Vehicle Cost Research Articles

Consumer response to fuel economy in the face of global fuel crises

One crucial determinant of the overall cost of national fuel efficiency rules is how consumers evaluate trade-offs between the initial cost of vehicles with better fuel economy and the anticipated savings in fuel expenses. Vehicle selection modeling is a useful tool for evaluating variations in customer preferences when it comes to purchasing vehicles. Additionally, it explores the impact of these changes on consumer surplus and compliance costs. The study employs a particular selection test under comparison test conditions to investigate its impact on the visual representation of fuel economy data. The fuel economy data is displayed distinctly for each of the six factors: average monetary savings/spending over four years compared to a car in the same class, miles per gallon, fuel costs over six years, yearly fuel prices, the complete government-mandated fuel efficiency label, and the total fuel cost throughout the lifetime of the vehicle. A nationally representative sample of 950 prospective car purchasers in Ghana was randomly allocated to a single situation throughout the country. The study determined that the data presented had a direct impact on how consumers assess fuel efficiency. When comparing other criteria such as annual fuel cost, five-year fuel cost, and spending/saving comparisons, the study found a significantly higher willingness to pay value for participating in the scenario that included fuel economy labels. The value was adjusted based on participant statistics. Participants of advanced age and those who anticipated a somewhat reduced expenditure for their forthcoming automobile attributed greater significance to fuel efficiency. This research investigates potential reasons for the disparities between consumer willingness to pay for fuel efficiency and the offerings of automakers, despite the limited available data to assess the relative importance of these various theories.

A Comprehensive Assessment of the Marginal Abatement Costs of CO2 of Co-Optima Multi-Mode Vehicles.

The Co-Optimization of Fuels and Engines (Co-Optima) is a research and development consortia funded by the U.S. Department of Energy, which has engaged partners from national laboratories, universities, and industry to conduct multidisciplinary research at the intersection of biofuels and combustion sciences. Since 2016, the Co-Optima team has examined high-quality bioblendstocks, and their properties, as design variables for increasing efficiency in modern engines while decarbonizing on-road light- and heavy-duty vehicles. The objective of this analysis is to combine and expand upon research into Co-Optima multi-mode bioblendstocks, which blend with petroleum gasoline to form high efficiency fuels for combustion in both spark ignition and advanced compression ignition engines. Consequently, the economic and environmental impacts of deploying 10 different multi-mode bioblendstocks derived from renewable and circular resources are quantified. Each bioblendstock is evaluated across several variables including (1) target blend levels of 10, 20, and 30 vol %, (2) years from 2030 to 2050, (3) crude oil benchmark prices, (4) vehicle lifetime miles, and (5) incremental vehicle costs. A Monte Carlo simulator is developed using a refinery optimization model and life-cycle analysis tool from prior Co-Optima research to sample marginal abatement costs of CO2, or cost of removing an additional unit of CO2, corresponding to each bioblendstock while considering input variable uncertainties. Results show that the combination of efficiency gains from advanced multi-mode fuel-engine technologies and the reoptimization of refinery operations results in several bioblendstocks demonstrating near-zero expected marginal abatement costs. Variable importances are also explored to highlight which aspects of the multi-mode technology are most influential in determining marginal abatement costs. Results suggest that Co-Optima multi-mode technology could provide economically viable decarbonization contributions to electrification-resistant light-duty vehicle sectors or near-term emission reductions, while Co-Optima fuels or alternatives decarbonize further to reach net-zero status.

Study of the Total Ownership Cost of Electric Vehicles in Romania

Due to the significant increase in the number of EVs, this manuscript presents a study of the total ownership cost of electric vehicles in Romania. The total cost of ownership (TCO) includes the initial purchase price, maintenance costs, power prices, and government incentives or subsidies unique to the market in Romania. The TCO was calculated for battery electric vehicles (BEVs) and internal combustion vehicles (ICEs). Several vehicles were selected for the study, representing the models with the highest sales in Romania and a similar price range. The results show that EVs have a lower TCO compared with internal combustion vehicles if the battery replacement cost for EVs is not considered in the analysis. If this cost is considered, the TCO for the BEVs has a significant increase due to the high cost of the battery. Another analysis performed regards the CO2 emissions. These are higher for ICEs compared to BEVs, so the BEVs help reduce emissions.

