Car Model Research Articles

Hybrid battery thermal management system of phase change materials integrated with aluminum fins and forced air

AbstractDue to its high self‐heat rate, most researchers have avoided using lithium cobalt oxide (LiCoO2) in their work, although, major car companies use it to power some car models because of its high‐power density. A thermal management system benefits from phase change material (PCM) and serves as a reliable cooling system to ensure the safety, performance, and lifespan of Li‐ion batteries. In this study, we conducted an experimental investigation of a new hybrid battery thermal management system (BTMS) using PCM combined with aluminum fins and forced air to enhance the cooling performance of Li‐ion battery type 18 650 LiCoO2. Furthermore, the hybrid model's thermal behaviors are compared with other models that use only air or PCM for cooling. The cooling performance of different BTMS models was tested under a high temperature of 40°C and various discharge rates, as well as, various air velocities. The results demonstrate that the hybrid model effectively minimizes the battery heat accumulation and can reduce the maximum operating temperature by 1.5°C, 5.5°C, and 9.5°C compared to the air‐cooling model and by 2.8°C, 5.1°C, and 16.1°C compared to the PCM model for discharge of 1C, 2C, and 3C rates, respectively. Furthermore, the maximum temperature difference within the battery pack did not surpass 3.1°C with our hybrid model. Moreover, the use of our model has a significant advantage in minimizing the air‐cooling power consumption by 89%.

Efficient wireless power transfer for a moving electric vehicle by digital control of frequency

Recently, demand for electric vehicles has been increasing as a countermeasure against global warming, but they currently face many problems compared to gasoline-powered vehicles. For example, charging takes time, and there are few places where electric vehicles can be charged. If AC power supplies that can transfer energy to electric vehicles wirelessly exist under the lanes where electric vehicles drive, the cruising range will be increased. In this study, assuming wireless power transfer to a moving electric vehicle, an experiment was conducted to light up a light-emitting diode (LED) on a moving electric model car. To improve the efficiency of transfer, the optimal frequency for the position of the electric model car was calculated, and the value was fed back to the power supply to adjust the frequency in real time.

Modeling and optimization of car suspension system in the presence of magnetorheological damper using Simulink-PSO hybrid technique

The suspension system is one of the important parts of the vehicle, which plays a substantial role in the steering and comfort characteristics of it. In the present research work, the optimization of the suspension system parameters has been carried out with the aim of reducing the vehicle vibrations. In this study, the magnetorheological damper (MR damper) is added to the one-half car model with four degrees of freedom and the effects of improving the efficiency of the suspension system have been investigated. In order to model the vehicle system, MATLAB/Simulink software is employed. The particle swarm optimization (PSO) algorithm has been applied to optimize the variables of the suspension system in order to decrease the vehicle vibrations and passenger comfort. The optimization of suspension system variables has been done for two bond-limited white noise and sinusoidal wave inputs. The outcomes revealed that the addition of MR damper in any mode reduces the vehicle vibrations. The maximum reduction in the vehicle vibrations was related to the state where all the variables of the suspension system were optimized and the amount of the objective function in that state was improved by about 32%.

A decision-support model through online reviews: Consumer preference analysis and product ranking

Online reviews have become an important reference for consumers when choosing and purchasing products (or services); however, previous research based on these reviews has overlooked consumer preferences at the attribute level, as well as product ranking at the regional level. Here, we propose a decision-support model through online reviews involving 4 modules: (i) data collection and preprocessing, (ii) transformation from star ratings into probabilistic linguistic term sets (PLTSs), (iii) consumer preference analysis, and (iv) product ranking. After data collection and preprocessing, star ratings are represented as probabilistic linguistic term sets. Then, the most satisfactory product attribute and the least satisfactory product attribute are extracted from text comments using the well-known term-frequency-inverse document frequency (TF-IDF) algorithm. Thereafter, a data-driven best-worst method, in which preferences are given by objectively quantifying multiple product attributes based on star ratings and the weightage score obtained from the TF-IDF results, is established to analyze consumer preferences. Furthermore, product ranking is investigated from two levels: a region-based ranking method to discuss product ranking over different regions and a ranking position aggregation method to discuss product overall ranking based on a 0-1 integer programming model. We verify the decision-support model with a dataset of 10987 reviews of 20 automobile models from the Autohome platform. Some comparisons, theoretical and practical implications are provided.

