Search

Environmental managers attempt to increasingly incorporate precautionary principles into decision making. The literature lacks Machine Learning -based approaches for forewarning lifecycle environmental impacts. This paper proposes a method to support electric vehicle design. The main innovation of the work lays in merging Life Cycle Assessment (LCA) and Machine Learning foundations to provide support and awareness to designers. The present approach overcomes the present literature because it provides a method for the design phase, is based on a well-established methodology (LCA) and provides quantitative results from little inputs. The approach exploits machine Learning Methods to develop models with the design features of a generic electric vehicle (such as vehicle mass and distance traveled) in six phases (Problem definition; Data collection; Data Preparation; Modeling; Model evaluation; Model interpretation). Differently from existing environmental analyses, all stages of the product life cycle have been considered in building the database; moreover, the model provides quantitative results. Regression models and supervised algorithms were used. The obtained model can be used by product engineers, as well as those not experts on LCA. Moreover, the model guarantees the database and hypothesis's uniqueness, ensuring the results coherence and comparability. The level of accuracy obtained in the case study (error or 17%) is comparable with studies handling full environmental analysis (that should be more accurate), and outstanding, as the present case is for the design phase. Future works will focus on additional significative indicators, similar electric vehicle design and integration with prospective LCA approaches. [ABSTRACT FROM AUTHOR]

Transitioning from internal combustion power to passenger electric vehicles presents a highly complex problem for governments. During the past decade, there has been a slow uptake of passenger electric vehicle sales in Australia, a mere 3.6% per annum. This study explores the electric vehicle adoption policies and practices in Australia through an analysis of various stakeholder perspectives. A mixed methods approach comprising program analytics and vector-algebraic study of vehicular stakeholders was used to interrogate a range of electric vehicle stakeholders' data sets with 1, 317 stakeholder statements being analysed. The results of this study reinforce the critical importance of direct financial incentives and economic support measures valued up to A$21,000 per vehicle. In addition, the analysis exposed strong electric vehicle consumer sentiment to install up to 5800 new fast charging stations (151 km of road route per unit) by 2040 at a cost of A$1.2 Billion, while ensuring provision of vehicle model performance data and charging network information programs. Importantly, governments must show leadership with cooperative revisions to national vehicular standards, public fleet electrifications, and setting national passenger electric vehicle purchase targets. The study posits important actions that suborn passenger electric vehicle transition as a foundation for future forms of electric mobility. • There is a slow uptake of electric vehicle sales in Australia. • Analysis suggests beneficial incentives policies for EV of A$21,000 per vehicle. • Estimated fast charging network of 5800 sites requires over A$1.2 Billion investment. • Governments must install national EV standards, public EV fleets and EV purchase targets. • Passenger EV transition supports multiple applications of future electric mobility. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are promising transportation tools for supporting green supply chain and cleaner production. In contrast to traditional fossil fuel-powered vehicles, which usually have a short range at lower speeds, EVs have a much longer (even double) range when traveling at lower speeds than high speeds. This feature has a major impact to the vehicle routing problem when EVs are used in the fleet. This study investigated the electric vehicle routing problem with time window (EVRPTW) considering the energy/electricity consumption rate (ECR) per unit of distance traveled by an EV as a function of the speed and load, referred to as EVRPTW-ECR for simplicity. As a consequence, the maximum range of an EV is estimated dynamically according to its speeds and loads along the route. A mixed-integer linear programming (MILP) model was developed for EVRPTW-ECR, where the EV's speed was treated as a continuous decision variable and the battery capacity, instead of a constant distance, was taken as the range restriction. Two linearization methods, i.e., the inner approximation and outer approximation, were introduced to handle the nonlinear relationship between the traveling speed and travel time with a given parameter ε to control the maximum permissible error. Computational experiments were carried out based on Solomon's instances to test the efficiency and effectiveness of the proposed model and methods, thereby demonstrating that the MILP model can be solved optimally for up to 25 customers by the CPLEX solver and partially optimized for large instances of up to 100 customers by using a heuristic approach. • Speed-varying range of electric vehicle is first considered in route optimization. • The speed and load are treated as continuous decision variables. • Two linearization methods are provided for the nonlinear time-speed curve. • A mixed-integer linear programming model and a heuristic approach are presented. • Experiments showed considerable improvements on energy efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a new Vehicle Routing Problem (VRP) with fair Carrier Collaboration (CC) which split multi-pickup and delivery services are considered for serving customers. This study has focused on VRP and serving customers with several commodity requirements from different geographically scattered suppliers subject to constraints on the vehicle capacity. A Mixed Integer Programming (MIP) model to maximize the total profit and the fair sharing of profit among the carriers by considering the travel time minimization is developed. Each carrier with its limited capacity can have reserved requests which must be served by itself and selective requests which can be served by itself or other vehicles or not served at all. There are various applications of the proposed model in the environment which can help reducing number of vehicles serving to the customers and eliminating empty back hauls. A Genetic Algorithm (GA) is proposed to solve this problem due to its Non-deterministic Polynomial-time hard (NP-hard) nature. In addition, Variable Neighbourhood Search (VNS) method is developed for improving the quality of initial solutions. Some instances are generated at different scales to evaluate the algorithm's performance by comparing the results of an exact optimal solution with that of the proposed algorithm. The obtained results demonstrate the efficiency of the proposed algorithm in providing reasonable solutions within an acceptable computational time. The algorithm is also tested for an online shopping website in Tehran, Iran. The test outcome shows that the proposed model returns a better benefit compared to the manual methods. The results of sensitivity analysis suggest that increasing the fairness coefficient among carries can led to a decrease in the total obtained profit. • Develop a novel carrier collaboration routing problem model with multi-commodity service and sharing fair profit. • Extend the carrier collaboration to general problems with in many-to-one problem pickup and delivery category. • Solve the problem with genetic algorithm with a new specific chromosome representation. • Result of implementation of the model in the real-world and sample instances. • Sensitivity analysis of the model based on fair distribution between participants is conducted. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) powered by renewable energy hold promise for significantly decarbonizing land-based transport. However, the environmental impacts of BEVs remain a critical concern. This paper investigates the circular business model (CBM) of leasing batteries for BEVs and compares its economic and environmental impacts with the linear model of selling and buying batteries. A comprehensive approach combining a battery fleet model, net present value (NPV) analysis, and cradle-to-grave life cycle assessment (LCA) is employed. By considering diverse driving profiles instead of the single "average" profile used in previous studies, the battery fleet model provides a more accurate representation of reality. The findings reveal that while leasing batteries can be as profitable as selling them, companies that lease may require higher revenues and tax contributions to obtain a comparable NPV. To generate additional income, companies may need to charge fees that do not always benefit customers. Notably, battery purchasing remains the most cost-effective option for users driving over 10,000 km/year. The LCA results indicate that the environmental benefits of leasing batteries over selling them are marginal. Leasing offers advantages such as user flexibility, and it encourages battery repair, repurposing, and recycling; however, there is insufficient evidence to support the cost-effectiveness of leasing or significant environmental improvements over traditional buying and selling models. The findings may be relevant for other business models targeting a substantial user base in the BEV domain, such as battery-as-a-service (BaaS) programs and battery swapping. [Display omitted] • This study compares circular battery leasing to linear selling-buying models for BEVs. • Leasing batteries can be as profitable as selling them, but higher revenues may be needed. • Battery purchasing remains cost-effective for users driving over 10,000 km/year. • LCA shows marginal environmental benefits from leasing batteries compared to selling batteries. [ABSTRACT FROM AUTHOR]

