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This study aims to identify the factors influencing the intention to purchase electric vehicles for personal use among residents in major urban areas in Vietnam, specifically in Hanoi. This research was conducted using a non-probability sampling method to recruit 360 participants currently residing, studying, or working in the city of Hanoi. Statistical analysis was employed to process the collected data. The results reveal four groups of factors that have an impact on the intention to purchase electric vehicles among urban residents in Hanoi: (1) attitude (economic benefits, environment, safety, convenience, and design), (2) perceived behavioral control, (3) attractiveness of alternative modes of transportation, and (4) subjective norms. Based on the findings, the authors propose several policy implications to promote the development and use of electric vehicles in urban Hanoi. These may include encouraging the use of electric vehicles from economic, environmental, and safety perspectives, creating financial support programs or tax incentives for electric vehicle buyers, and improving public electric charging infrastructure to make electric vehicle usage more convenient. [ABSTRACT FROM AUTHOR]

Tuktuk, as a generalized connotation, serves as a widely used vehicle for urban transportation, adapted from Thailand by the Philippines. The creation of the electric-typed public vehicle has now been recognized as one of the modalities of public transportations, the etuktuk. This study investigated the factors influencing passengers' intention to use etuktuks as a mode of transportation in the Philippines by integrating the Theory of Planned Behavior with higher-order SERVQUAL dimensions. The objective was to understand how service qualities, attitudes, subjective norms, and perceived behavioral control impact passenger satisfaction and intention to use etuktuks. Data were collected from 501 respondents who had used etuktuks and were analyzed using partial least square structural equation modeling. The findings revealed that assurance, empathy, reliability, tangibility, and responsiveness significantly affected passenger satisfaction and intentions, with assurance being the most influential factor. Conversely, tangibles, such as the physical attributes of the etuktuk, were the least significant in shaping passenger preferences. Notably, a negative significant effect was observed between service quality and behavioral intention, indicating that while passengers are generally satisfied with etuktuk services, they may opt for alternative transportation options when available. These results highlight the need for improvements in etuktuk services, particularly in enhancing comfort, safety, and accessibility. Such improvements are crucial for encouraging wider adoption of etuktuks and fostering cleaner, more sustainable urban environments. By addressing the identified service quality issues and leveraging the research findings, stakeholders can better support the transition to more environmentally friendly and efficient transportation options. [ABSTRACT FROM AUTHOR]

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals.

The main objective of this paper is to develop a readiness index model that can serve as an analytical tool for exploring the achievements of the electrification of transportation systems. We have applied this readiness index model to evaluate the readiness positioning of China, Norway, and Sweden towards transportation electrification. We have chosen these three countries as they represent diversity among countries adopting electric transportation system solutions. Our developed readiness index model has four key dimensions: technological readiness, political readiness, societal readiness, and economic readiness. The embeddedness of all four dimensions in one model provides a multi-perspective way of analyzing and evaluating the readiness levels of countries moving towards transforming their transportation system. Therefore, we named the model a “multidimensional readiness index”. Our main conclusions are that political processes and decisiveness are the most important factors, followed by societal needs and economic ability, with the current technology as the fourth. Without the participation of dedicated and determined political decision makers, the other three factors are challenging to obtain. Political decision makers need to facilitate economic means to support the transformation in society and affected industries to balance the economic disadvantages of the electrically powered vehicle systems until they pass the cost disadvantage turning point. The development of relevant technology is no longer the significant barrier it was at the beginning of this transformation about 20 years ago. The technology for electrically powered transportation systems and devices is widely available now, although it is continuously evolving and being improved. Associated industries cannot be expected to initiate, finance, take risks, and take the lead in this global societal transformation without clear and strong political support.

Battery health assessments are essential for roadside energy storage systems that facilitate electric transportation. This paper uses the samples from the charging and discharging data of the base station and the power station under different working conditions at different working hours and at different temperatures to demonstrate the decay of the battery health of a roadside energy storage system under different cycles. In this paper, for the first time, the predicted state-of-health values are obtained by extracting the characteristic quantities affecting the battery health based on three indicators: the internal resistance, the rate of change of voltage, and the change of temperature. Data on state of health are clustered by K-Means, GMM, K-Means++ and divided into high, medium, and low levels. Using a comparison of the three methods, GMM clustering appears to be the best at reflecting the charging and discharging capacity of the battery.