Analysis sedan vehicle structure in frontal impact using computer model

This paper presents an overview of Sedan vehicle structure based on frontal impact. The analysis results include parameters on the degree of deformation, the collision absorption ability of the Sedan vehicle structural model in accidents. The purpose is based on the comparison results to simulate the Sedan vehicle structure model that collides with other car chassis models at different speed levels, based on that, compares the crash analysis simulations to find the textures the frame absorbs the maximum impact energy and minimizes the deformation so as not to affect intrusion into the safe space of the passenger compartment of the vehicle. The results of this paper show that the frame structures such as the A-Pillars and Horizontal brace between the engine compartment and the passenger compartment need to be improved to increase the safety for the passengers inside. The study results are expected to be expanded in to optimize the engineering structures and improve the efficiency in various fields included shipbuilding, offshore structures, marine aquaculture structures, etc. Keywords: Structural, analysis, Sedan, vehicle structure, fontal impact.

Abstract 2913: Co-expression of extracellular matrix enzymes heparanase or PH-20 augments the anti-tumor efficacy of folate receptor 1-targeting CAR T cells in an ovarian cancer model

Abstract Introduction: The tumor extracellular matrix (ECM) is a physical barrier which obstructs trafficking of CAR T cells to tumors. We hypothesize that co-expressing ECM remodeling/degrading enzymes heparanase (HPSE) or PH-20 with a CAR directed against folate receptor 1 (FolR1) will overcome the physical tumor microenvironment barrier and improve anti-tumor efficacy of anti-FolR1 CAR in a human ovarian cancer model. Methods: ECM CARs were constructed by combining anti-FolR1 ScFv with a CD8 hinge and transmembrane domain, 4-1BB, and CD3ζ endodomains, followed by a P2A element and full-length HPSE or GPI-anchored PH-20 sequence. CAR T cells were prepared from CD4+ and CD8+ T cells of healthy donors by lentiviral transduction. CAR expression was evaluated by flow cytometry; HPSE and PH-20 expression were measured via ELISA and Western blotting, respectively. CAR-mediated killing and cytokine release were observed in overnight co-culture assays with FolR1-expressing tumor cells. ECM CARs’ migration through CultrexTM and sodium hyaluronate matrices were assessed for HPSE and PH-20 function, respectively. ECM CARs’ efficacy was evaluated in vivo using a FolR1-expressing subcutaneous OV90 ovarian cancer xenograft model in NOD SCID Gamma (NSG) mice, and representative OV90 tumors were harvested at day 7 post-CAR T treatment for immunohistochemical staining. Results: FolR1 expression was confirmed in ovarian cancer cell lines and substrates targeted by HPSE (perlecan, glypican 3, CD138) and PH-20 (hyaluronan) were detected by immunofluorescence in human ovarian tumors’ sections. Lentiviral transduced primary T cells yielded robust expression of FolR1 CAR and ECM enzymes. Anti-FolR1 CAR and ECM CARs secreted pro-inflammatory cytokines IL-2, TNFα, IFNƳ and lysed OVCAR3 tumor cells with similar efficacy. ECM CARs with HPSE or PH-20 exhibited enhanced migration through ECM-rich matrices as compared to anti-FolR1 CAR alone. In vivo, all anti-FolR1 CAR T cells rejected OV90 tumors and extended survival compared to tumor alone or untransduced T cells (UTD). However, ECM CARs co-expressing HPSE or PH-20 accelerated tumor regression as compared to anti-FolR1 CAR alone. Tumor histology revealed CAR T cell infiltration (CD3+, CD8+) in mice treated with anti-FolR1 CARs but not UTD, indicating that anti-FolR1 CARs can effectively infiltrate OV90 tumors. Imaging studies are underway to evaluate the function of ECM CARs in the tumor microenvironment. Conclusions: The anti-FolR1 CARs boosted with ECM enzymes HPSE or PH-20 demonstrated specific lysis of FolR1-expressing cell lines, improved penetration through ECM-rich matrices in vitro, and accelerated tumor regression in an ovarian cancer model in vivo. Therefore, ECM CARs may help improve clinical outcomes in patients with ovarian tumors. Citation Format: Brittany L. Steimle, Peirong Hu, Ngoc Tran, Jia-Jye Lee, Kathleen Baysac, Kun Luo, Xu Wang, Genqing Liang, Saule Nurmukhambetova, Tri Tran, Pradyot Dash, Dina Schneider. Co-expression of extracellular matrix enzymes heparanase or PH-20 augments the anti-tumor efficacy of folate receptor 1-targeting CAR T cells in an ovarian cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2913.

