Internal Parameters Research Articles

A modified handling stability control strategy for steer-by-wire vehicles based on variable steering ratio and active front steering control

Steer-by-wire (SBW) systems with steering actuator motors rotating the front wheels allow more flexibility for handling stability control; however, determining the desired steering angle and accurately tracking the desired steering angle are still key challenges. Therefore, a modified handling stability control strategy for handling stability is proposed in this paper. Firstly, based on the normal function, a unified variable steering ratio (UniVSR) model that varies with vehicle speed and steering wheel angle is developed. Then, the UniVSR model parameters are optimized by particle swarm optimization (PSO) algorithm in speed segments to improve the steering sensitivity at low speeds and the stability at medium and high speeds. Next, the active front steering (AFS) controller that considers the intervention timing and intervention threshold is proposed to improve the stability of the SBW vehicle under the driving conditions of low-adhesion road, split-μ road, and lateral wind interference. Lastly, the linear active disturbance rejection control (LADRC) based angle tracking control algorithm is presented to guarantee the desired angle tracking accuracy of the steering actuator motor under both internal parameter and external torque perturbations. The simulations validate the effectiveness of the proposed UniVSR model and AFS controller in improving handling stability.

A new fault location method for high‐voltage transmission lines based on ICEEMDAN‐MSA‐ConvGRU model

AbstractGiven the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.

Dirac fermions collimation in heterostructures based on tilted Dirac cone materials

This paper aims to theoretically analyze the behavior of Dirac fermions in a tilted Dirac cone material, particularly those interacting with a barrier potential. Our results show that the degree of tilt in the y-direction can lead to different collimations of Dirac fermion beams relative to the Fermi and confinement surfaces. We have highlighted a range of results, including the conical geometry by illustrating the active surfaces and their geometric parameters in reciprocal space. To study the transmission probability, we have conducted numerical analyses, considering various system configurations and different external and internal physical parameters to characterize the fermionic transport behavior in a proposed heterostructure. Additionally, we examined the transmission of Dirac fermions in relation to the refractive indices and refraction between the different media constituting the system, discussing the tunneling effect and the Klein paradox in relation to various physical parameters. Our findings lay the groundwork for the development of controllable electronic devices using Dirac fermion collimation, governed by the tilt parameter, enabling precise manipulation and enhanced functionality.

Enhancing residential heating load prediction with advanced machine learning and optimization techniques

The effective prediction of heating load in residential buildings is crucial for optimizing energy consumption and enhancing thermal comfort. This study investigates three prominent regression techniques Extra Tree Regression, Decision Tree Regression, and Support Vector Regression utilized for this purpose. To improve their predictive performance, a novel metaheuristic optimization algorithm, the Tyrannosaurus Rex Optimization Algorithm, is employed. The dataset used includes features such as Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, and Glazing Area Distribution. Each machine learning model (Extra Tree Regression, Decision Tree Regression, Support Vector Regression) is trained on historical heating load data and optimized using the Tyrannosaurus Rex Optimization Algorithm to adjust internal parameters. Comparative analysis demonstrates that the proposed Extra Tree + Tyrannosaurus Rex Optimization Algorithm model achieves superior performance with an R-squared value of 0.987 and a low Root Mean Square Error of 1.167. By leveraging the strengths of different machine learning algorithms, this research presents a reliable approach for accurately predicting heating load in residential buildings. This method holds promise for improving energy efficiency, enhancing thermal comfort, and promoting sustainable building design.

Analyzing false turn-on events with varying gate drive parameters in high voltage GaN devices

In this paper, we address the problem of false turn-on effects in a half-bridge GaN power converter in terms of circuit and device parameters. The model shows that the inherent false turn-on problem is caused by slew rates dvdsdt and dvgsdtduring the switching transients occurring at the turn-on and off phases. A higher slew rate propagates the gate driver voltage to overshoot beyond the threshold voltage causing it to accidentally turn on This study shows that dvdsdt is dependent on the internal device parameters such as gfs (transconductance) and Coss(output capacitance). From the CV characteristics, it is pretty much evident that the internal capacitances Cossand Crss (reverse transfer capacitance) are reduced with higher drain voltage enabling higher slew rates which increases the probability of false turn-on problems. Experimental results at numerous operating points at 400 V with the variation in different gate drive parameters support the analysis.

