The arc plasma torch can be used for pre experiments on ground erosion performance testing of spacecraft flight materials，which can save costs. The three-phase AC arc plasma torch has the advantages of simple power supply and reliable operation. The hollow electrode structure with dual inlet channels can not only improve the electrode life, but also achieve a wider range of power control. However, the design of plasma torches with this type of electrode structure is more complex and there is limited research and application in China. A three-phase AC plasma torch with magnetic motion, tangential inlet, and supersonic jet was developed and numerically modeled and experimentally studied.
Firstly, a three-dimensional turbulent MHD multiphysics coupling simulation model of a hollow electrode three-phase AC arc plasma torch with a dual end inlet structure was established, and the flow state and electric thermal characteristics of the arc plasma inside the torch were obtained. Secondly, the influence laws of air intake, working current, air intake distribution ratio, and working frequency on the electric field, magnetic field, temperature field, flow field distribution, and arc characteristics inside the plasma torch were studied and revealed. Finally, the correctness of the numerical model was verified by comparing the arc voltage, nozzle outlet temperature, and arc root position under various operating conditions in simulation and experiment.
The conclusion drawn from the study is as follows: (1) In a three-phase AC plasma torch, aerodynamic and electromagnetic forces dominate the flow characteristics of the arc root. During the process of increasing the intake volume from 30 g/s to 60 g/s, the cooling effect of the gas flowing along the wall is greater than the heat generated by the arc column, resulting in a downward trend in temperature; And the larger the intake volume, the more obvious the compression effect of the cold air layer on the arc, and the higher the arc pressure; The higher the working current, the higher the plasma temperature and jet velocity. (2) In a hollow electrode AC plasma torch with dual inlet ducts, changing the air intake distribution ratio can alter the position of the arc root along the electrode axis and the magnitude of the output power. Increasing the air intake distribution ratio can make the arc more significantly stretched in the axial direction, the arc longer, and the arc root closer to the arc back cover. (3) When the operating frequency is 1 kHz, the arc has a more stable motion trend, and the rotation speed of the arc root is five times that of the power frequency. The contact area with the electrode is reduced, which reduces the degree of electrode erosion and can improve the electrode life.

Metallic foreign objects in various types of power equipment may cause discharge problems. To achieve accurate multi-spectral monitoring of the discharge phenomenon caused by metallic foreign object, it is necessary to deeply understand its influence on the mechanism of optical radiation during discharge. Currently, research on the impact of metallic foreign objects on high-voltage discharge is mostly focused on the macro level, without delving into the micro-particle level to analyze its effect on the discharge mechanism, and the influence of metallic foreign object on optical radiation during discharge has not been thoroughly explored. To address these issues, this paper analyzes the characteristics of metallic foreign object's impact on the full-band optical radiation of discharge and its influence mechanism on the day-blind ultraviolet band through experiments and simulations.
A high-voltage discharge experimental platform was first constructed. Discharge images were captured using ultraviolet and high-speed cameras, and the emission spectra were measured with a spectrometer to investigate the influence of metallic foreign object on full-band optical radiation during discharge. The effect of metallic foreign objects on the generation of day-blind ultraviolet radiation was further studied. It was verified that the particle transitions responsible for producing day-blind ultraviolet radiation are mainly $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+} \rightarrow \mathrm{X}^{1} \Sigma_{\mathrm{g}}^{+}\right)$ and NO-γ(A²Σ⁺(v′)→X²Π(v″)). Based on this, a two-dimensional plasma simulation model was constructed to investigate the effect of different quantities of large metallic particles and varying masses of metal shavings on the discharge process. The model was used to calculate the number densities of NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles under different conditions, and the simulation results were validated by comparing them with the measured spectra. The experimental and simulation results were then comprehensively analyzed to explore the influence of metallic foreign object on day-blind ultraviolet radiation during discharge.
High-speed camera reveals that metallic foreign object increases the chance of arc formation between the tip of the needle electrode and the metallic foreign object. From the spectrum of the 200-1000 nm band measured in the experiment, it is evident that the increase in metallic foreign object enhances the optical radiation across the entire spectrum generated by the discharge. However, this enhancement is selective to certain bands, with the ultraviolet and visible light bands responding more sensitively. Therefore, ultraviolet and visible light detection is more effective for monitoring discharges caused by metallic foreign objects.