Abstract 6324: Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity

Abstract Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies. However, the rarity of cell surface protein targets that are specific to cancerous but not vital healthy tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificity from TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We first engineered murine T cells with an ovalbumin-specific TCR to express a CAR targeting murine CD19 and discovered that the expression of a strong TCR antigen on CD19+ leukemia enhanced CAR T killing. Importantly though, the presence of a weak TCR antigen antagonized CAR T responses, assessed by in vitro multiplexed dynamic profiling as well as in vivo cytotoxicity. We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19+ B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster, while TCR/CAR agonism enhanced infiltration of this T cell cluster. We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2) together with a hedgehog acyltransferase (HHAT) peptide-specific TCR that binds strongly to mutated tumor neoantigen while retaining weak affinity for the wild-type self-antigen on healthy tissue. We established a humanized in vivo model of CAR T function and found that AEBS CAR T cells maintained high anti-tumor activity against a human lung adenocarcinoma (PC9) but notably, their anti-tissue cytotoxicity against human bronchial epithelial cells (BEAS-2B) was minimized. AEBS CAR T cells therefore sharpen the discriminatory power of synthetic anti-tumor lymphocytes. Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies. Citation Format: Taisuke Kondo, François X. Bourassa, Sooraj Achar, Justyn DuSold, Pablo Cespedes, Madison Wahlsten, Audun Kvalvaag, Guillaume Gaud, Paul Love, Michael Dustin, Gregoire Altan-Bonnet, Paul François, Naomi Taylor. Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6324.

CAR PARKING NEEDS ANALYSIS AT CAMPUS A, JAKARTA STATE UNIVERSITY IN THE CONSTRUCTION PERIOD

Parking is the main supporting facility in the campus environment. Jakarta State University (UNJ) is one of the state universities in the DKI Jakarta province. The increase in the number of UNJ students every year certainly causes an increase in the need for parking, especially on Campus A UNJ as the main campus. Coupled with a construction project in Campus A UNJ, the parking area, especially car parking, is getting narrower. Based on these problems, it is necessary to research parking arrangements at Campus A UNJ. This study aims to determine the need for parking and arrange the car parking area for Campus A UNJ according to the demand for car parking space that existed during the construction period. The parking space arrangement for Campus A UNJ considers UNJ's policy as the manager in allocating parking spaces in the future. Primary data was obtained by conducting a field survey using the number plate survey method and PSU calculation survey while secondary data was obtained from UNJ parking inventory data. Primary data were obtained by conducting field surveys on 29 May-31 May and 8-9 June. The results showed that the value of the need for car parking in 2023 was 367 PSU cars, while the number of parking lots available at Campus A UNJ was 389 PSU. The peak day occurs on Tuesday 30 May 2023 and the peak hour for parking accumulation occurs at 13.00-14.00 WIB. With a parking index close to 100%, this can make it difficult for drivers to find an empty parking space, especially considering the pattern of car parking in the UNJ campus A parking area rotates in one direction so that the opportunity to find an empty parking space during peak parking hours only has a one-time or one-time chance. parking spaces at peak parking hours only have a one-time chance or the driver must first exit the parking lot and then enter again to find an empty parking space. The arrangement of car parking modeling will also be presented in this article. It is hoped that the results of this early-stage research can provide an overview and recommendations on parking management methods at Campus A UNJ.

An open-source radar-based hail damage model for buildings and cars

Abstract. Severe hailstorms result in substantial damage to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250 000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false-alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of damaged building in 91 %, their total cost in 77 %, the number of damaged vehicles in 74 %, and their total cost in 60 % of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland.

