Total Cost Of Ownership Research Articles

Practice Makes Perfect: Using the Total Cost of Ownership to Teach Global Locational Decision Making.

Practice Makes Perfect: Using the Total Cost of Ownership to Teach Global Locational Decision Making.

Techno-Economic Comparison of Total Cost Ownership of Electric and Combustion Light Commercial Vehicles

Objective: This study aims to investigate the Total Cost of Ownership (TCO) of an Electric Vehicle (EV) and a Combustion Vehicle (CV) to determine the economic viability of the EV. Theoretical Framework: Net Present Value (NPV) is used to evaluate costs over a 10-year timeframe, considering purchase prices, insurance, maintenance costs, fixed-term deposit rates, and road taxes. Method: Two similar commercial light vehicles (Renault Kangoo and Renault Kangoo ZE) are compared using a financial analysis based on NPV. Results and Discussion: The electric version of the vehicle (Renault Kangoo ZE) proves to be economically attractive, especially with intensive use. Research Implications: The results suggest that, with intensive use, electric vehicles can be a financially viable option, which could encourage their widespread adoption in the future. Originality/Value: The value of the study lies in its detailed financial approach to comparing the TCO between an EV and an equivalent combustion vehicle. This analysis allows for the evaluation of the economic viability of electric vehicles, providing a solid basis for decisions on their widespread adoption.

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

Fuel Cell Electric Buses: A Systematic Literature Review

This paper presents a comprehensive review of scientific papers and market reports analyzing the economic competitiveness of fuel cell electric buses (FCEBs) with respect to their conventional alternatives via the total cost of ownership (TCO) methodology. We discussed the variables and data taken into account and compared the resulting outcomes by year and geographical areas. It emerged that FCBs are not currently cost competitive. The decreasing trend in acquisition and fuel costs, however, indicates potential for future competitiveness. We find that the current TCO literature on FCEBs presents several areas of uncertainty and weakness. Potential improvements can be achieved by: (i) extending the geographic coverage to Asian and African developing countries; (ii) making use of real-world data instead of simulated data, in particular, concerning acquisition costs, hydrogen costs under different pathways, fuel efficiency, and maintenance costs; (iii) clarifying the role of infrastructural costs; (iv) exploring the existence of economies of scale at fleet level; (v) distinguishing among different bus sizes.

The Use of eXplainable Artificial Intelligence and Machine Learning Operation Principles to Support the Continuous Development of Machine Learning-Based Solutions in Fault Detection and Identification

Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept.

Investigating the Future of Freight Transport Low Carbon Technologies Market Acceptance across Different Regions

Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035.

Unlocking the potential of electric and hybrid tractors via sensitivity and techno-economic analysis

The majority of agricultural vehicles in use today still rely on diesel-based propulsion, a major source of air pollution. Electrification is seen as a potential solution for decarbonizing these off-road vehicles but is hampered by higher upfront costs in energy storage and charging infrastructure. To better quantify these barriers, this paper proposes a techno-economic tool that can assist farmers in evaluating the total costs of ownership of electric and hybrid tractors. A pragmatic simulation model of the tractor is developed to predict the annual energy/fuel consumption and NOx emissions, while an economic model estimates the total acquisition, operation, and maintenance costs. For managing the energy in hybrid tractors, a new power split strategy based on model predictive control is developed, allowing the designer to balance energy efficiency and NOx emissions, while taking into account operational constraints of the electric powertrain. Additionally, we propose novel decision maps that allow farmers to quickly identify operating regions (in term of average load and yearly working time) where the deployment of electric/hybrid tractors is economically viable. To account for variability in the tractor’s operational conditions, we conduct both a deterministic sensitivity analysis with multi-parameter variation and a stochastic analysis of the total cost of ownership. The tool is validated with different mission profiles based on data from a California farm. The results show that, compared to diesel powertrains, electric tractors are more cost-effective for light-duty farming activities (engine loads less than 20%). On the other hand, hybrid powertrains are more economical for medium-duty tasks, where engine loads range from 20% to 60%.

