set;

[0201] Health control warning module: used to perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

[0202] A new energy vehicle battery device of the present invention can implement any new energy vehicle battery control method, and is used to combine the operation and signal transmission medium between various modules to complete a new energy vehicle battery control method. The internal modules of the device cooperate with each other to achieve the battery health control early warning task.

[0203] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0204] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for a new energy vehicle battery device, characterized in that: The following steps are involved: Step S1: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data; performing battery heat dissipation structure analysis according to the battery shell structural data, thereby obtaining new energy vehicle battery shell heat dissipation data; Step S2: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data; performing battery voltage balancing analysis according to the battery pack structure data, thereby obtaining battery voltage balancing data; Step S3: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data; performing a battery heat distribution dynamic adjustment according to the new energy battery short circuit data and the battery voltage balancing data, thereby obtaining the battery heat distribution dynamic adjustment data; Step S4: acquiring new energy vehicle environmental sensor data, and performing temperature effect influencing factor analysis on new energy battery short circuit data according to the new energy vehicle environmental sensor data, thereby obtaining a temperature effect influencing factor set; Step S5: Perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data, and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

2. The control method of the new energy vehicle battery device according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: acquiring new energy vehicle structural data, and extracting battery shell structural features according to the new energy vehicle structural data, thereby obtaining battery shell structural data; Step S12: performing a battery heat dissipation path structure analysis according to the battery housing structure data, thereby obtaining the battery heat dissipation path structure data; Step S13: performing a battery shell material structure analysis according to the battery shell structure data, thereby obtaining the battery shell material structure data; Step S14: Merging the new energy battery structure according to the battery heat dissipation path structure data and the battery shell material structure data, thereby obtaining the new energy vehicle battery shell heat dissipation data.

3. The control method of the new energy vehicle battery device according to claim 2, characterized in that: Step S12 is specifically as follows: Step S121: extracting the characteristics of the heat dissipation holes of the battery and the characteristics of the heat dissipation fins of the battery according to the battery shell structure data, thereby obtaining the data of the heat dissipation holes of the battery and the data of the heat dissipation fins of the battery; Step S122: constructing a battery thermal flow model according to the battery heat dissipation hole data, thereby obtaining a battery thermal flow model; Step S123: performing heat dissipation hole blockage analysis according to the battery thermal flow model, thereby obtaining heat dissipation hole blockage data; Step S124: Calculating the convection heat transfer coefficient according to the battery cooling fin data, thereby obtaining the cooling fin convection heat transfer coefficient data; Step S125: performing battery heat dissipation path analysis according to the heat dissipation hole blockage data and the heat dissipation fin convection heat transfer coefficient data, thereby obtaining battery heat dissipation path structure data.

4. The control method of the new energy vehicle battery device according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: performing statistics on the types of battery shell materials according to the battery shell structure data, thereby obtaining battery shell metal material data and battery shell composite material data; Step S132: Calculating the thermal conductivity of the metal material data of the battery shell to obtain the thermal conductivity data of the material; Step S133: calculating the material heat capacity of the battery shell composite material data, thereby obtaining the material heat capacity data; Step S134: performing battery charge and discharge simulation according to the material thermal conductivity data and the material heat capacity data, thereby obtaining battery charge and discharge simulation data; Step S135: Perform finite element structural analysis of the battery casing according to the battery charge and discharge simulation data, thereby obtaining the battery casing material structure data.

5. The control method of the new energy vehicle battery device according to claim 4, characterized in that: Step S135 is specifically as follows: Extract the battery charge and discharge heat characteristics based on the battery charge and discharge simulation data, so as to obtain the battery charge and discharge heat data; Calculate the battery charge and discharge thermal effect according to the battery charge and discharge simulation data, so as to obtain the battery charge and discharge thermal effect data; Draw a heat conduction distribution map of the battery heat flow model according to the battery charge and discharge heat data and the battery charge and discharge thermal effect data, so as to obtain a heat conduction distribution map; Calculate the thermal runaway risk based on the heat conduction distribution diagram to obtain the thermal runaway risk data; Extract the thermal runaway position features of the battery casing based on the thermal runaway risk data, thereby obtaining the thermal runaway position data of the battery casing; The material structure analysis is performed based on the thermal runaway position data of the battery casing to obtain the material structure data of the battery casing.

6. The control method of the new energy vehicle battery device according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: extracting battery pack structure features according to the new energy vehicle structure data, thereby obtaining battery pack structure data; Step S22: Counting battery cell voltages according to the battery pack structure data, thereby obtaining battery cell voltage data; Step S23: Calculating the voltage balance degree according to the battery cell voltage data, thereby obtaining battery voltage balance degree data; Step S24: performing battery voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data; Step S25: performing voltage passive balancing according to the battery voltage imbalance data, thereby obtaining voltage passive balancing data; Step S26: performing active voltage balancing according to the battery voltage imbalance data, thereby obtaining active voltage balancing data; Step S27: integrating the voltage balancing data according to the passive voltage balancing data and the active voltage balancing data, so as to obtain the battery voltage balancing data.