Tuktuk, as a generalized connotation, serves as a widely used vehicle for urban transportation, adapted from Thailand by the Philippines. The creation of the electric-typed public vehicle has now been recognized as one of the modalities of public transportations, the etuktuk. This study investigated the factors influencing passengers’ intention to use etuktuks as a mode of transportation in the Philippines by integrating the Theory of Planned Behavior with higher-order SERVQUAL dimensions. The objective was to understand how service qualities, attitudes, subjective norms, and perceived behavioral control impact passenger satisfaction and intention to use etuktuks. Data were collected from 501 respondents who had used etuktuks and were analyzed using partial least square structural equation modeling. The findings revealed that assurance, empathy, reliability, tangibility, and responsiveness significantly affected passenger satisfaction and intentions, with assurance being the most influential factor. Conversely, tangibles, such as the physical attributes of the etuktuk, were the least significant in shaping passenger preferences. Notably, a negative significant effect was observed between service quality and behavioral intention, indicating that while passengers are generally satisfied with etuktuk services, they may opt for alternative transportation options when available. These results highlight the need for improvements in etuktuk services, particularly in enhancing comfort, safety, and accessibility. Such improvements are crucial for encouraging wider adoption of etuktuks and fostering cleaner, more sustainable urban environments. By addressing the identified service quality issues and leveraging the research findings, stakeholders can better support the transition to more environmentally friendly and efficient transportation options.

Lithium-ion batteries are an excellent choice for electric transportation because of their high energy density, minimum self-discharge, and prolonged cycle life. The performance of electric transportation depends on the battery management system (BMS) for efficient functioning in vehicles. The state of charge (SOC) is one of the crucial BMS parameters to indicate the available charge in the vehicle. A reliable and accurate SOC prediction is crucial for an effective electric vehicle operation but SOC estimation is challenging since it depends on multiple variables including ambient temperature, battery age, charging, and discharging current. The data-driven techniques use an approach to run sophisticated algorithms on a vast quantity of measured battery data to understand its behavior. Lithium-ion battery state of charge assessment poses a complex difficulty. Temperature and aging affect the non-linear connection between voltage and SOC, accurate current measurement is an essential parameter that requires rigorous calibration to manage inaccuracies. Estimation is further complicated by hysteresis effects during charge and discharge cycles, different C-rate dependencies, and state of health parameters. To solve critical challenges, the paper highlights the recent advancements in model-based approaches, coulomb counting techniques, and machine learning methodologies. By summarizing the basic principles and presenting a comprehensive overview of SOC estimation through deep learning, the review paper aims to serve as a valuable resource for researchers, and practitioners in the field of battery management systems for electric transportation applications. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop a readiness index model that can serve as an analytical tool for exploring the achievements of the electrification of transportation systems. We have applied this readiness index model to evaluate the readiness positioning of China, Norway, and Sweden towards transportation electrification. We have chosen these three countries as they represent diversity among countries adopting electric transportation system solutions. Our developed readiness index model has four key dimensions: technological readiness, political readiness, societal readiness, and economic readiness. The embeddedness of all four dimensions in one model provides a multi-perspective way of analyzing and evaluating the readiness levels of countries moving towards transforming their transportation system. Therefore, we named the model a "multidimensional readiness index". Our main conclusions are that political processes and decisiveness are the most important factors, followed by societal needs and economic ability, with the current technology as the fourth. Without the participation of dedicated and determined political decision makers, the other three factors are challenging to obtain. Political decision makers need to facilitate economic means to support the transformation in society and affected industries to balance the economic disadvantages of the electrically powered vehicle systems until they pass the cost disadvantage turning point. The development of relevant technology is no longer the significant barrier it was at the beginning of this transformation about 20 years ago. The technology for electrically powered transportation systems and devices is widely available now, although it is continuously evolving and being improved. Associated industries cannot be expected to initiate, finance, take risks, and take the lead in this global societal transformation without clear and strong political support. [ABSTRACT FROM AUTHOR]