Cooling System of A High-Pressure Centrifugal Compressor: Development and Impact on Wheel Temperatures (GT2024-121464)

Abstract High power density, low fuel consumption, and a compact design with competitive total costs of ownership are key requirements for internal combustion engines for conventional fuels and carbon-free or carbon-neutral future fuels. These requirements influence the development targets of charging systems and call for single-stage charging systems with increased charge air pressures. However, high compressor pressure ratios of the turbocharger lead to challenging material temperatures in aluminum alloy wheels. Consequently, creep becomes the fundamental lifetime-limiting damage mechanism for these wheels. In order to meet customer requirements regarding total costs of ownership, the lifetime is of major importance for the development of aluminum compressor wheels for turbochargers with pressure ratios beyond 6. This paper focuses on a high-pressure compressor design with a water-cooling system for a new turbocharger generation. The development of its components and their validation based on numerically predicted and measured wheel temperatures are discussed. After introducing the need for high-pressure ratios and the accompanying lifetime challenges, the paper presents the design strategy and methods of the development process. Finally, the paper summarizes the achievements obtained by this procedure. Detailed and rarely before documented temperature measurements from the rotating compressor wheel are published. As will be shown, the temperature measurements confirm the predictions from the pre-development phase. A comparison of the temperature measurements from the recently developed wheel with measurements carried out for a state-of-the-art wheel reveals the improvements achieved by this new generation of high-pressure compressors.

A Comparative Analysis of Microsoft Fabric and Snowflake: Architectural Paradigms and Performance Metrics in Modern Cloud Data Platforms

Abstract: This article presents a comprehensive comparative analysis of two leading cloud data platforms: Microsoft Fabric and Snowflake. As organizations increasingly rely on cloud-based solutions for data management and analytics, understanding the strengths and limitations of these platforms becomes crucial. This article examines the architectural foundations, functional capabilities, performance metrics, and cost considerations of both platforms. We explore Microsoft Fabric's integrated service model within the Azure ecosystem and Snowflake's multi-cluster shared data architecture, assessing their approaches to data integration, storage, analytics, and machine learning integration. Through empirical performance comparisons and an evaluation of scalability mechanisms, we provide insights into the operational efficiencies of each platform. Additionally, we analyze the security features, compliance standards, and pricing models to offer a holistic view of the total cost of ownership. Our findings reveal distinct advantages in Microsoft Fabric's end-to-end integration and Snowflake's performance in multicloud environments, while also highlighting areas for potential improvement in both platforms. This article aims to serve as a valuable resource for organizations navigating the complex landscape of cloud data solutions, offering evidence-based criteria for platform selection aligned with specific business needs and technological ecosystems

Sustainable Development Goals (SDGS) and Total Cost of Ownership (TCO): a holistic approach

Sustainable Development Goals (SDGS) and Total Cost of Ownership (TCO): a holistic approach

Estimating the market share of electric motorcycles: A system dynamics approach with the policy mix and sustainable life cycle costs

Introducing electric vehicles is critical to maintaining air quality and reducing carbon emissions. The Indonesian government has issued several regulations to stimulate the diffusion of electric vehicles. This research attempts to forecast the electric vehicle market share, especially electric motorcycles, by involving the policy mix and sustainable life cycle costs. We propose a system dynamics approach that takes into account a policy mix including 0% down payment without credit interest subsidies, tax abolition, expansion of charging station network, and sustainable life cycle costs, i.e., total cost of ownership, social, and environment. The system dynamics model has four modules: the electric motorcycle cost, the conventional motorcycle cost, the economy module, and the consumer market. The simulation results show that the electric motorcycle market share will increase positively in 2021–2030, reaching 5.7% in 2030. Based on the scenario simulation results, providing more charging stations and vehicle tax abolition can significantly boost the market share of electric motorcycles in Indonesia. The study provides valuable insights for policymakers in formulating more appropriate policy instruments to promote electric vehicle diffusion in Indonesia.

Case Study: Advanced Technology Underpins Efficiency and Climate-Related Goal Gains