Influence of Temperature and LED Light Spectra on Flavonoid Contents in Poa pratensis

Light and temperature are the driving forces in plant development and growth. Specific photoreceptors provide the ability to sense and interpret light and temperature to regulate growth. Under the limited light conditions in most sports stadiums, natural grasses suffer from light deficiency. Artificial light provided by light-emitting diodes (LEDs) is used to increase their growth and adjust their development. Flavonoids like flavonols and anthocyanins are influenced by light conditions and temperature. Increased blue light can elevate the content of these secondary metabolites. Remote measurements of internal parameters using non-destructive methods provided information on their content under different temperature conditions for quality monitoring. This experiment tested flavonoid contents in Kentucky bluegrass (Poa pratensis) for different blue-to-red light ratios (0.6 and 0.4) and three temperature courses (constant temperature of 4 °C, constant temperature of 12 °C, and temperature switching among 12–8–4–8–12 °C). The results show elevated levels of flavonoids under blue-dominant artificial light as well as increased content under low-temperature (4 °C) conditions. The lack of flavonoids at elevated temperatures (12 °C), especially under red-dominant light, suggests an increased requirement for artificial blue light at increased temperatures. Non-destructive flavonoid determination was suitable for this experiment and can therefore be used for practical sports turf quality monitoring.

Performances of Large Language Models in Detecting Psychiatric Diagnoses from Chinese Electronic Medical Records: Comparisons between GPT-3.5, GPT-4, and GPT-4o

Objectives: As a type of artificial intelligence (AI), the large language model (LLM) is designed to understand and generate human-like fluent texts. Typical LLMs, e.g., GPT-3.5, GPT-4, and GPT-4o, interact with users through “prompts” and some internal parameters, like “temperature.” Currently, some AI models have been widely used in the field of psychiatry, but systemic reports examining the capacity and suitability of LLM in detecting psychiatry diagnoses are still lacking. In this study, we intended to explore the performances of different generations of LLMs with different levels of temperature in detecting mental illnesses from electronic medical records (EMRs). Methods: We collected 500 Chinese EMRs from one mental hospital in northern Taiwan, with the “current medical history” section as corpuses. We used the GPT-3.5-turbo-16K, GPT-4, and GPT-4o models provided by Microsoft’s Azure OpenAI service (www.portal.azure.com) to generate AI-based predictions (the probability) for the diagnoses of major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autistic spectrum disorder (ASD). Clinic diagnoses made by qualified psychiatrists were treated as gold standards (target) of receiver operating characteristic curve analysis. Then, their area under the ROC curve (AUCs) were compared using the DeLong test. Results: Among 500 recruited Chinese EMRs in this study, 56.6% were primarily diagnosed with MDD, as well as 22.4% with SCZ, 11.2% with ADHD, and 9.2% with ASD. In general, our LLMs achieved AUCs of 0.84 to 0.98 for detecting four different diagnoses. There were no significant differences between versions, but newer versions (GPT-4o models with AUCs of 0.98–0.97 for SCZ, ADHD, and ASD) performed better than older versions (GPT-3.5 models with AUCs of 0.88–0.96) except for MDD (AUC of 0.95 for GPT-4 and AUC of 0.93 for GPT-4o). Although DeLong tests showed nonsignificant differences between the AUCs of models with different levels of temperature, models with zero temperatures generally represented the best performances in magnitudes. Conclusion: To the best of our knowledge, this study is the first to demonstrate that LLMs performed excellently in distinguishing some mental illnesses. Nevertheless, the diagnostic capabilities of LLMs differed from other diagnoses such as MDD. We hypothesize that this phenomenon may partially result from the complexity of symptomology and/or the content filtering rules of OpenAI. Therefore, more advanced models, e.g., GPT-5, or private training models, e.g., Llamma 3, with the relevance generative answering technique, are expected to answer our questions.