How Drivers Lose Control of the Car

<div>After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus<sup>®</sup> database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.” The driving simulator results have been reproduced by experimental tests on track. We have assessed that the driver’s steering action can make the car motion unstable if a proper disturbance has acted. The delay of the driver’s steering action is the primary cause for the generation of limit cycles. Future automated vehicles should be conceived by focusing on the addressed phenomenon.</div>

Analysis of the isolation performance of two operator’s seat suspension systems

From the good isolation results of Negative-Stiffness (NS) suspension and Three-Parallel (TP) suspension, the new isolation models designed by NSTP suspension (combination of NS and TP) and TPNS suspension (combination of TP and NS) to improve the operator’s shaking and riding quality have been investigated based on a 3-D dynamic model of car. Sensitivity of initial parameters in NSTP and TPNS suspension are analyzed and optimised via root mean square of the vertical acceleration ( a wzs), pitching acceleration ( a wϕs), and rolling acceleration ( a wθs) of operator’s seat. Research shows that (1) the vertical-riding quality in operator’s seat designed by NSTP and TPNS suspension is enhanced by 54.9% and 55.5% compared to the traditional suspension of operator’s seat; (2) the isolation efficiency in the vertical direction of both NSTP and TPNS suspension is similar. However, the a wϕs and a wθs with TPNS suspension are greatly deteriorated by 48.9% and 54.5% in comparison with NSTP suspension; (3) design parameters of NSTP suspension greatly affect the a wzs, a wϕs, and a wθs; whereas design parameters of TPNS suspension insignificantly affect the a wzs, a wϕs, and a wθs; and (4) the a wzs, a wϕs, and a wθs with optimised TPNS suspension are smaller than designed TPNS suspension by 42.8%, 71.3%, and 78.1%, respectively. Therefore, optimised TPNS suspension should be applied to further ameliorate operator’s shaking and riding quality.

Wall-modeled large eddy simulation in the immersed boundary-lattice Boltzmann method

This work presents the wall-modeled large eddy simulation (WMLES) in the immersed boundary-lattice Boltzmann method (IB-LBM). Here, the wall model with both diffusive- and sharp-interface immersed boundary methods (IBMs) is incorporated into the IB-LBM to handle the turbulent boundary layer in high Reynolds number turbulent flows. To maintain the numerical stability, two collision models, i.e., multiple-relaxation-time (MRT) and recursive regularized (RR), are implemented. The performance of these models in the WMLES is examined and compared in the simulation of internal and external flows by considering four benchmarks, i.e., turbulent flow in a channel, flow around a hull of submarine, flow around an Ahmed car model, and flow around a circular cylinder. It is found that a diffusive-interface IBM with wall model is capable to achieve excellent results for the simulation of external flows around bluff objects but fails in the simulation of internal flows of underestimating the wall shear stress due to its extra dissipation. The sharp-interface IBM with the wall model predicts the internal flow very well but fails in some simulations of external flow around bluff bodies due to the failure in the separation flow modeling. It is also found that the MRT-LBM is less dissipative than the RR-LBM, but it generates spurious nonphysical noise in the turbulent flows and tends to be unstable at high Reynolds numbers. Therefore, the diffusive-interface IBM with the wall model is more suitable for the external turbulent flow modeling, while its sharp-interface counterpart is more suitable for the internal turbulent flow modeling. The RR-LBM outperforms the MRT-LBM for the better stability and less nonphysical noise.

Age, Cars, and Claims: Decoding the Insurance Landscape

Embark on a journey through the realms of car insurance modeling, where the fusion of statistical and mathematical prowess unveils the secrets behind predicting claim frequency, severity, and overall costs. This enchanted exploration not only guides you through the wizardry of Python but also empowers you with the art of crafting insurance products, navigating risk, and orchestrating business strategies. If the arcane world of Car Insurance Modeling beckons you, join this mystical narrative, where algorithms and Python spells converge, weaving a tale of predictive mastery. Illuminate your path and delve into the enchantment of modeling automotive destinies with code as your guide

Automobile Transportation Logistics Supply Chain Management Strategy

To enhance the management level and quality of the automobile transportation logistics supply chain and promote innovation and development in automobile transportation logistics enterprises, it is essential to strengthen the construction of the automobile transportation logistics supply chain management model. This can be achieved through the gradual improvement of the automobile transportation logistics management process, ensuring that the management of the automobile transportation logistics supply chain proceeds in an orderly manner. The aim is to improve automobile transportation and logistics service levels while meeting the changing market supply needs. This will enable automobile transportation and logistics enterprises to maintain steady economic benefits and enhance their core competitiveness in the market. Therefore, this paper has conducted a comprehensive exploration and research on managing the automobile transportation logistics supply chain. Corresponding management strategies are proposed as a starting point to achieve the aforementioned goals.