_ Today’s gas-compression operators are under increasing pressure to reduce greenhouse gas (GHG) emissions. Fortunately, multiple technologies are available that can support that objective while enabling operators to move more gas cost-effectively. Embracing such advancements can positively impact a reduced-carbon future without compromising operational efficiency or profitability. To choose the right technologies for a gas-compression package, it’s critical for operators to have a clear understanding of their business and GHG emissions-related goals. Operators also need clarity concerning the impact of evolving requirements on total cost of ownership (TCO) as well as potential financial ramifications. Producing more gas at a lower cost with lower GHG emissions is possible with new technologies. Operators can support their success by choosing the right engine driver for a gas-compression package based on their unique needs and site conditions. It’s critical to consider an engine’s power density and TCO, as that informs an operator’s selections and can lower a gas-compression package’s operating cost in terms of dollars per thousand cubic feet ($/Mcf) of gas throughput. An engine, such as the Cat G3600 A4 Gen 2, that can deliver a 10% power increase compared to the previous model can allow operators to use fewer engines to perform the same amount of work. This advantage can lower capital requirements, reduce operating expenses, and decrease fuel consumption. Fewer engines operating in the field may also contribute to lower GHG emissions. Modernizing Existing Assets Operators prioritize flexibility, durability, and lower TCO. They can support those objectives by choosing an engine platform that allows performance upgrades across the life of the engine. This enables operators to apply advanced technology in their existing fleet year after year without incurring the substantial cost of purchasing a new engine. Engine upgrade kits offer operators an option to maximize and modernize active assets in a streamlined way, as the upgrades can be applied conveniently and cost-effectively at the time of a scheduled overhaul to minimize disruption. For example, by using an upgrade kit for an A4 Gen 1 engine model referenced above, operators can lower volatile organic compounds up to 32%, formaldehyde up to 24%, carbon monoxide up to 13%, methane emissions up to 33%, and total GHG emissions up to 5% on a grams per brake horsepower-hour (g/bhp-hr) basis* while saving the traditional capital expenditure of a new engine. (*Site conditions: Same power level between the G3600 A4 Gen 1 and Gen 2, 85MN, 905 BTU/scf, 100% load, 25°C ambient, 500-ft altitude.) Such updates are relatively cost neutral when applied as part of a scheduled major overhaul and require minimal or no change to the compression package. This makes upgrade kits a wise choice to ensure equipment keeps pace with industry demands.

Decarbonizing Commercial Vehicles: Life Cycle Assessment of Diesel, Petrol and Electric Trucks

Abstract The electric vehicle (EV) landscape in Malaysia is still trailing behind the more established internal combustion engine industry. However, the promotion of EVs has become an increasing priority for the government, especially after committing to achieving net-zero emissions by 2050 at the earliest, as outlined in the 12th Malaysia Plan. Malaysia has set a target of having 1.5 million EVs by 2040, with a notable portion consisting of commercial EVs like buses and trucks. Developing the EV ecosystem and sustainable trucks in Malaysia faces challenges such as high costs and insufficient supporting infrastructure. This study aims to assess the Well-to-wheel life cycle of CO2 emissions and total cost of ownership (TCO) of diesel trucks (DTs), petrol trucks (PTs), and electric trucks (ETs). The study employs IPCC 2006 guidelines to estimate CO2 emissions and a TCO assessment method. The results indicate that the average CO2 emissions of ET are 38% lower than those of DT, and 33% lower than PT. Furthermore, the TCO of ET is 30% and 15% higher than that of DT and PT, respectively. Despite the higher cost, the study concludes that ETs display superior performance in terms of CO2 emission reduction. These findings contribute significantly to the development of policies and knowledge aimed at promoting sustainable trucks, aligning with the country’s net-zero goals.

Current Status and Future Perspectives for Mobility Options Toward Sustainable Transportation With a Focus on Fuel Cells

ABSTRACTThis paper presents a comprehensive analysis of five different vehicle technologies with a particular emphasis on fuel cell vehicles (FCVs) in comparison to conventional gasoline and diesel ICEs, hybrid electric vehicle (HEV), and electric vehicle (EV) technologies. Key parameters in comparison included greenhouse gas emissions (GHG), social cost of carbon (SCC), energy efficiency, fuel consumption, fuel price, driving range, and total cost of ownership (TCO). A normalization approach is employed to rank the technologies across parameters, providing valuable insights for sustainable transportation decision‐making. FCV technology showed the highest average performance (5.343/10) followed by EV technology (4.760/10). The paper also presents current and anticipated hydrogen demand and supply centers, accompanied by a brief overview of commercially available fuel cell technologies. Finally, the sustainability of FCV technology as well as economic, legal, and infrastructural limitations associated with the widespread adoption are also discussed.

Vehicle To Building (V2b) Peak Load Shaving and Tariff Analysis

Abstract Commercial buildings are essentially important energy consumers for national electricity grid provider as it’s owned by businesses owners that directly keep up the national gross domestic product (GDP). Soaring load demand from commercial buildings usually occurs within few hours on peak business hour. Electricity grid infrastructure designed to support maximum demand of the system but underutilize most of the time outside the peak session. Growing number of EV penetration in local market can serve as mobile energy storage for Vehicle to Building (V2B) energy integration thus enable peak load shaving to minimize maximum demand during peak period. This study presents a feasible methodology approach on determining the suitable V2B tariff on several peak load shaving scenarios to provide attractive return to building owner and discounted off-peak tariff to accommodate lower EV owner’s charging cost. By having the combination of attractive V2B and off-peak tariff that benefits both sides, subsequently increase EV penetration for V2B. It may also charm EV markets as it minimizes the total cost of EV ownership and reduce maximum demand from electricity grid. Hence it reduces the transportation CO2 emissions and contribution toward optimization of grid network electrical infrastructure of transmission and distribution systems design with lower maximum design system requirement.