Energy flow analysis is one of the most effective methods for assessing and improving vehicle efficiency. It can provide the most effective support for the development and optimization of a hybrid electric vehicle (HEV) control strategy for improving the performance and energy saving of HEVs. In this study, an energy flow experiment of a series–parallel HEV is conducted in a climate chamber under the Worldwide Light-duty Test Cycle to address high fuel consumption. Then, GT-Suite and MATLAB/Simulink co-simulation models for the tested vehicle are built and calibrated. The problems of the original HEV control strategy are addressed using the analysis of vehicle energy flow distributions, operating conditions, and energy efficiency of key parts. Then, two optimized vehicle control strategies (termed Opt1 and Opt2) are employed to test vehicle fuel savings using a calibrated co-simulation model. The specific optimized vehicle control strategies primarily include canceling the series and parallel charging in the braking state, adding the engine direct-drive mode, and reducing the proportion of the parallel mode. The simulated results show that the 100 km fuel consumptions of Opt1 and Opt2 are reduced by 7.1% and 7.5%, respectively, compared with the original vehicle. The proportion of energy that drives the vehicle directly through the engines of Opt1 and Opt2 is increased from 45.7% to 70.2% and 61.6%, respectively. The total vehicle energy losses for Opt1 and Opt2 decrease from 29.0% to 22.5% and 22.9%, respectively. Although the engine of the original vehicle exhibits high thermal efficiency, the efficiency of the entire powertrain system is the lowest, resulting in the highest fuel consumption. [ABSTRACT FROM AUTHOR]