Motion Control of a Low-Cost Underwater Vehicle with Three-Position Cross Rudder

Underwater vehicles are widely employed as platforms for marine monitoring and operations, with rudders playing a crucial role in controlling their movements. Traditional underwater vehicles utilize servos to steer rudders, which results in high production and maintenance costs. To reduce the manufacturing costs of small underwater vehicles, this paper proposes a design featuring a three-position cross rudder driven by a binary-controlled electromagnet. Additionally, a novel virtual rudder angle controller is developed, which utilizes PWM frequency conversion to perform rudder control, ensuring motion performance comparable to that of conventional underwater vehicles. By establishing kinematic and dynamic models of a typical underwater vehicle (REMUS100), a corresponding virtual rudder angle control strategy is introduced. Based on the binary-controlled three-position cross rudder and virtual rudder angle controller, the effectiveness of the motion control method is verified by numerical simulation. The results confirm that the proposed virtual rudder angle controller successfully executes turning and spatial path-tracking tasks. The binary-controlled three-position cross rudder, together with the virtual rudder angle controller, offers a cost-effective solution for the practical application of small unmanned underwater vehicles, particularly for disposable underwater vehicles that do not require recovery.

Overcoming Barriers to Sustainable Green Transportation in Ho Chi Minh City: A Pathway Toward Achieving SDGs 11 and 13

Ho Chi Minh City (HCMC), Vietnam’s largest urban center, is home to over 9 million people and faces significant challenges due to rapid urbanization and the heavy reliance on personal vehicles. Over 95% of urban passenger transport in HCMC relies on high-emission cars, contributing to severe air pollution and slowing progress toward the United Nations Sustainable Development Goals (SDGs). Greening HCMC’s transport system is essential to reducing emissions and achieving SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Despite ongoing efforts, many barriers continue to hinder this transition. This paper aims to identify and prioritize these barriers using the fuzzy TOPSIS method. The results highlight limited infrastructure investment, a lack of charging stations, and unclear policies as the top obstacles. Other challenges include poor service quality, dependence on private vehicles, low community awareness, high fuel prices, and the cost of green vehicles. These findings offer a foundation for proposing solutions and prioritizing actions to build an eco-friendly transport system. This would directly support the SDGs related to sustainable cities, climate action, and public health. A thorough analysis of these barriers and their impact is vital to expedite and enhance the “greening” process by focusing on factors with significant and decisive influence.

A Game Theory Based Scheduling Approach for Charging Coordination of Multiple Electric Vehicles Aggregators in Smart Cities

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study various EVs aggregators including residential, commercial, official, university, and industry parking lots have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88% and 4.28%, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

States and Distributions of Water inside a Membrane of a PEMFC Above 100°C at Constant Water Vapour Pressure Studied Using Raman Spectroscopy