Rogue curves in the Davey-Stewartson I equation.

We report new rogue wave patterns whose wave crests form closed or open curves in the spatial plane, which we call rogue curves, in the Davey-Stewartson I equation. These rogue curves come in various striking shapes, such as rings, double rings, and many others. They emerge from a uniform background (possibly with a few lumps on it), reach high amplitude in such striking shapes, and then disappear into the same background again. We reveal that these rogue curves would arise when an internal parameter in bilinear expressions of the rogue waves is real and large. Analytically, we show that these rogue curves are predicted by root curves of certain types of double-real-variable polynomials. We compare analytical predictions of rogue curves to true solutions and demonstrate good agreement between them.

Physical Quality Parameters and Activity of Albumen Lysozyme of Eggs From Traditional Hens Bred in Poland Compared With Commercial Hybrids

Abstract The variation in egg quality parameters of traditional/native hen breeds with good freshness and antibacterial properties of egg albumen may determine the attractiveness of these eggs for modern consumers looking for high quality products. The objective of this study was to evaluate external and internal physical quality parameters and enzymatic activity of lysozyme of eggs from three traditional hens bred in Poland and to compare these egg quality parameters to those obtained from commercial hybrids kept under the same management conditions in extensive farming production system. The study was carried out on eggs collected from 4 genetic groups of hens, i.e. Polish Liliputy Bantams (PLB), native Polish Crested Chickens (PCr, CP-22 strain), Gold Laced Polish Chickens (GLP) and from Hy–Line Brown hybrids (HLB). In total 135 PLB, 75 PCr, 75 GLP and 75 HLB hens were kept on litter (3 pens) and fed commercial feed. The eggs (n = 33/genotype/age) were collected at the 33 and 55 weeks of age. The study concluded that with the age of the hens, there was an increase (P<0.05) in the weight of the egg and the proportion of its main fractions, as well as a decrease (P<0.05) in the quality parameters of the albumen and the eggshell. However the eggs from traditional breeds retain good parameters of albumen quality even at the end of production period. The eggs of traditional hens were characterized by higher (P<0.05) yolk proportion and its color and albumen lysozyme parameters, and a lower (P<0.05) eggshell and albumen height and Haugh unit score in relation to commercial hybrids. The highest (P<0.05) content of lysozyme and enzymatic activity of lysozyme were found in eggs from Gold Laced Polish Chickens. Thus, eggs obtained from traditional hens may meet requirements of modern consumers because of specific physical characteristics, good albumen lysozyme activity and freshness parameters. Particularly noteworthy are Polish Liliputy Bantams eggs, characterized by the low weight and a high proportion of more intensely colored yolk and good Haugh unit score at the end of production period.

Effect of variable conditions on transient flow in a solid–liquid two-phase centrifugal pump

To investigate the influence of altering operational parameters on the transient flow characteristics within the flow channel of a solid–liquid two-phase centrifugal pump, this study employs a particle model based on the Euler–Euler method. Utilizing the standard k–e model, flow field simulations are conducted using the ANSYS-CFX software. Specifically, the study focuses on throttle regulation scenarios, monitoring and comparing the external and internal flow parameters of the solid–liquid two-phase pump with those of a clear water medium centrifugal pump. The results indicate notable modifications in head, efficiency, and shaft power due to the presence of solid particles in the two-phase flow. Decreasing flow rates during throttle regulation lead to fluctuations in pressure and turbulence energy distribution. Furthermore, under identical operational conditions, the variable working conditions of the solid–liquid pump result in increased flow rates of solid-phase particles near the impeller's outer edge, with particles shifting toward the middle and tail of the vane suction surface. This phenomenon exacerbates wear on the vane tail of the suction surface. Moreover, the study identifies that changing operational conditions in the solid–liquid dual-flow centrifugal pump contribute to increased axial forces, consequently leading to pump vibrations. Overall, this research elucidates the transient flow characteristics within the flow channel of solid–liquid two-phase centrifugal pumps under varied operational conditions, serving as a foundational reference for assessing the stability of such pumps.