Analysis of UV images, 240~280 nm spectra, and simulations shows that an increase in the metal foreign object causes an increase in the amplitude of the spectral curve of the sun-blind UV band, an increase in the number densities of NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles, and an increase in the rate of the chemical reactions in the discharge region; however, the spectral shape remains basically unchanged, which means that it does not affect the types of chemical reactions and the relative ratios among them. By combining the electric field simulation results, the reason can be analyzed as follows: the metal foreign object increases the strength and inhomogeneity of the electric field, promoting the excitation and ionization of particles. This leads to the production of more NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles, thus promoting the enhancement of the sun-blind ultraviolet radiation.
The results of this paper apply to discharge phenomena in air influenced by metallic foreign objects, and the influence of metal particles on discharge in SF₆ and its alternative gases will be further investigated in the future.

The oil-filled terminal adopts a solid-liquid composite insulation composite structure composed of silicone rubber (SiR) stress cone and silicone oil (SO) inside. Compared to the cable body, when the composite insulation interface is invaded by moisture or contains air gaps or impurities, it can cause electric field distortion. This distortion can trigger creepage and even flashover along the surface of the stress cone, significantly impacting the service life of the oil-filled terminal. Among them, moisture intrusion is recognized as the main factor causing insulation deterioration in cable terminals, and cable termination failure rate caused by it account for about 50%. Therefore, the moisture migration process and equilibrium characteristics between SO and SiR solid-liquid medium in the oil-filling terminal need to be further studied. In this paper, the moisture migration law between SO-SiR composite insulation system in oil-filled terminals is systematically studied, the swelling model and mechanism of SO in the terminals are discussed, and the moisture equilibrium characteristics of SO-SiR composite insulation systems under the effect of temperature and swelling are clarified.
Firstly, this study conducted moisture absorption experiments on SO and SiR under various temperature and humidity conditions. The water content of SO and SiR at different temperature and humidity equilibrium states was measured. Using the indirect equilibrium theory, a moisture equilibrium curve for the SO-SiR composite insulation was plotted. The results show that the water content of SO has a linear relationship with the relative humidity at the same temperature, and the saturated water content of SO changes exponentially with temperature. The water content of SiR has a nonlinear relationship with relative humidity, and the saturated water content of SiR does not change with temperature. As the temperature rises, moisture migrates from the SiR to the SO.
In addition to the moisture migration between the SO and the SiR duplex medium in the oil-filled terminal, the SO will also diffuse into the SiR. This diffusion destroys the physical and chemical cross-linking results of the SiR, and affects the moisture absorption characteristics of the SiR. Therefore, it is necessary to clarify the physical mechanism underlying the swelling of SiR by SO. The results show that the SO swells into the SiR in the form of free state and bound state according to the Langmuir diffusion process. With increasing time, the swelling rate increases as a logarithmic function. With increasing temperature, the equilibrium swelling mass remained unchanged, but the swelling rate increased. Under the SO (solvent)-SiR (solute) system, the elastic free energy of the system increased due to the swelling of SO, which was offset by the Gibbs free energy. Finally, the total free energy is zero, and the swelling reaches equilibrium.
On this basis, the moisture equilibrium curve of SO-SiR composite insulation was further optimized. After the swelling of SO, the free volume of SiR increases, which can dissolve more water. However, SiR with different degrees of swelling still exhibits the same water absorption characteristics as unswollen SiR. Combined with the moisture dissolution characteristics of SO, the moisture equilibrium surface diagram of SO-SiR composite insulation under temperature and swelling was drawn. With increased swelling, water molecules migrate from the SO to the SiR. Through this surface diagram, the water content of SO and SiR under different equilibrium states can be obtained, and the operation and maintenance of oil-filled terminals can be guided.

Ice disasters can cause serious damage to power transmission network, it is crucial to enhance the resilience of power transmission network during ice disasters. Unlike extreme natural disasters such as hurricanes or earthquakes, ice disasters develop slowly and last long time. It is difficult to predict the development trend of ice disaster accurately due to the influence of microclimate and terrain on their geographic coverage. Currently, the spatiotemporal evolution patterns of ice disasters are not clear. The existing research on improving the resilience of power transmission networks considering the impact of ice disasters have not involved the temporal modeling of ice disaster scenarios. Therefore, the paper proposes a method for temporal modeling of ice storm scenarios based on multispectral satellite remote sensing. By combining multispectral remote sensing image fusion methods based on Laplacian pyramid decomposition, efficient extraction and analysis of the spatial distribution and temporal changes of ice-covered areas in Sentinel-2 satellite remote sensing images are achieved. Using partial differential convolution, ice-covered areas are predicted dynamically based on the fused images, and an ice disaster temporal model is constructed. Additionally, a conditional variational autoencoder is used to generate a set of ice disaster scenarios, which accurately reflect the spatiotemporal characteristics of "source-network-load" during ice disasters.