The trend of vehicle electrification is rapidly growing and is likely to continue in the future. However, electric vehicles (EVs) with high vehicle performance, energy efficiency, and low cost are required to satisfy industry and customer needs. Design optimization is an important method that can significantly improve the performance of batteries and EVs. However, only a few studies integrate accurate full-order electrochemical models of lithium-ion batteries into optimization frameworks of the battery module, pack, and EV design owing to complex modeling with huge computational costs. Multi-scale models with high efficiency and accuracy are required to provide optimal design, considering the overall system across scales. This study develops an efficient multi-scale design optimization framework for EVs, including four levels from the battery cell level to the marketing level. In the engineering domain, three levels of fundamental physics-based models are developed—an electrochemical cell, thermal module, and EV dynamics—to describe the performance of EVs. In the marketing domain, a utility model is used to maximize profit while considering customer preferences and costs. In addition, the vehicle model at the top level of the engineering domain is linked with the marketing model. Thus, all four model levels are hierarchically connected and can pass information. The multi-scale design optimization problem is solved by applying an analytical target cascading (ATC) method specialized for multi-level optimization. Through the ATC framework, optimization problems at each level can be independently solved while satisfying the targets from upper levels. Results show that each level successfully communicates with the others and carries out optimization to maximize profit while satisfying various constraints. ATC reduces the computational cost by 3% compared to the conventional all-in-one (AIO) approach. Parametric studies provide insights into how these design parameters, constraints, and operating conditions affect each other. To the best of our knowledge, this study is the first attempt toward developing multi-scale level modeling of EVs, which covers the marketing (very high business level) down to the cell (very detailed engineering level) while satisfying multi-level safety constraints. [ABSTRACT FROM AUTHOR]

Rapid motorization has made vehicles become one of the major sources of air pollution and poses substantial risk to human health. Accurate estimation of spatiotemporal variation and driving factors of vehicle emissions will be valuable for pollution control and public health protection. Here, taking polycyclic aromatic hydrocarbons (PAHs) as an example, a dynamic vehicle emission model (DVEM) was developed to assess vehicle emissions. The inventories of vehicle emissions from 2002 to 2017 were estimated under different emission standards and vehicle kilometers traveled for all types of vehicles in China. Vehicle PAHs emissions peaked 1586.85 tonne (t) in 2012 and declined by 36% in the follow-up five years because the vehicle growth has been offset by the upgraded emissions standards. There were remarkable variations among different provinces in China. The vehicle emissions are higher in eastern coastal provinces like Guangdong, Shandong, Jiangsu, and Zhejiang, while lower in the western provinces except Xinjiang. Motorcycles (44.1%) and light duty vehicles (17.8%) were the main contributors to PAHs emissions. The higher urbanization within a region, the larger its vehicle emission density. Urban road density may be linked with the number of on-road vehicles, which is the real driving factor of emissions. Therefore, integrated management should be taken by government to reduce the impacts of vehicle PAHs emissions. [Display omitted] • Major PAHs emissions were from LDV and Motorcycles. • Vehicle PAHs emission peaked in 2012 and declined in the follow-up five years. • Vehicle PAHs emissions in eastern China are significantly higher than the west. • Urban road density is the most important driving factor for vehicle emissions. • Integrated management should be strengthened to reduce vehicle PAHs emissions. [ABSTRACT FROM AUTHOR]

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services. [ABSTRACT FROM AUTHOR]

In many countries, the market for electric vehicles is not scaling up as expected despite huge public subsidies and technological progresses. One potential explanation is the absence of profitable business models that support commercialization and drive wide diffusion of electric vehicles. Business models that served conventional cars may not be appropriate for electric vehicles because of technological limitations such as shorter driving range and long charging cycles as well as higher acquisition cost. Recent years, however, have witnessed a growing body of scientific literature on electric vehicle business models. Nevertheless, the insights from this literature are fragmented and rarely address the business model with all its constituting elements. In this article, we review relevant literature on electric vehicle business models and distill key insights along each business model element. This way, our study provides a comprehensive and condensed overview of state-of-the-art research, while identifying potential directions for future research for scholars. • The paper integrates insights from scientific literature about EV business models. • The paper develops 16 key insights along the business model elements. • These propositions integrate knowledge on electric vehicle (EV) business models. • The literature review enables us to discover potential directions for future research. [ABSTRACT FROM AUTHOR]