3D Vibration Analysis using Phase-Based Displacement Estimation and Stereo Cameras

Non-contact sensors are increasingly integral to the field of Structural Health Monitoring (SHM), with camera being widely utilized due to their high spatial resolution and cost-effectiveness in measuring structural displacements. Phase-based optical flow (POF) techniques have proven valuable for accurate and computationally efficient 2D vibration measurements. However, these techniques are typically confined to single-camera systems and encounter challenges in measuring vibrations in out-of-plane directions. Previous research on 3D vibration measurement has predominantly centered on pixel intensity-based techniques using stereo camera systems. Although these methods demonstrate high performance in calculating global strains and stresses, they are less suitable for accurate vibration measurement. To address these limitations, this study proposes a novel 3D vibration measurement approach that integrates POF within the context of stereo camera systems. 2D vibrations are extracted by applying Complex Gabor filters to stereo images, and subsequently, 3D vibration measurements are estimated through camera calibration and triangulation, using computed camera internal and external parameters. To validate the method, modal analysis experiments are conducted on an aluminum plate. The 3D vibration measurement results are compared with those obtained through 3D Digital Image Correlation (DIC) and contact sensors and the accuracy is demonstrated.

Parameter Estimation and Preliminary Fault Diagnosis for Photovoltaic Modules Using a Three-Diode Model

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference.

A Hierarchical Nano to Micro Scale Modelling of 3D Printed Nano-Reinforced Polylactic Acid: Micropolar Modelling and Molecular Dynamics Simulation.

Fused deposition modelling (FDM) is an additive manufacturing technique widely used for rapid prototyping. This method facilitates the creation of parts with intricate geometries, making it suitable for advanced applications in fields such as tissue engineering, aerospace, and electronics. Despite its advantages, FDM often results in the formation of voids between the deposited filaments, which can compromise mechanical properties. However, in some cases, such as the design of scaffolds for bone regeneration, increased porosity can be advantageous as it allows for better permeability. On the other hand, the introduction of nano-additives into the FDM material enhances design flexibility and can significantly improve the mechanical properties. Therefore, modelling FDM-produced components involves complexities at two different scales: nanoscales and microscales. Material deformation is primarily influenced by atomic-scale phenomena, especially with nanoscopic constituents, whereas the distribution of nano-reinforcements and FDM-induced heterogeneities lies at the microscale. This work presents multiscale modelling that bridges the nano and microscales to predict the mechanical properties of FDM-manufactured components. At the nanoscale, molecular dynamic simulations unravel the atomistic intricacies that dictate the behaviour of the base material containing nanoscopic reinforcements. Simulations are conducted on polylactic acid (PLA) and PLA reinforced with silver nanoparticles, with the properties derived from MD simulations transferred to the microscale model. At the microscale, non-classical micropolar theory is utilised, which can account for materials' heterogeneity through internal scale parameters while avoiding direct discretization. The developed mechanical model offers a comprehensive framework for designing 3D-printed PLA nanocomposites with tailored mechanical properties.

FORECASTING SHORT-TIME LOAD DEMAND BY NON-LINEAR AUTOREGRESSIVE WITH EXTERNAL INPUTS

Accurate estimation of the load demand in each period could be a useful approach to implement necessary policies for better management in the load supply-consumption chain. Hourly load demand is a function of external parameters (such as calendar data) and internal parameters (load demand). Nonlinear Autoregressive with External Inputs (NARX) was selected because of the ability to use external and internal inputs. The model was trained and evaluated using the hourly load demand data for Shahrekord city in a four-year period. The novelties of this study include (i) identifying essential factors that have a significant effect on load demand and (ii) Reduce model inputs (parsimonious model). By analysis of variance (ANOVA), external factors were selected to increase the accuracy of the model in addition to simplicity. External inputs of the developed model included calendar and holiday data. Unlike other existing models, the number of inputs were reduced but the days classification was increased from two groups (working day and holiday) to three groups (working day, semi-holiday and holiday). These changes caused, in addition to increasing the accuracy of the model, the number of inputs to be reduced. The mean absolute percentage error average (MAPE) is 0.983%, which shows higher accuracy than similar models.