Bus fleet decarbonization under macroeconomic and technological uncertainties: A real options approach to support decision-making

Fleet owners deal with various and critical uncertainties while planning bus fleet replacement, encompassing macroeconomic and technological factors, such as fuel and electricity prices, energy consumption, purchasing costs of electric batteries and powertrains, and bus salvage value upon resale. This paper proposes a Real Option-based framework to evaluate investment choices in different bus technologies over time (diesel, hybrid electric, compressed natural gas, and full-electric), where the proposed options usually involve replacing existing diesel fleets with alternatively powered buses. In particular, we provide a multi-option least squares Monte Carlo simulation to help fleet owners in making cost-effective investment decisions, resulting in potential cost savings of up to 10% of the annual total cost of ownership. By applying the proposed approach to different bus route types (inner-city, urban, suburban, commuter), the results show how the optimal investment choices for fleet replacement change over time. Initially, diesel buses or, at most, compressed natural gas buses are more cost-effective compared to electric technology. However, as electric technology becomes increasingly competitive, internal combustion buses are phased out. Inner-city and urban routes are better suited for the electric transition, whereas the transition may progress more slowly on other route types.Finally, we show that, under technological and macroeconomic uncertainties, the duration of contracts can strongly affect the investment decisions, in absence of specific guarantees on the new fleet.

Integral techno-economic design & operational optimization for district heating networks with a Mixed Integer Linear Programming strategy

The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business-cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a Mixed Integer Linear Programming approach that captures the physics as well as the financial choices. Linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.

Carbon emission reductions in the University of São Paulo’s transportation sector using hydrogen-powered vehicles

The transportation sector, especially diesel-powered buses in Brazil, faces a significant decarbonization challenge, contributing with 19.76 million tCO2-eq GHG emissions. Therefore, this study aims to calculate the achievable GHG emission reductions by implementing renewable hydrogen vehicles at the University of São Paulo. The methodological approach considers low-carbon hydrogen production using the ethanol steam reforming route, where energy and mass balances are performed throughout the hydrogen chain. Two models are developed to calculate CO2-eq emissions. Results show that replacing the current fleet of conventional internal combustion engine buses with hydrogen-based options leads to a significant annual reduction of 2658 tCO2-eq, approximately an 83% reduction as compared to the conventional one. Moreover, the total energy conversion efficiency, well-to-wheel, reaches 26.9% and 11.8% for hydrogen and conventional vehicles, respectively, without air-conditioning system, or 21.9% and 9.9% when air-conditioning is coupled, respectively. Notably, CO2-eq emissions increase by at least 38.3% and 15.5% due to the presence of that equipment, respectively. Finally, the results of total cost of ownership (TCO) showed that CAPEX represents only 7.14% of the TCO, being the fuel cost the major proportion for both technologies.

Electrified fleet and infrastructure aware energy efficient routing

This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.

Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles

The primary objective of the carried-out research work is to utilise the multi-criteria decision-making (MCDM) techniques to rank the top-selling electric two-wheeler (E2W) models based on the different performance features stated by original equipment manufacturers and estimated through standard and regional driving cycle (DC) analysis. First and foremost, the vehicle structural, powertrain, performance features, and cost component-related data of eight top-selling E2W models (M1–M8) are gathered from the official websites of OEMs and local dealers. Then, performance features like energy consumption per km (EC/km), driving range (DR), well-to-wheel (WTW) CO2 emission, total cost of ownership (TCO), and total cost of ownership per km (TCO/km) are estimated under standard and regional DCs through the developed energy consumption and TCO models. Further, the ten different MCDM tools are utilised for prioritising the E2W models under each DC with the attributes’ weights estimated from the entropy methods. To enhance the trustworthiness of ranking results, sensitivity analysis is done using six distinct combinations of attribute weights. Moreover, the M7 E2W is identified as the best E2W by utilising Borda Count to integrate all the ranking results. Overall, this study provides an in-depth understanding for users about how to select E2W with minimal effort.