Because a greater proportion of large-scale electric vehicles (EVs) are connected to the grid, their stochastic charging load has a significant impact on the power quality and economic operation of the distribution network. However, a random charging load can be transformed into flexible demand response resources through intelligent control of the EV charging process. Therefore, a bi-level optimization model for EV charging is proposed in this study based on the real-time charging price according to the regional grid load, providing more flexible charging strategies for EVs. First, the framework for a bi-level optimization control strategy for EV charging based on the load is developed. Based on the regional grid load, the charging price of each period is optimized in accordance with the load elasticity coefficient of the real-time charging price. Second, an upper-level EV charging price optimization model and a lower-level EV charging load optimization model are established. Third, an optimal control strategy for the scheduling of EV charging is developed, which considers the charging cost of EV users as the objective function and the standard deviation and peak-valley difference of the regional grid load as validation functions. Finally, using a regional power grid in China as the simulation object, the daily operation data of the grid are used to analyze and verify the proposed model. The simulation results demonstrate that the optimized real-time charging price can better respond to the regional grid load, smooth the regional grid load curve, reduce the peak-valley difference, promote clean energy consumption, and further lower the charging cost for EV users. • A framework of the bi-level optimization for electric vehicle charging is proposed. • Load elasticity coefficient of real-time charging price is introduced. • Real-time charging price model following the load of the power grid is constructed. • Impacts of real-time charging price change on electric vehicle load are analyzed. • Flexible charging strategies are provided for electric vehicle users. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are seen as a crucial tool to reduce the polluting emissions caused by the transport and power systems (PS) sector and the associated shift to a cleaner and more sustainable energy sector. The combination of EVs and solar photovoltaics (PV) in PS, specifically through the aggregation of EVs in parking lots (PLs), may improve the reliability and flexibility of the PS, assisting the power network in critical moments. This work proposes a novel aggregator agent in the energy system which is an EV charging station with an installed PV system. In this work, an optimal operation strategy for the solar-powered EV PL (EVSPL) operation is presented. The model optimizes the EVSPL's participation in various energy and ancillary services markets, including the effects of capacity payments. The results show that the EVSPL leads to higher profits. The EVSPL's participation in ancillary services is highly influenced by the prices. The results of this work show that this novel agent can actively participate in the energy system in an economically viable manner while respecting the technical constraints of the network and providing important ancillary services to the system operator. • A new agent, the electric vehicle solar-powered parking lot (EVSPL) is introduced. • An optimal operational strategy for the EVSPL is developed. • The EVSPL participates in both the wholesale and ancillary energy markets. • Results show that the EVSPL can operate in an economically viable manner. • The EVSPL reduces power system emissions and operating costs for electric vehicles. [ABSTRACT FROM AUTHOR]

Relying on life cycle assessment (LCA) to evaluate product-service systems (PSSs), and more specifically car sharing systems, involves many challenges. Car sharing services include free-floating car sharing, which enables users to take and leave vehicles anytime and anywhere within a limited service area. This paper proposes a model of a free-floating electric car sharing system in which eight parameters that influence environmental impacts may be identified. Among these parameters are the rate of use of the vehicles, standard trip representative of the service's actual use, vehicle model used within the service, and electric mix used to charge the vehicles. Adapting the life cycle assessment methodology to the studied system makes it possible to link the studied parameters to the indicator values of the service's environmental impact. The environmental indicators considered are global warming potential (GWP), photochemical oxidation potential (POCP), eutrophication potential (EP), and abiotic resource depletion potential (ADP). As a result, by using a design of experiments, it is possible to prioritise the eight system parameters according to their influence on the four environmental impact indicators. More specifically, the experiment demonstrates that the electricity mix has a major influence on the GWP and POCP indicators. With regard to the ADP indicator, the vehicle model used in the service is the most influential parameter by far. The use rate and trip type parameters have significant effects on the four environmental indicators. Finally, the experiment also shows that the results heavily rely on the study's methodological choices. [ABSTRACT FROM AUTHOR]

Nowadays, the agricultural supply chain as one of the key fields of food production is considered which has recently attracted much attention from researchers. This paper focuses on optimizing a closed-loop citrus supply chain. To this end, a multi-objective mathematical model is formulated that it attempts to minimize total costs, to maximize demands' responsiveness, and to minimize CO2 emissions as environmental damages. To solve the proposed model, five meta-heuristic algorithms include a multi-objective version of the recently published algorithm called tree growth algorithm (MOTGA) and four well-known algorithms called NSGA-II, NRGA, MOKA, and MOSA are utilized. It should be noted that these algorithms are tuned using the Taguchi method to achieve better performance and these are validated using the ε-constraint method in small size examples. It shows that the ε-constraint cannot solve the large size problems and it implies the NP-hardness of the problem. Moreover, the MOTGA is selected as the best approach with the least distance from the ideal point. Finally, to more measurement, a sensitivity analysis is performed and the results confirmed the efficiency of the proposed algorithms. Based on the results, it is shown that multiple transportation vehicles can reduce CO2 emissions and improved all objective functions' values. In addition, considering multiple vehicles improved responsiveness by about 14%. So, considering two new assumptions include CO2 emissions and multiple vehicles in this model, can lead to improve demand responsiveness and emissions reductions. [ABSTRACT FROM AUTHOR]