IntroductionHigh-temperature applications of PEMFCs under low humidity conditions have the potential to decrease the cost of heavy-duty vehicles, like trucks and buses. Water plays a crucial role in PEMFCs as it assists in the transportation of protons from the anode to the cathode for the redox reaction. To better understand the behaviour of water states and their distribution inside the membrane, a study was conducted using Raman Spectroscopy. This study was done to observe the different states of water in the Nafion 211 (25 µm) membrane at a constant water vapour of 36.85 kPa at cell operating temperatures ranging from 80 to 110°C.ExperimentA single-cell fuel cell setup was constructed for this study, as illustrated in Figure 1. A commercial Nafion 211 (25 µm) was placed between two (2 cm x 2 cm) 22BB gas diffusion layers (GDLs). One of the GDLs had a centred 0.5 mm diameter hole, while the other had a thin film sheet of platinum with the same diameter. Both the anode and cathode were supplied with humidified nitrogen gas in a crossflow manner at a dew point temperature of 74.65°C, equivalent to a water vapour pressure of 36.85 kPa. The experiment was conducted at a range of temperatures (80, 90, 95, 105, and 110°C) and corresponding relative humidities of (80.0, 54.7, 44.4 30.9, and 26.0%) respectively with Raman Spectroscopy (LabRAM HR 800) measurements taken at a depth of 15 µm, or at the centre of the membrane using the laser wavelength of 633 nm[1]. Before each measurement, the membrane stability was ensured for 3 hours. The exposure time for measurement was 90 s.Results and DiscussionThe spectrum was first baseline corrected and smoothened using the LapSpec software. It was then normalized to show the different intensities of the O-H stretching band of water between the Raman shift of 3000 to 3800 cm-1 as shown in Figure 2. Using the Gaussian function, the O-H stretching band of water present in the Nafion 211 membrane was deconvoluted with Igor Pro 8 software.Five major peaks were identified, including two species of protonated water - Eigen (H3O+) and Zundel (H5O2 +) - at 3080 and 3345 cm-1 respectively. Another peak was found at 3326 cm-1, which indicates H bonding to the sulfonic acid group (SO3H). Additionally, there was a peak at 3471 cm-1, indicating H-bonded water (H2O --- H2O) in bulk water, and a peak at 3663 cm-1, which indicates non-H-bonded water [2], [3]. The relative area, AOH/ACF, of the O-H stretching band of water was determined and plotted against the cell temperatures. Using the gravimetric water uptake obtained from Nafion 211, the number of water molecules per sulfonic acid group (λ) was determined, and each water component λi is expressed by λi = λTotal × fi Upon analysing the data, it was observed that the number of water molecules per sulfonic acid group (λi) of protonated water, hydrogen bonded to the sulfonic acid group (SO3H), and that of non-hydrogen bonded water remained relatively consistent through the temperature changes. However, the number of water molecules hydrogen bonded to one another, forming H2O---H2O bonds, decreased from λ = 5 at 80°C to λ = 1.6 at 110°C illustrated in Figure 3. In conclusion, we succeeded in determining the states of water existing in Nafion membranes at temperatures higher than 100°C.

Fitting Scattering Profiles of Perfluorinated Sulfonic Acid Ionomer Dispersions

Proton exchange membrane fuel cells (PEMFC) provide a path to wider adoption of hydrogen energy systems. This is because the energy density afforded by hydrogen makes PEMFC a strong candidate for the medium- and heavy-duty transportation sectors, where high allowable costs can reduce the barrier to adoption. As such, the focus of PEMFC research is toward greater efficiency and durability to reduce lifetime vehicle costs. To this end, high oxygen permeability ionomers (HOPI) are of interest, as the material has already been shown to increase mass activity in the cathode catalyst layer due to reduced anion poisoning from shorter side chains, in addition to enabling high power density through reduced oxygen transport resistance. While the performance of electrodes cast from HOPI and HOPI blended with commercial perfluorosulfonic acid (PFSA) has been documented, questions remain regarding the behavior of the novel ionomer in the dispersions and inks from which catalyst layers are fabricated. The literature on conventional PFSA ionomers devotes much attention to the effects of solvent media on interactions of ionomer backbone and side chains to form aggregates, motivated by the possible relationship between aggregates in dispersion and the structure of the cast catalyst layer, and thereby cell performance. In the present work, we probe the characteristic sizes and interactions among ionomer aggregates in dispersions by small angle x-ray scattering (SAXS). We report properties of aggregates in both HOPI and a commercial PFSA ionomer over a series of ionomer concentrations and at different ratios of water and propanol in the solvent medium. From these dispersions, aggregate spacings are estimated by fitting to the interference peak in the scattering intensity profile, and sizes are determined by form factor fitting. Next, series of blended HOPI and PFSA ionomer are prepared and studied by SAXS to understand the morphology of ionomer blends in solution. We further evaluate ionomer blends with different durations of ultrasonication, which can provide insight into the effect of sonication on ionomer blend aggregates during ink preparation.