Double-diode model carrier lifetime-based internal recombination parameter analysis and efficiency prediction of crystalline Si solar cells

With the continuous and significant advancements in the performance of crystalline silicon (c-Si) solar cells, an effective method for a detailed analysis of the efficiency loss factors is increasingly important. Specifically, a detailed analysis of recombination parameter (J0) is crucial for devising strategies to approach the theoretical efficiency limit. In this study, we propose an analysis method that utilizes minority carrier lifetime based on the double-diode model and predicts the performance efficiency of c-Si solar cells. The analysis, conducted through the p-PERC structure, examines the contributions to J0 from different recombination areas. The advantages of this method include: (i) its relative simplicity and reliance on non-destructive analysis techniques; (ii) its capability to extract internal recombination parameters, such as J0,total emitter, J0,rear, and J02, from a fully assembled solar cell with metal contacts. Furthermore, this method serves as a diagnostic tool to identify the sequence in which parameters should be adjusted to enhance solar cell efficiency effectively and to pinpoint areas within the solar cell that significantly limit performance efficiency. Using this method, the diagnosis of the p-PERC cell revealed that the main cause of performance limitation was the emitter in the front region. The carrier lifetime analysis showed that for overcoming cell performance limitations, it is more effective to design process improvements in the following order: i) front emitter region (J0,em), ii) space charge region (J02), iii) rear surface region (J0r).

High power impulse magnetron sputtering of a zirconium target

High power impulse magnetron sputtering (HiPIMS) discharges with a zirconium target are studied experimentally and by applying the ionization region model (IRM). The measured ionized flux fraction lies in the range between 25% and 59% and increases with increased peak discharge current density ranging from 0.5 to 2 A/cm2 at a working gas pressure of 1 Pa. At the same time, the sputter rate-normalized deposition rate determined by the IRM decreases in accordance with the HiPIMS compromise. For a given discharge current and voltage waveform, using the measured ionized flux fraction to lock the model, the IRM provides the temporal variation of the various species and the average electron energy within the ionization region, as well as internal discharge parameters such as the ionization probability and the back-attraction probability of the sputtered species. The ionization probability is found to be in the range 73%–91%, and the back-attraction probability is in the range 67%–77%. Significant working gas rarefaction is observed in these discharges. The degree of working gas rarefaction is in the range 45%–85%, higher for low pressure and higher peak discharge current density. We find electron impact ionization to be the main contributor to working gas rarefaction, with over 80% contribution, while kick-out by zirconium atoms and argon atoms from the target has a smaller contribution. The dominating contribution of electron impact ionization to working gas rarefaction is very similar to other low sputter yield materials.

Untying black boxes with clustering-based symbolic knowledge extraction

Machine learning black boxes, exemplified by deep neural networks, often exhibit challenges in interpretability due to their reliance on complicated relationships involving numerous internal parameters and input features. This lack of transparency from a human perspective renders their predictions untrustworthy, particularly in critical applications. In this paper, we address this issue by introducing the design and implementation of CReEPy, an algorithm for symbolic knowledge extraction based on explainable clustering. Specifically, CReEPy leverages the underlying clustering performed by the ExACT or CREAM algorithms to generate human-interpretable Prolog rules that mimic the behaviour of opaque models. Additionally, we introduce CRASH, an algorithm for the automated tuning of hyper-parameters required by CReEPy. We present experiments evaluating both the human readability and predictive performance of the proposed knowledge-extraction algorithm, employing existing state-of-the-art techniques as benchmarks for comparison in real-world applications.

