Search Results (90)

Precise electromagnetic torque estimation for permanent magnet synchronous motors (PMSMs) is crucial for enhancing the dynamic performance and energy efficiency of electric vehicles. To address the dynamic variations in dq-axis inductance caused by magnetic cross-coupling and saturation effects during motor operation—which lead to significant torque estimation errors in traditional fixed-parameter models under variable torque and speed conditions—this paper proposes a dynamic torque estimation method that integrates online dq-axis inductance identification based on a variable-step adaptive linear neural network (ADALINE) with an extended flux observer. The online identified inductance values are embedded into the extended flux observer in real time, forming a closed-loop torque estimation system with adaptive parameter updating. Experimental results demonstrate that, under complex operating conditions with varying torque and speed, the proposed method maintains electromagnetic torque estimation errors within ±3%, with a convergence time of less than 20 ms, while achieving inductance identification accuracy also within ±3%. These results significantly outperform conventional methods that do not incorporate inductance identification. This study provides a highly adaptive and engineering-practical solution for high-precision torque control of interior permanent magnet synchronous motors (IPMSMs) in automotive applications. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

The adoption of Interior Permanent Magnet Synchronous Motor (IPMSM) in Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV) drives the need for innovative approaches to improve control performance and power conversion efficiency. This paper aims at evaluating advanced Model Predictive Control (MPC) strategies for IPMSM drives in a methodic comparison with the most widespread Field Oriented Control (FOC). Different extensions of direct Finite Control Set MPC (FCS-MPC) and indirect Continuous Control Set MPC (CCS-MPC) MPCs are considered and evaluated in terms of reference tracking performance, robustness, power efficiency, and complexity based on Matlab, Simulink™ simulations. Results confirm the inherent better control quality of MPCs over FOC in general and allow us to further identify some possible directions for improvement. Moreover, indirect MPCs perform better, but complexity may prevent them from supporting real-time implementation in some cases. On the other hand, direct MPCs are less complex and reduce inverter losses but at the cost of increased Total Harmonic Distortion (THD) and decreased robustness to parameters deviations. These results also highlight various trade-offs between different predictive control strategies and their feasibility for high-performance automotive applications. Full article

Banana fibres (BFs), derived from the pseudo-stems of Musa acuminata, represent a widely available agricultural residue with strong potential as an eco-friendly reinforcement in composite materials—particularly in bio-based epoxy or thermoplastic systems used in automotive interiors, packaging, and lightweight construction. However, their inherent variability presents challenges for consistent and reliable mechanical characterisation. This study investigates the effect of wood ash treatment, an eco-friendly alternative to conventional alkaline processing, on the tensile strength of single BFs. Fibres were treated in aqueous wood ash solutions at two pH levels (12.4 and 13.5) and soaking durations of 3 h and 24 h, and then tested according to ASTM C1557. At least 50 valid tensile tests per series were performed, and the results were analysed using a two-parameter Weibull distribution to quantify characteristic strength and variability, complemented by reliability analysis to assess survival probability. Untreated fibres exhibited low characteristic strength (396.6 MPa) and a Weibull modulus of 1.79, confirming significant scatter. Treated fibres showed marked improvements: the highest characteristic strength was achieved at pH 13.5 for 3 h (552.8 MPa, m = 3.17), while the greatest uniformity was observed at pH 13.5 for 24 h (m = 4.62). Reliability curves confirmed superior performance of treated fibres, with 75% survival strengths up to 373 MPa compared to 198 MPa for untreated. These findings demonstrate that wood ash treatment enhances both the strength and reliability of BFs for sustainable composite applications. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

Electric vehicles now play a critical and promising role in the automotive industry. This study presents how electric car interiors innovatively appeal to consumers’ needs, influencing their preference for interior design based on essential features. It investigates why consumers prefer the interior design of electric vehicles and what specific characteristics influence these preferences from the perspective of innovation. This study applies a preference-based research method to determine the significance of the innovative appeal of electric cars. The evaluation grid method is applied to interpret experts’ professional insights, which are outlined using a semantic hierarchical diagram of electric vehicle interiors. This study also conducts a questionnaire survey based on consumers’ reactions and analyzes their answers using Quantification Theory Type I. The four key original evaluation items for electric car interiors are determined as “tasteful,” “avant-garde,” “technical innovation,” and “sustainable innovation.” These four factors can be applied using their corresponding reasons and characteristics. This study contributes critical suggestions for interior designers and researchers of electric vehicles. The study also provides useful information on user-centered interaction design, sustainability, and consumer psychology. Full article