(Keynote) on the Challenges and Opportunities for Parameterizing Multi-Physics Battery Models with Expansion Measurements

Lithium-ion batteries are projected to reach cost targets making them competitive with internal combustion engines for light-duty transportation within the decade [1], which will help drive demand for EVs. Upcoming regulations in Europe, China, and the United States for automotive safety and durability will drive the design of battery systems, and the algorithms for battery management over the next decade. The Global Technical Regulation 20 (GTR20) describes the safety requirements for electric vehicle battery systems. In particular, the proposed regulation addresses the need for early detection and warning of occupants about battery faults and failures. The warning should allow for egress 5 minutes prior to the presence of a hazardous situation inside the passenger compartment caused by a battery cell thermal runaway. However, detecting the failure with enough time can be difficult using the current production battery sensor suites. Durability assessment for Lithium-ion batteries is also tricky because these systems are designed to live for more than 10-15 years, so testing under representative conditions could exceed multiple product development cycles. Accelerated aging tests can shorten the testing time to reach the product's end-of-life conditions, typically defined as 70 or 80% state of health, but a deep physical understanding of the degradation modes that are accelerated by the testing is required to project these results back to the real-world use case and warranty period. Due to the many possible definitions of battery State of Health (SoH) which can be related to both capacity loss and internal resistance growth, the Global Technical Regulation 22 (GTR22) defines their durability requirements in terms of two new related metrics: the State of Certified Energy (SOCE) and the State of Certified Range (SOCR). Both metrics represent a percentage of the certified battery energy or electric range remaining at a given point in time. The SOCE is based on the Society of Automotive Engineers (SAE) metric of Usable Battery Energy (UBE). The GTR22 will also require a way for the consumer to read battery health information and usage data from the vehicle. Physics-based battery models which include degradation mechanisms have great promise in addressing many of these challenges due to the relationship between modeled design parameters and the resulting device performance and durability. The models can enable virtual engineering to assess design tradeoffs before building the first prototype, however, parameterizing these models can be difficult due to the large number of parameters. In this presentation, we will explore the challenges of parameterizing physics-based battery models using conventional current, voltage, and temperature measurements. We will show how the measurement of the cell expansion in the laboratory setting and the fixtures that support the testing [2]. The augmentation of expansion data can inform model development and parameterization of a Single Particle Model with electrolyte dynamics (SPMe) implemented in the PyBaMM (Python Battery Mathematical Modelling) framework [3]. Next, we will demonstrate how the sensor data could be paired with the models to augment diagnostic algorithms to infer electrode-level aging phenomena, improve the state of charge estimation, and provide an early warning of gas generation before cell venting. Finally, we will discuss the practical implementation issues in packaging temperature and strain sensors in automotive battery modules and packs. [1] C. Shen, P. Slowik, A. Beach. “INVESTIGATING THE U.S. BATTERY SUPPLY CHAIN AND ITS IMPACT ON ELECTRIC VEHICLE COSTS.” Feb 2024, ICCT REPORT [2] S. Pannala, A. Weng, I. Fischer, J.B. Siegel, and A.G. Stefanopoulou, “Low-cost inductive sensor and fixture kit for measuring battery cell thickness under constant pressure.” 2022, IFAC-PapersOnLine, 55(37), 712-717. [3] Sravan Pannala et al 2024 J. Electrochem. Soc. 171 010532

The multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands

The rapid development of the urban logistics recycling industry, combined with the complexity of the pickup and delivery networks, has created a surge in dynamic customer demands and exacerbated the difficulty of logistics resource sharing. Accordingly, this work focuses on a multi-depot pickup and delivery vehicle routing problem with time windows and dynamic demands, which incorporates resource sharing. A bi-objective mathematical model is formulated to minimize the total operating cost and number of vehicles. A three-dimensional affinity propagation clustering and an adaptive nondominated sorting genetic algorithm-II are combined to find Pareto optimal solutions. A dynamic demand insertion strategy is proposed to determine the vehicle service sequences for dynamic situations. Combined with an elite iteration mechanism to prevent the proposed algorithm from falling into search stagnation and improve the convergence performance. The superiority of the proposed algorithm is verified by comparing with CPLEX solver (i.e., ILOG CPLEX Optimization Studio 12.10), multi-objective ant colony optimization, multi-objective particle swarm optimization, multi-objective evolutionary algorithm, multi-objective genetic algorithm, and decomposition-based multi-objective evolutionary algorithm with tabu search. Besides, the proposed model and algorithm are tested by a real-world case study in Chongqing city, China, and the further analysis indicates that significant improvement can be achieved. Furthermore, by incorporating the recognition and prediction techniques of artificial intelligence on dynamic demand data, the proposed approach can realize the self-optimization of multi-depot vehicle routes and the precise allocation of logistics resources in dynamic environments. This study is conducive to the construction of a digitally-intelligent urban logistics system.

Integrating market penetration and cost technologies (IMPACT): Procurement incentives on fuel cell electric truck adoption in California

In the United States, both federal and local governments have instituted various financial incentives to promote the adoption of zero-emission vehicles (ZEVs) within their transportation sectors. However, it remains unclear whether these procurement incentives can effectively stimulate the adoption of ZEVs to achieve the expected targets, especially given the wide range of financial and non-financial factors anticipated in the coming decades. To investigate the energy, economic, and environmental aspects of the transition toward ZEVs, we developed the Integrating Market Penetration and Cost Technologies (IMPACT) model. In this study, it was tailored as a detailed analytical and quantification tool for investigating financial incentives for medium- and heavy-duty fuel cell electric trucks (FCETs). The IMPACT model comprises three interconnected sub-models: a vehicle cost model, a vehicle choice model, and an infrastructure model, each specifically tailored for the California market from 2024 to 2040. We designed three different incentive plans based on current policies: the IRS Clean Vehicle Tax Credits (CVTC) and the California Hybrid and Zero-Emission Truck and Bus Voucher Incentive Project (HVIP). The proposed hierarchical method facilitates a robust analysis of these incentives in relation to FCET sales and market shares, providing a quantitative basis to gauge the fiscal impact and effectiveness of these incentives over time. Our findings indicate that while financial incentives are crucial for enhancing FCET adoption, their design and implementation must be meticulously planned. Careful balancing of factors such as vehicle cost, infrastructure availability, and long-term sustainability is essential to optimize policy outcomes. This study underscores the complexity of incentive strategies and offers critical insights for policymakers aiming to expedite the deployment of FCETs and supporting hydrogen infrastructure in California and beyond.

Optimisation of River State Company (RTC) Fleet Management System

In the transportation industry, fleet maintenance is essential for ensuring operational efficiency, reducing costs, and extending vehicle lifespan. As fleets age, maintenance challenges escalate, impacting profitability. This study focuses on developing a maintenance reliability program for the Rivers State Transport Company (RTC) to optimize fleet management and enhance financial performance. The aim of this study is to assess RTC’s maintenance activities and design a reliability-based program that minimizes costs and maximizes profits. A mixed-method survey research design was adopted, with secondary data collected from RTC’s maintenance workshop records and financial reports spanning from 2014 to 2023. The dataset includes maintenance costs, replacement costs, and income for three vehicle models (Nissan Urvan, Toyota Hiace, and Ford Transit) across multiple years. The Dynamic Recursive Programming Model was employed to analyze vehicle age, cost, and income trends, and Microsoft Excel was used for data processing. The results show a significant rise in maintenance costs as vehicles age. For example, the Nissan Urvan’s maintenance costs increased from ₦1.2 million in 2014 to ₦2.6 million in 2023, representing a 116.7% increase over 9 years. Replacement costs for the Toyota Hiace surged from ₦4 million in 2014 to ₦8.5 million in 2023. Income generated by older vehicles also declined by approximately 25%, with the Ford Transit generating ₦5.1 million in 2023 compared to ₦6.8 million in 2014 due to frequent breakdowns and reduced reliability. The analysis further revealed that replacing vehicles after 6-7 years of operation would maximize financial returns, based on the balance of maintenance and income. In conclusion, maintaining an aging fleet presents a substantial financial burden, with rising maintenance costs, diminishing income, and increasing replacement expenses. This study recommends that RTC adopt a structured vehicle replacement policy guided by the Dynamic Recursive Programming Model. Furthermore, reliability-based maintenance strategies should be implemented to reduce unnecessary expenses and improve the overall performance and profitability of the fleet.