A Framework for Communicating and Building a Digital Twin Model of the Electric Car

The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car’s speed and current in the electric motor. The achieved results are presented and then discussed.

The modified in-plane rigid-elastic-coupled tire modal model: dynamic response to short wavelength road profiles

The 400-degree-of-freedom deformed in-plane rigid-elastic-coupled tire model was enhanced by incorporating the vertical movement of the wheel as a rigid body. This refined model was then utilised to build the tire modal model. The tire modal model was developed to derive the transfer function (TF) covering a range of 0–300 Hz, which connects road irregularities as input to the contact patch and the wheel's vertical motion as output. The TF was operated under a quasi-static condition to obtain the tire's enveloping characteristics rolling over short wavelength obstacles as a direct function of vertical wheel displacement under varying contact patch length constraints. The tire modal model was combined with the quarter car model to analyse the vehicle's response when passing through short wavelength obstacles and random road profiles. This analysis was performed under varying load conditions represented by the contact patch length. The model and methodology demonstrated their effectiveness in handling rolling tires over short-wavelength obstacles and random road profiles.

Vehicle suspension tuning for bridge-friendliness and influence on coupled vehicle-bridge system frequency

A novel study on the mitigation of bridge response via tuning of vehicle suspension parameters is presented. The coupled vehicle-bridge interaction (VBI) system consists of a quarter car model with a semi-active skyhook suspension. Transmissibility functions of the time-varying VBI system are derived and an extended fixed points principle is applied to obtain optimally tuned values of the vehicle suspension parameters for bridge-friendliness. Performances of both the optimally tuned passive and semi-active suspensions are assessed in terms of the dynamic responses, energetics and interacting force of the VBI system, and are shown to achieve excellent bridge-friendliness. Effects of vehicle suspension parameters on the temporal variation of the coupled bridge frequency are also studied. It is found that the bridge frequency depends strongly on two key physical parameters: vehicle-bridge frequency ratio and vehicle damping ratio. Moreover, variations of the bridge frequency during the vehicle passage are small when the tuned suspensions are used. This new finding may have critical implications in future research in bridge frequency identification using a moving vehicle.

МЕТОДИКА АНАЛИЗА УСТАЛОСТНОЙ ДОЛГОВЕЧНОСТИ НЕСУЩЕЙ РАМЫ СПЕЦИАЛИЗИРОВАННОГО ВАГОНА ДЛЯ ПЕРЕВОЗКИ ШЛАКА

A technique to assess the strength and fatigue life of the welded load-bearing frame structure of a specialized slag carriage is proposed. This method provides for taking into account the specific operating modes of the carriage, which differ significantly from the operating conditions of mainline freight cars. For this car type, the fatigue life is determined by the modes associated with loading and unloading operations, as well as temperature loads acting on the bearing structure of the frame. The proposed method is based on mathematical and computer modeling of the dynamic loading of a car in operation. Dynamic loads are found on the basis of the developed solid car model. The structural strength is assessed using the finite element method. The fatigue life of the structure is estimated within the framework of a hybrid model that includes elements of low- and multi-cycle fatigue. The approbation of the technique is carried out using the example of a specialized car for transporting 16-VP slag. The results obtained confirm the operability of the studied car structure and correspond well with the results of field tests and analysis of bearing structures in operation.

Unlocking the value from car data: A taxonomy and archetypes of connected car business models

The automotive industry is relocating from viewing cars as standalone products to an all-encompassing ecosystem built around connected cars enabling data-driven business models. The vastly increasing amount of data collected by connected cars grants a unique driving experience for its users while providing companies operating in the automotive industry access to valuable information and, ultimately, cost and revenue benefits. In this article, we develop an empirically and theoretically grounded taxonomy of data-driven business models in the connected car domain to explore the impact of car connectivity and data availability on business models. Building on this, we conduct a cluster analysis revealing seven business model archetypes for the connected car domain: data platforms, location-based services, fleet management, diagnostics and maintenance, driving analytics, cyber-physical protection, and connected infotainment. Our findings advance the theoretical knowledge of data-driven business models, provide researchers with a systematic analysis of connected car-enabled business models, and enable decision-makers to identify strategic opportunities for leveraging connected car technology to enrich their business portfolios.

Information extraction from automotive reports for ontology population

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling (OCD),11 industryportal.enit.fr/ontologies/OCD,22 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the OCD ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.