7. The control method of the new energy vehicle battery device according to claim 6, characterized in that: Step S25 is specifically as follows: Step S251: performing high voltage battery cell statistics according to the battery voltage imbalance data, thereby obtaining high voltage battery cell data; Step S252: Connecting consumption resistors according to the high voltage battery cell data to obtain consumption resistor data; Step S253: dissipating the energy of the high voltage battery cell data according to the consumption resistance data, thereby obtaining the energy dissipation data of the high voltage battery cell; Step S254: setting a battery voltage balancing threshold value according to the battery voltage balancing degree data, thereby obtaining battery voltage balancing threshold data; Step S255: performing a battery voltage balancing threshold comparison on the battery voltage balancing threshold data according to the high voltage battery unit energy dissipation data, thereby obtaining battery voltage balancing degree comparison data; Step S256: Perform passive balancing judgment according to the battery voltage balancing degree comparison data, so as to obtain voltage passive balancing data.

8. The control method of the new energy vehicle battery device according to claim 6, characterized in that: Step S26 is specifically as follows: Step S261: performing battery cell voltage statistics according to the battery voltage imbalance data, thereby obtaining high voltage battery cell data and low voltage battery cell data; Step S262: performing inductive charging according to the high voltage battery cell data, thereby obtaining inductive charging data; Step S263: storing magnetic energy according to the inductor charging data, thereby obtaining inductor magnetic energy storage data; Step S264: performing inductive discharge on the low-voltage battery cell data according to the inductive magnetic energy storage data, thereby obtaining inductive discharge data; Step S265: performing battery cell voltage detection on the inductor charging data and the inductor discharging data, thereby obtaining battery cell voltage data; Step S266: Perform active voltage balancing control according to the battery cell voltage data, thereby obtaining active voltage balancing data.

9. The control method of the new energy vehicle battery device according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing a battery short circuit simulation according to the heat dissipation data of the battery shell of the new energy vehicle, thereby obtaining the new energy battery short circuit data; Step S32: performing voltage balance statistics according to the battery voltage balance data, thereby obtaining battery voltage imbalance data; Step S33: Calculating the battery heat distribution according to the battery voltage imbalance data, thereby obtaining the battery heat distribution data; Step S34: performing local temperature rise distribution detection of the battery according to the short-circuit data of the new energy battery, thereby obtaining local temperature rise distribution data of the battery; Step S35: dynamically adjusting and cooling the battery according to the battery heat distribution data and the battery local temperature rise distribution data, thereby obtaining the battery dynamic adjustment and cooling data; Step S35: Perform battery heat distribution analysis according to the battery dynamic adjustment cooling data, so as to obtain battery heat distribution dynamic adjustment data.

10. A new energy vehicle battery device, characterized in that: The device comprises a main body of a new energy vehicle battery device, a power supply unit and an electrical control unit. The power supply unit is installed inside the main body of the new energy vehicle battery device. The electrical control unit is electrically connected to the power supply unit. The electrical control unit is used to charge the main body of the new energy vehicle battery device and control the main body of the new energy vehicle battery device. The electrical control unit comprises: Heat dissipation structure analysis module: used to obtain new energy vehicle structure data, and extract the battery shell structure features based on the new energy vehicle structure data, so as to obtain the battery shell structure data; perform battery heat dissipation structure analysis based on the battery shell structure data, so as to obtain the new energy vehicle battery shell heat dissipation data; Voltage balancing analysis module: used to extract the structural features of the battery pack according to the structural data of the new energy vehicle, so as to obtain the structural data of the battery pack; perform battery voltage balancing analysis according to the structural data of the battery pack, so as to obtain the battery voltage balancing data; Heat distribution dynamic adjustment module: used to simulate battery short circuit according to the heat dissipation data of the battery shell of new energy vehicles, so as to obtain new energy battery short circuit data; dynamically adjust the battery heat distribution according to the new energy battery short circuit data and battery voltage balancing data, so as to obtain battery heat distribution dynamic adjustment data; Temperature effect influencing factor module: used to obtain new energy vehicle environmental sensing data, and perform temperature effect influencing factor analysis on new energy battery short circuit data based on the new energy vehicle environmental sensing data, so as to obtain a temperature effect influencing factor set; Health control warning module: used to perform battery health control warning on the battery pack structure data according to the temperature effect influencing factor set and the battery heat distribution dynamic adjustment data, so as to obtain battery health control warning data and upload it to the new energy vehicle management platform to execute the new energy vehicle battery health control warning task.

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