A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.

Assessing the environmental and economic impacts of intracity express delivery: Pathways for carbon reduction and cost efficiency in China

With the rapid development of e-commerce, the environmental impacts and economic costs of intracity express delivery, along with reduction pathways, however, remain largely unexplored. Therefore, we compiled carbon emission inventories of express delivery at the intracity scale covering the entire process from warehouse to consumers. Additionally, five pathways were designed and compared to explore the feasible measures for coordinated carbon reduction and cost-effective efficiency improvements, incorporating new technologies such as electric vehicles and autonomous driving. The results show that carbon emissions from intracity express delivery in China surged from 97 kt in 2013 to 1143 kt in 2021. A series of measures could reduce carbon emissions by 64.86 % (741.287 kt) through the electrification of gasoline vehicles, although the vehicle cost of electric vehicles would triple. If unmanned electric vehicles are used for the last mile of express delivery, the economic costs could decrease significantly by 77.77 % (US$36.693 billion), reducing the cost per express from US$0.436 down to US$0.097. Our study could provide a theoretical basis for the sustainable development and costs reduction of China's express delivery industry.

A two-echelon multi-trip vehicle routing problem with synchronization for an integrated water- and land-based transportation system

This study focuses on two-echelon synchronized logistics problems in the context of integrated water- and land-based transportation (IWLT) systems. The aim is to meet the increasing demand in city logistics as a result of the growth in transport activities, including parcel delivery, food delivery, and waste collection. We propose two models, a novel mixed integer linear joint model, and a logic-based Benders’ decomposition (LBBD) model, for a two-echelon problem under realistic settings such as multi-trips, time windows, and synchronization at the satellites with no storage and limited resource capacities. The objective is to optimize transfers and satellite assignments, thereby reducing overall logistics costs for street vehicles and vessels. Computational experiments demonstrate that the LBBD model is more robust in terms of solution quality and solution time on average while the added value of the LBBD is more evident when solving large-scale instances with 100 customers, reducing the overall costs by 10.6% on average and significantly reducing the fleet costs on both networks. Furthermore, we assess the effect of changing cost parameters and satellite locations in the proposed IWLT system–analyzing system behavior and suggesting potential improvements–and evaluate several system alternatives in city logistics–consisting of different transportation network designs (single- and two-echelon), vehicle types, and operational constraints. On average, the proposed two-echelon IWLT system reduces the number of kilometers traveled by vehicles at street level by ranging from 20% to 30% compared to a typical single-echelon service design that relies solely on trucks.

Wireless power transfer systems and wireless charging design between electric vehicles

The foundations of wireless power transmission were laid down by Nikola Tesla many years ago, by transmitting energy wirelessly using a Tesla coil. Nikola Tesla's work many years ago pioneered wireless power transmission studies today. Wireless power transmission, which telephone companies have focused on in recent years, also forms the basis for charging electric vehicles. One of the main reasons why lithium ion batteries are preferred in electric vehicles is the advantages they offer regarding the charging process. These batteries provide longer range compared to other battery types, thanks to their high energy density, that is, they can store more energy compared to their size. At the same time, faster charging capabilities are a great advantage for electric vehicle owners. As a result, charging technology is very important in electric vehicles. In this study, we will present a design project that enables electric vehicles, which have become more common in daily life with the developing technology, to transfer their charge to each other contactlessly in emergency situations. In the design, the receiver coil will be placed on the front hood of the vehicle and the transmitter coil will be placed on the rear hood of the vehicle. There will be a distance sensor where the transmitter coil is located, thanks to this sensor, the distance can be adjusted. When the vehicles reach the optimum distance, the distance sensor will give a warning and be charged enough to go to the nearest charging station. In addition, thanks to the ease of sharing for emergencies, emergency vehicles (ambulances) will be able to quickly receive energy from another vehicle and continue their duties without interruption. In this way, we will reduce the towing cost of electric and hybrid vehicles and provide drivers with uninterrupted driving. In the wireless charging station; We will offer many advantages such as getting rid of cable clutter, providing ease of use, compatibility with all devices, multiple charging and durability.