Considering the interaction between the disaster development process and resilience enhancement measures, the power transmission system resilience can be simultaneously enhanced through both pre-disaster prevention and in-disaster repair measures. This paper proposes a comprehensive resilience evaluation index and constructs a two-stage robust resilience enhancement planning model for power transmission networks based on the set of ice disaster scenarios. The first stage focuses on pre-disaster fixed energy storage configuration and pre-planning of maintenance resources to find the optimal investment decision. The second stage focuses on in-disaster power supply through fixed energy storage and emergency maintenance considering limited maintenance resources, ensuring rapid response from fixed energy storage and maintenance teams after the occurrence time of the ice disaster, which aims to ensure rapid load recovery, maximize system resilience, and minimize system economic losses. The model is iteratively solved using a parallelizable column-and-constraint generation algorithm.
Finally, case studies are conducted using ice-covered remote sensing data from a region in Yunnan and a modified IEEE RTS-79 power transmission system as the test system. The results show that the coordination of fixed energy storage power supply and emergency maintenance can effectively ensure power supply and transmission during ice disasters, as the system resilience improved by 90.97% and total system losses decreased by 43.19% during the ice disasters. Compared with other resilience enhancement strategies, the proposed strategy in this paper balances both economic efficiency and resilience. What’s more, different ice disaster center locations are set in the case study considering the inherent uncertainty of ice disasters. The results demonstrate that for ice disasters with multiple origins, the proposed method effectively ensures power restoration in the transmission system, enhances system resilience, reduces load shedding losses and total costs.

Electromagnetic pulse welding (EMPW), an advanced solid-phase welding technology for dissimilar metals, has garnered extensive applications across domains such as electric power transmission, automotive manufacturing, and refrigeration equipment due to its distinctive advantages. However, the Al-Cu joints welded by this technique encounter challenges regarding forming an intermediate layer comprising intermetallic compounds and cracks at the weld seam, which reduces the weld's mechanical performance. Based on the formation mechanism of the interface morphology and the necessary conditions for electromagnetic pulse welding, a method to regulate the electromagnetic pulse welding interface using a dual-coil structure was proposed. This method aimed to suppress the generation of the intermetallic compound intermediate layer in the weld seam by diminishing the horizontal component of the movement velocity at the welding interface, thereby reducing the shear effect at the interface. To validate the efficacy of this approach, an electromechanical coupled finite element simulation model was utilized to compare the electromagnetic parameter distribution characteristics during the EMPW process based on single and dual-coil structures. The experimental results from the high-speed camera verified the simulation of the plate movement process and results revealed that the horizontal component of interface velocity decreased by using the dual-coil structure. A scanning electron microscope was employed to analyze the micro-morphology of the welding interface. The results showed that the welding interface based on a dual-coil structure mainly included the wave and straight types, while the interface via single-coil included the vortex type. The findings indicated that joints welded using a single-coil structure EMPW method exhibited a pronounced intermediate layer at the interface. In contrast, those welded using the double-coils structure EMPW method failed to show the formation of an intermediate layer at the interface, exhibiting a reduced shear effect on the interface morphology and superior mechanical properties. Besides, the line scanning results of the welding interface based on a dual-coil structure reflect a monotonic change in elements, while the welding interface of a single-coil structure exhibits regional oscillations in elements. Overall, the effectiveness of this method in suppressing the formation of intermetallic compounds was validated at the interface. Utilization of a dual-coil structure can reduce the shear effect at the interface by controlling the horizontal component of the plastic flow, thereby suppressing the formation of intermetallic compounds and enhancing the tensile performance of the welded joints. This study contributes to understanding the physical mechanisms of the electromagnetic pulse welding process, which is of great significance for the research and development of high-performance, lightweight heterogeneous metal composite materials and the advancement of lightweight manufacturing.