This study investigates the use of bio-based polyurethane foams (PUFs) containing phase change material (PCM) microparticles as a sustainable alternative for the automotive sector. These foams are synthesized using polyols derived from waste cooking oil (WCO), aligning with circular economy principles. To evaluate the thermophysical properties of these materials and, more in general, their thermal behavior, the use of non-destructive thermographic techniques has been proposed. This technique enables a rapid, full-field thermal analysis without physical contact, making it especially suitable for porous and heterogeneous structures like foams. As a reference, both virgin and foams with PCM were characterized in terms of density and thermal conductivity using well-established methods. Then, Lock-in thermography has been used as the first attempt technique to investigate variations in thermal behavior due to different thermophysical material properties based on the thermal response in transmission configuration. The thermographic approach proves to be an effective tool not only for assessing thermal behavior but also for supporting quality control and process optimization of sustainable polymeric materials. Full article

In addressing the challenges posed by the numerous rotor structure parameters and the difficulty in analyzing the air gap magnetic field distribution in interior permanent magnet (IPM) motors, and to enhance the performance of automotive IPM synchronous motors, this paper proposes a multi-objective optimization method based on sensitivity stratification. Firstly, sensitivity analysis is conducted on the positional and shape parameters of the rotor permanent magnets (PMs), and the parameters are stratified according to their sensitivity levels. Subsequently, distinct analysis and optimization methods are applied to parameters of different strata for dual-objective optimization, which aims to increase the amplitude of the air gap flux density and reduce its total harmonic distortion (THD). Moreover, the waveform of the air gap flux density is analyzed to propose a targeted arrangement of magnetic isolation slots, thereby further optimizing the magnetic field distribution. Meanwhile, the demagnetization conditions and influencing factors of the PMs under overload are analyzed to enhance their demagnetization resistance and determine the final structural parameters. Simulation results indicate that, with the application of the proposed optimization method, the fundamental amplitude of the air gap flux density is increased by 0.035 T and THD is decreased by 9.9% when the proposed optimization method is applied. This verifies the effectiveness and feasibility of the method. Full article

The automotive industry increasingly relies on 3D modeling technologies to design and manufacture vehicle components with high precision. One critical challenge is optimizing the placement of latches that secure the dashboard side paneling, as these placements vary between models and must maintain minimal tolerance for movement to ensure durability. While generative artificial intelligence (AI) has advanced rapidly in generating text, images, and video, its application to creating accurate 3D CAD models remains limited. This paper proposes a novel framework that integrates a PointNet deep learning model with Python-based CAD automation to predict optimal clip placements and surface thickness for dashboard side panels. Unlike prior studies that focus on general-purpose CAD generation, this work specifically targets automotive interior components and demonstrates a practical method for automating part design. The approach involves generating placement data—potentially via generative AI—and importing it into the CAD environment to produce fully parameterized 3D models. Experimental results show that the prototype achieved a 75% success rate across six of eight test surfaces, indicating strong potential despite the limited sample size. This research highlights a clear pathway for applying generative AI to part design automation in the automotive sector and offers a foundation for scaling to broader design applications. Full article

Automotive interior lighting has progressed from basic functional illumination to sophisticated aesthetic systems emphasizing chromatic stability under thermal variations. This study enhances an RGB temperature compensation algorithm for LEDs, extending it to an RGBW solution. While several approaches for LED temperature compensation have been proposed in the literature, none have addressed a complete RGBW solution where the white channel is derived and actively adjusted on thermal variations. This research aims to fill this gap by extending an RGB algorithm to RGBW and validating it under realistic automotive conditions. While the proposed compensation strategies are general and may be applied to other LED systems, the automotive interior lighting domain has been selected as a representative case study because it combines stringent chromatic stability requirements ($\Delta u^{\prime }{v}^{\prime }\le 0.01$) and high industrial relevance. Leveraging Infineon’s LITIX™ LED drivers, experimental results show that the algorithm maintains chromatic stability with deviations below $\Delta u^{\prime }{v}^{\prime }=0.00562$ in RGB mode and $\Delta u^{\prime }{v}^{\prime }=0.0067$ in RGBW mode across the tested temperature range. The addition of the white channel improves the color rendering index (CRI) by up to 58.9 points (from 19.7 to 78.6) while preserving color quality. Compared to previous works limited to RGB systems, our approach provides the first practical RGBW compensation algorithm experimentally validated under realistic automotive conditions. Full article

The estimation of core loss in permanent magnet synchronous machines (PMSMs) is a fundamental step for the optimization of the performance of PMSM drives. However, there is a lack of literature which fully demonstrates the goodness of some of the empirical approximations that are commonly used in industrial and automotive sectors. This work investigates how different approximations for the core loss estimation of PMSMs can lead to considerable error across the entire machine operating domain. An interior PMSM (IPMSM) is modeled in finite element analysis (FEA) and used to calibrate the coefficients of the Bertotti equation. Approximations of the Bertotti equation are then made, which are calculated from a simple algebraic expression of measurable states, and these are used to estimate machine core loss in the whole direct-quadrature ($d-q$) domain of operation. The estimated core loss obtained with the approximations are finally compared to FEA core loss results. The approximations are shown to have considerable variability in their accuracy compared to FEA results. The results of this work can be used as guidance during the development of estimation algorithms for PMSM losses and the development of control strategies. Full article