An innovative AR-assisted self-adjusting digital twin for speed control of a servomechanism

A machine might undergo changes in its system due to prolonged operation. These changes can significantly impact the performance and behavior of the machine’s controller. The general digital twin solutions typically use compensators to adjust the physical entity instead of updating the internal model parameters of the digital twin. Therefore, if deviations are significant, the physical entity may never fully match its digital twin. To address potential degradation in controller performance, this study introduces an AR (augmented reality)-assisted self-adjusting digital twin (SADT) servomechanism monitoring system. The primary objective of this system is to provide real-time monitoring, maintenance, and adjustments to ensure the optimal operation of the machine’s controller, even in the presence of significant deviations caused by extended operation or environmental changes. In smart manufacturing, servomechanisms are often used in wheeled mobile robots and conveyor mechanisms. Hence, this study selects servomechanisms as the target application. The AR-assisted SADT accomplishes this by comparing the angular velocity error between the servomechanism and its digital twin to identify when significant changes have occurred. Upon detecting such a change, the SADT calculates new internal model parameters, generating a modified digital twin. This allows resynchronization with the changed physical entity, restoration of the virtual-physical mapping, and continuous, accurate monitoring and optimized control. Furthermore, to enhance human–machine interaction, AR technology is used to offer users visual information, enabling intuitive monitoring of the servomechanism’s performance. In summary, the AR-assisted SADT system makes human–machine interaction more intuitive for operators and enhances the adaptability and accuracy of system monitoring.

Coupled internal rotations and 14N quadrupole hyperfine structure of 2,4-dimethylpyrrole investigated by microwave spectroscopy and quantum chemistry.

The microwave spectrum of 2,4-dimethylpyrrole was investigated using a Fourier-transform microwave spectrometer in a supersonic expansion. Torsional splittings arising from two inequivalent methyl internal rotors in combination with hyperfine splittings due to the nuclear quadrupole coupling of the 14N nucleus were observed. The experiments were accompanied by quantum chemical calculations. A total of 1561 rotational lines were assigned and fitted in global fits using the programs XIAM and BELGI-Cs-2Tops-hyperfine, both achieved the measurement accuracy of 4kHz. Local separate fits were also performed to verify the correctness of the assignment. Accurate experimental molecular and internal rotation parameters could be deduced and compared to the calculated ones. The barrier to internal rotation of the 2-methyl rotor was determined to be 277.830(26) cm-1, essentially the same as the value of about 280cm-1 found for 2-methylpyrrole but lower than the value of 317cm-1 found for 2,5-dimethylpyrrole. The torsional barrier value of the 4-methyl rotor is 262.210(27) cm-1, slightly higher than the value of 246cm-1 found for 3-methylpyrrole. Benchmarking the rotational constants for 2,4- and 2,5-dimethylpyrrole revealed that the MP2/6-31G(d,p) level could be helpful to guide the assignment of microwave spectra of pyrrole derivatives.

Robust Online Estimation of State of Health for Lithium-Ion Batteries Based on Capacities under Dynamical Operation Conditions

Lithium-ion batteries, as the main energy storage component of electric vehicles (EVs), play a crucial role in ensuring the safe and reliable operation of the battery systems through monitoring their state of health (SOH). However, temperature variations and battery aging have significant impacts on the internal parameters of lithium-ion batteries, and these changes directly correlate with the accuracy of battery SOH estimation. To address these issues, this paper proposes an estimation method for lithium-ion battery SOH that considers the impact of temperature. The method begins with reconstructing a second-order hybrid equivalent circuit model for lithium-ion batteries, through which an adaptive update rate for battery model parameters is designed. On this basis, a nonlinear observer for battery states is introduced by integrating filters to estimate SOH. The proposed method considers the impact of capacity in the design of the parameter adaptive update rate, enabling the capacity to be dynamically adjusted based on the actual state of the batteries. This reduces the cumulative error in the SOC observer and improves the modeling accuracy of battery models. Experimental results demonstrate that the method proposed in this paper exhibits exceptional performance in SOH estimation under different temperature conditions. The mean absolute error for SOH estimation does not exceed 0.5%, and the root mean square error does not exceed 0.2%. This method can significantly improve the estimation accuracy of SOH, providing a more efficient and accurate solution for battery management systems in EVs.