The catenary insulator is a critical component of the traction power supply system for high-speed railways. It not only provides electrical control insulation but also plays an essential role in supporting the catenary arm structure. Therefore, the operational safety of the insulator is directly related to the stability of the entire high-speed railway system. However, the detection of insulator defects is often subject to various interferences due to the complex and dynamic railway environment, resulting in low detection accuracy. Moreover, traditional detection methods generally only identify the presence of defects but fail to provide specific semantic descriptions of these defects. This limitation significantly hampers the efficiency of fault diagnosis and maintenance operations. To address these challenges, this paper proposes a defect description method for insulators based on a diffusion model. This method optimizes existing detection technologies in several ways, enabling the model to not only detect insulator defects more accurately but also generate detailed textual descriptions of these defects.
Firstly, we designed a large-kernel spatial selection feature extraction network. Compared to traditional feature extraction networks, this network captures the feature information of insulator defects through larger spatial convolution kernels, significantly enhancing the model's ability to extract insulator defect features. The model can accurately identify potential defects in the insulator, even in complex backgrounds. Secondly, we proposed a detection decoder with a fusion diffusion mechanism based on the diffusion model. This decoder generates noise boxes and uses inverse Bayesian diffusion to restore predictions of the insulator's true bounding box, significantly improving the model's resistance to background interference. This innovation allows the model to more effectively isolate background noise in complex environments, thereby improving the accuracy of defect detection. Finally, to address the limitations of traditional detection models in semantic description, we designed an encoder and decoder based on a cross-attention mechanism to achieve cross-modal mapping between images and text. By using the BLIP model driven by a text filtering mechanism, the model can generate corresponding textual descriptions of the defects based on the detection results. The functionality not only provides maintenance personnel with more intuitive references but also greatly enhances the efficiency of fault handling. Experimental results validate the effectiveness of our method. The proposed insulator defect detection model achieved the mAP_0.5 of 93.04% and the AR and F1-score of up to 83.22% and 82.91%. The BLEU achieved 83.51%, with CIDEr of 1.94, ROUGE-L of 81.59%, METEOR of 51.50%, and SPICE of 37.88%.
The experimental results lead to the following conclusions: (1) Utilizing a large-kernel spatial selection feature extraction network as the image encoder enhances the insulator defect detection network's ability to focus on key features, thereby improving the model's detection accuracy. (2) To address the issue of insulator defect detection being easily disturbed by complex background environments, a detection decoder with a fusion diffusion mechanism was designed. This decoder performs inverse Bayesian diffusion on the noise boxes generated by the decoder, restoring the prediction of the insulator's true bounding box. The model's ability to resist background interference reduces the loss of semantic information related to insulator defects, and enhances the accuracy of the predicted bounding boxes. (3) A cross-modal mapping module was designed to map the relationship between insulator image defect features and text features. The language modeling encoder outputs a textual description of the insulator defects, completing the detection task. Thus, the proposed model not only offers higher detection accuracy but also generates accurate and detailed semantic descriptions of the defects, meeting the actual needs for insulator defect detection and description.

In fuel cell hybrid systems, the degradation processes of fuel cells and power batteries are highly inconsistent. The excessive consumption and premature end of life of one power source can disrupt the balance of the power system, deplete the performance of the other power source, accelerate the aging of the entire power system, and negatively affect vehicle economy and system durability. Consequently, it becomes challenging to achieve optimal fuel economy and system durability simultaneously. To address this issue, an optimization strategy based on condition prediction and coordinated power source life degradation is proposed.
Firstly, to improve prediction accuracy, operating conditions are categorized into three typical states: low-speed, medium-speed, and high-speed. An upper-level Markov Chain Monte Carlo (MCMC) prediction model is established based on historical conditions to predict the tram's operating conditions. This prediction provides more information for the lower-level energy management strategy to optimize system energy distribution. Secondly, in the lower-level energy management strategy, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the auxiliary power source are analyzed. A continuous degradation model for the fuel cell and power battery is established, introducing optimization objectives and adaptively adjusting the weights of each objective online to optimize the multi-objective function. Finally, the proposed strategy is compared with the traditional equivalent consumption minimization strategy (ECMS) and the external energy maximization strategy (EEMS).