In this study, alkali-treated wood flour/dynamic polyurethane composites were successfully prepared through a solvent-free one-pot method and in situ polymerization. The effects of the alkaline treatment process, changes in the flexible long-chain content in the dynamic polyurethane system, and the wood flour filling amount on the interface’s bonding, mechanical, and reprocessing properties were investigated. Partial removal of lignin and hemicellulose from the alkali-treated wood flour enhanced rigidity and improved interface bonding and mechanical strength when combined with dynamic polyurethane. The tensile strength was improved from 5.65–11.00 MPa to 13.08–23.53 MPa. As the composite matrix, dynamic polyurethane could not easily infiltrate all wood flour particles when its content was low or its fluidity was poor. Conversely, excessive content or overly high fluidity led to leakage and the formation of large pores, affecting the mechanical strength. As the polyol content increased, the matrix exhibited greater fluidity, which enabled it to accommodate more wood flour and penetrate the cell cavity or even the cell wall. This improved infiltration enhanced the interface bonding performance of the composites and made their mechanical properties sensitive to changes in wood flour content. The reprocessing ability of the prepared composites decreased with the increase in wood flour content, and the interface bonding was enhanced after reprocessing. The tensile strength retention rate of the composites prepared with alkali-treated wood flour was lower. This study provides a theoretical basis for optimizing the performance of wood fiber/dynamic polyurethane composites and an exploration path for developing self-healing and recyclable wood–plastic composites, which can be applied to building materials, automotive interiors, furniture manufacturing, and other fields. Full article

The growing development of conductive functionalised textiles has attracted the interest of the automotive industry, which is seeking innovative solutions for seamless and futuristic interior design aimed at improving both vehicle aesthetics and user experience. In line with this trend, the present work investigates the electrical performances of two conductive flocked yarns, one incorporating silver-coated fibres and the other carbon black-based fibres, for potential application in smart automotive interiors. The stability of their electrical properties was also evaluated under thermal ageing and mechanical stress conditions. Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and field emission scanning electron microscopy (FE-SEM) investigations provided information about the composition and structural properties of the yarns. Silver-based yarns demonstrated superior conductivity and thermal stability. In contrast, carbon-black yarns exhibited lower electrical performance and increased sensitivity to ageing due to filler agglomeration. A multitouch capacitive sensor prototype was also developed using the silver-based fabric and successfully integrated into a microcontroller platform. The results demonstrate the suitability of conductive flocked textiles for durable, low-voltage human–machine interfaces requiring robust, flexible, and responsive textile-based control surfaces, such as automotive applications, consumer electronics, and wearable technology. Full article

Car interior design should evoke emotions, offer comfort, convey safety and at the same time project the brand identity of the car manufacturer. Lighting is used to address these functions. Modules required for automotive interior lighting often feature injection-moulded (IM) light guides, whereas woven fabrics with polymer optical fibres (POFs) offer certain technological advantages and show first-series applications in cars. In the future, car interior illumination will become even more important in the wake of megatrends such as autonomous driving. Since the increase in deployment of these technologies facilitates a need for an economical comparison, this paper aims to deliver a cost-driven approach to fulfil the aforementioned objective. Therefore, the cost structures of the supply chains for an IM-based and a POF-based illumination module are analysed. The employed research methodologies include an activity-based costing approach for which the data is collected via document analysis and guideline-based expert interviews. To account for data uncertainty, Monte Carlo simulations are conducted. POF-based lighting modules have lower initial costs due to continuous fibre production and weaving processes, but are associated with higher unit costs. This is caused by the discontinuous assembly of the rolled woven fabric which allows postponement strategies. The development costs of the mould generate high initial costs for IM light guides, which makes them beneficial only for high quantities of produced light guides. For the selected scenario, the POF-based module’s self-costs are 11.05 EUR/unit whereas the IM module’s self-costs are 14,19 EUR/unit. While the cost structures are relatively independent from the selected scenario, the actual self-costs are highly dependent on boundary conditions such as production volume. Full article

This review article focuses on the long-term durability challenges associated with bamboo fiber-reinforced polymer composites when subjected to various environmental aging conditions such as water immersion, hygrothermal fluctuations, ultraviolet (UV) radiation, soil burial, and refrigerated storage. The primary issue addressed is the degradation of mechanical and structural performance of bamboo fiber-reinforced polymer composites due to moisture absorption, fiber swelling, and fiber–matrix interface deterioration. To mitigate these aging effects, the study evaluates and compares multiple strategies, including chemical and physical fiber surface treatments, filler additions, and fiber hybridization, which aim to enhance moisture resistance and mechanical stability. These composites are relevant in automotive interiors, construction panels, building insulation, and consumer goods due to their eco-friendly nature and potential to replace conventional synthetic composites. This review is necessary to consolidate current knowledge, identify effective enhancement approaches, and guide the development of environmentally resilient bamboo fiber-reinforced polymer composites for real-world applications. Full article