Co-Estimating State of Charge and Capacity of Automotive Lithium-Ion Batteries Under Deep Degradation Using Multiple Estimators

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.

Preferable price to buy an electric two-wheeled vehicle for college students

This research delves into the determinants affecting Taiwanese college students’ purchasing behavior towards two-wheeled electric vehicles (ETWVs) and the financial allocation they are willing to commit. A choice experiment design was utilized, with a sample of approximately 900 college students, regular users of two-wheeled gasoline vehicles (TWGs). The students engaged in one of two choice scenarios, presented with general characteristics, operational efficiency, vehicle cost, and subsidy strategies. Subsequent to the scenarios, respondents indicated their willingness to pay (WTP) for a ETWV. ETWVs, apart from being an emerging transportation alternative, also present potential environmental and health benefits due to reduced air pollution. The dependent inverse hyperbolic sine (IHS) double-hurdle model was employed, facilitating the accommodation of heteroscedasticity and correlated normality. The outcomes suggest that purchasing behavior for ETWVs among college students is shaped by factors such as purchase subsidy, tax breaks, and ETWVs’ capability in supporting students’ external travel requirements. The average WTP for ETWVs among this demographic stands at US$ 1,410. Importantly, the dependent IHS double-hurdle model, considering heteroscedasticity, proved statistically superior to its counterparts. Insights from this research offer crucial information on the price range students are likely to accommodate for ETWVs and provide a foundation for framing incentive-driven policies to amplify ETWV adoption.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

ANALISIS BIAYA OPERASIONAL KENDARAAN BERDASARKAN KECEPATAN RATA-RATA PERJALANAN DI JALAN MAJAPAHIT KOTA MATARAM

The city of Mataram, the center of trade, offices, and education, experienced an increase in population and vehicles. The 2023 data recorded 441.147 thousand residents and 385,579 motor vehicles. The increase in traffic volume on road Majapahit has an impact on traffic performance and vehicle operating costs. The study entitled " Analysis Of Vehicle Operational Costs Based On Average Travel Speed On Jalan Majapahit Mataram City" was conducted to further understand the condition of road section performance and the Operating Cost of Vehicles passing through the road. The MKJI method is a calculation standard used to analyze the performance of road sections, assess capacity and service levels, based on traffic volume and road geometry. while to calculate BOK (Vehicle Operating Costs) used an equation method developed by the PCI 1989 method to measure vehicle costs, including fixed costs and non-fixed costs, which are influenced by the average speed of travel. The results of the study on the Majapahit road section show the service level of the Majapahit road section in category C. For the calculation of the free flow speed, namely 52.68 km / h (east) and 49.10 km / h (west). The speed of the eastbound peak clock is 48 km/h and the westward is 42 km/h. While the calculation results of BOK Group I (Eastward) Free Flow Speed Rp1.049,- / km, Peak Hour Speed Rp1.082,- /km. Group I (West) Free Flow Speed Rp1.072,- /km, Peak Hour Speed Rp1.163,- /km. Group IIA (Eastward) Peak Hour Speed Rp2.921,- /km, Peak Hour Speed Rp3.007,- /km. Group IIA (Westward) Free Flow Speed Rp2.980,- /km, Peak hour speed Rp3.235,- /km