Results show that at the end of the entire operating condition, the proposed strategy's hydrogen consumption is 99.61 g, the degradation rate difference between the dual power sources is 0.000 66%, the system efficiency is 81.66%, the power fluctuation range is -800 W to 800 W, and the stress on the power battery and supercapacitor is 117.5 and 176.4 respectively. Compared to the ECMS strategy, with a hydrogen consumption of 115.1 g and system efficiency of 77.64%, the proposed strategy improves fuel economy and system efficiency by 15.6% and 5.2% respectively. Compared to the EEMS strategy, with a dual power source degradation rate difference of 0.014 4% and system efficiency of 79.77%, the proposed strategy reduces the degradation rate difference by 21.82 times and improves system efficiency by 2.4%. Additionally, the power fluctuation range under the proposed strategy is significantly reduced compared to the -1 000 W to 1 000 W range under both the ECMS and EEMS strategies, resulting in a smoother power source power curve. Under the ECMS strategy, the stress on the power battery and supercapacitor is 156.6 and 215 respectively, while under the EEMS strategy, the stress is 156.8 and 226.6 respectively. The proposed strategy reduces the stress on the auxiliary power source compared to the ECMS and EEMS strategies, decreasing excessive consumption and resulting in a more reasonable power distribution.
Comprehensive simulation analysis reveals the core advantages of the proposed strategy: (1) Establishing an MCMC prediction model for condition prediction improves the adaptability of the energy management strategy to operating conditions, achieving more reasonable, precise, and efficient energy control and reducing damage to the hybrid power system. (2) Overcoming the poor fuel economy of traditional ECMS and the high inconsistency in power source degradation of EEMS. (3) Achieving superior fuel economy and system durability, thereby extending the lifecycle of fuel cell hybrid systems.

Internal short circuit is one of the most serious faults in transformers, which can lead to a rapid increase in fault energy in a short period of time and easily cause high-energy discharge and explosion inside the equipment. However, there are many potential combinations of internal short circuit conditions in transformers. The analysis method of field-circuit coupling commonly used by transformer manufacturing enterprises has the problems of excessive time and resource consumption. And it is difficult to model jointly with the external power grid. Existing circuit models face difficulties in multi-scale coupling characterization and parameter calculation of windings.
This article focused on the urgent need for transformer short circuit fault analysis. A construction method of multi-scale fault analysis model for single-phase transformer with internal short circuit was proposed. Firstly, based on the multi-scale characteristics of transformer windings and internal short circuit faults, the transformer windings were virtually divided into several sub-windings using axial segmentation. By parametrically scanning the finite element model of the transformer, the self-mutual inductance matrix and resistance matrix of sub-windings was calculated. Secondly, an calculation method was proposed to transform the self-mutual inductance matrix into the coupled leakage inductance matrix, which could effectively characterize the leakage magnetic characteristics between sub-windings. This parameter calculation method could be carried without port short circuit tests, which solved the problem of parameter calculation for existing multi-winding transformer models. Finally, a multi-scale circuit model for transformers was established based on the coupled leakage inductance matrix. By connecting the terminals of each sub-winding based on the electromagnetic connection relationship and the physical process of internal short circuit, transformer fault analysis models for different internal short circuit conditions could be obtained. The problems of low efficiency and poor circuit adaptability in the fault analysis model based on field-circuit coupling were solved.
Furthermore, a disk-scale circuit model of an 80 MV·A single-phase transformer was constructed. A comparative simulation was conducted with the finite element model. The results indicated that the errors of the short-circuit impedance and the peak value of the port current at rated operating condition were almost zero. And the simulation time was reduced by about 99.98%. After single inter-turn short circuit faults, the errors of the first peak values of the port currents and short-circuit currents did not exceed 2.5%. The simulation efficiency was improved while ensuring simulation accuracy. Then, based on a certain engineering accident, a developmental inter-turn short circuit analogy simulation analysis was carried out. The errors of the first peak values of the port currents and short-circuit currents after the fault, as well as the local peak values during the fault development process, did not exceed 5.5%. And the duration of the second harmonic percentage of fault differential current accounting for more than 15% of the circuit model was calculated to be 49 ms. It was consistent with the finite element model calculation results. The existing method was 14ms. Therefore, the proposed construction method of multi-scale fault analysis model for single-phase transformer with internal short circuit can accurately simulate the transient characteristics of transformers with internal short circuit under multiple scales and operating conditions. This method provides a basic model for research on equipment accident analysis, traceability, and fault defense.

Epoxy resin is widely used in epoxy cast electrical equipment such as dry-type transformers and dry-type reactors due to its good mechanical strength, chemical corrosion resistance, and excellent electrical insulation performance. However, the irreversible cross-linking network formed after curing makes it difficult to degrade and recycle retired electrical equipment. Researchers have developed a series of biodegradable resins with high electrical thermal mechanical properties and degradation characteristics by introducing dynamic covalent bonds. However, epoxy electrical equipment such as dry-type transformers and dry-type reactors that operate in complex environments such as high temperature, high electric field, and mechanical vibration for a long time can experience performance degradation due to resin aging, which affects their service life. The changes in the cross-linking structure of epoxy resin caused by thermal oxidative aging may have a certain impact on the service performance and degradation recovery characteristics of degradable resins. This article used the ester exchange catalyst triethanolamine to construct a degradable epoxy resin system, and conducted accelerated thermal oxidative aging tests on it to analyze the effects of aging time and catalyst on the service performance and degradation characteristics of degradable epoxy resin.
Firstly, this article used ester exchange catalyst triethanolamine to construct a degradable epoxy resin system, and used traditional non degradable epoxy resin as a reference to conduct thermal oxidative aging tests on resins with different triethanolamine contents at three temperatures of 180℃, 200℃, and 220℃. Then, the performance changes of different resin systems after aging were studied through comprehensive analysis of electrical properties, thermogravimetric analysis, dynamic thermomechanical analysis, mechanical properties and microstructure analysis. The bending strength retention rate was used as an aging index to estimate the service life. Finally, this article also explored the influence of thermal oxidative aging on the degradation properties of degradable resins.
From the experimental analysis, the following conclusions can be drawn: (1) The insulation and electrical performance of the degradable epoxy resin system after high-temperature aging is slightly worse than that of traditional resins, but the degradation rate of the insulation performance of degradable resins is slower than that of traditional resins under 200℃ and 220℃ conditions, with V-TEOA-0.05 maintaining better electrical performance. (2) The thermal stability of the degradable epoxy resin system is slightly inferior to traditional resins, but V-TEOA-0.05 has a higher storage modulus and a slightly lower glass transition temperature, and also exhibits good thermal properties. (3) As the aging temperature increases, the difference in flexural strength between degradable epoxy resin and traditional resin after aging gradually narrows, and remains basically unchanged after 49 days of aging at 220℃. The estimated lifespan of the V-TEOA-0.05 system shows a temperature index of 163.13℃, demonstrating excellent heat and oxygen aging resistance. (4) In the mixed solution of EG and TBD, the degradation rate of V-TEOA-0.05 sample decreases with increasing aging time, which may be related to the increase in resin crosslinking density, decrease in free volume, and decrease in ester bonds.

Power battery packs are widely used in new energy electric vehicles and are the core components of electric vehicles. Studying the temperature field modeling of the power battery pack is not only beneficial to understanding its temperature field dynamic characteristics, but is also very important for the structural design and health management of the power battery pack. The temperature field of the power battery pack is described by complex partial differential equations. Since a large number of parameters are unknown and many model parameters show strong time variability, traditional physics-based modeling methods are ineffective in achieving online modeling of the temperature field of the power battery pack. Although methods based on deep learning do not rely on physical models, they require a large amount of experimental data during the training process, the model training time is long, and the real-time performance of temperature field prediction is poor. In response to the above problems, this paper proposes a spatio-temporal modeling of the temperature field of power battery packs based on long short-term memory network.
First, the spatio-temporal separation method is used to extract spatial features and time features under offline conditions. Spatial features are continuously updated with the help of incremental learning, and the long short-term memory (LSTM) network is used to model temporal dynamics. Finally, the updated spatial characteristics and time model are integrated to obtain a prediction model of the power battery pack temperature field.
The proposed method was verified on a power battery pack composed of 24 battery cells. Experimental results show that the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. Without airflow interference, the single-point temperature prediction error of the proposed method is less than 0.4℃, and the root-mean-square error (RMSE) on the test set is 0.095 1℃. In the presence of airflow interference, the single-point temperature prediction error of the proposed method is less than 0.07℃, and the RMSE on the test set is 0.014 7℃.Under the condition of air flow, the modeling error of the proposed method is smaller. This is because under the condition of air flow interference, the spatial gradient of the temperature change of the power battery pack at the same time is smaller, that is, the temperature change is gentler, making the spatial characteristics of the modeling smoother.
The following conclusions can be drawn from the simulation analysis: (1) the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. (2) The proposed method can update spatial features in real time through incremental learning, thereby reducing the computational complexity of the method. (3) The proposed method is a purely data-driven method that does not rely on accurate partial differential equations and is therefore suitable for application in temperature field modeling of actual power battery packs.