Battery health assessments are essential for roadside energy storage systems that facilitate electric transportation. This paper uses the samples from the charging and discharging data of the base station and the power station under different working conditions at different working hours and at different temperatures to demonstrate the decay of the battery health of a roadside energy storage system under different cycles. In this paper, for the first time, the predicted state-of-health values are obtained by extracting the characteristic quantities affecting the battery health based on three indicators: the internal resistance, the rate of change of voltage, and the change of temperature. Data on state of health are clustered by K-Means, GMM, K-Means++ and divided into high, medium, and low levels. Using a comparison of the three methods, GMM clustering appears to be the best at reflecting the charging and discharging capacity of the battery. [ABSTRACT FROM AUTHOR]

Abstract Structural batteries are attractive for weight reduction in electric transportation. For their practical applications excellent mechanical properties and electrochemical performance are required simultaneously, which remains a grand challenge. In this study, we present a new scalable and low‐cost design, which uses a quasi‐solid polymer electrolyte (QSPE) to achieve both remarkably improved flexural properties and attractive energy density. The QSPE has a high ionic conductivity of 1.2 mS cm−1 and retains 91% capacity over 500 cycles in graphite/NMC532 cells. Moreover, the resulting structural batteries achieved a modulus of 21.7 GPa and a specific energy of 127 Wh kg−1 based on the total cell weight, which to our knowledge is the highest reported value above 15 GPa. We further demonstrate the application of such structural batteries in a model electric car. The presented design concept enables the industrialization of structural batteries in electric transportation and further applications to improve energy efficiency and multifunctionality.

Abstract The battery management system (BMS) is the main safeguard of a battery system for electric propulsion and machine electrification. It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels. In addition to effectively monitoring all the electrical parameters of a battery pack system, such as the voltage, current, and temperature, the BMS is also used to improve the battery performance with proper safety measures within the system. With growing acceptance of lithium-ion batteries, major industry sectors such as the automotive, renewable energy, manufacturing, construction, and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefined the world of energy storage. Hence, the functional safety considerations, which are those relating to automatic protection, in battery management for battery pack technologies are particularly important to ensure that the overall electrical system, regardless of whether it is for electric transportation or stationary energy storage, is in accordance with high standards of safety, reliability, and quality. If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events, then the environment, people, and property could be endangered. This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems, particularly in areas concerned with hazardous environment. The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards. A comprehensive evaluation of the components, architecture, risk reduction techniques, and failure mode analysis applicable to BMS operation was also presented. The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.

Forecasted growth in aviation sector emissions has motivated research into reduced-emissions aircraft designs. All-electric aircraft (AEA) are of unique interest due to negligible in-flight emissions. In contrast to fuel-based aircraft, AEA emissions vary depending on power sector emissions intensity at point of departure. The present work identifies regional pathways for future AEA to reduce greenhouse gas emissions through use of novel energy and emissions models spanning the entirety of the domestic commercial aviation sector across eight separate scenarios considering different emerging battery cells and emissions scenarios. While regions with high power sector emissions intensity are ill-suited for future electrification, clear regional potential for future AEA is demonstrated, with flight networks in the Pacific Northwest, California, and the East Coast. Airports in cities such as Atlanta and Washington, D.C. are clear potential candidates for future AEA hubs, while electrifying flights between Portland, Oregon, and Seattle, Washington offers greatest potential impact. • All-electric aircraft (AEA) offer regional potential for GHG emissions reduction. • High-impact regions include the Pacific Northwest, California, and the East Coast • Reducing power sector emissions intensity is critical for broader AEA impact • A three- to four-fold increase in specific energy is critical for viable AEA. [ABSTRACT FROM AUTHOR]

The study examines the cybersecurity environment of electric transportation networks using a machine learning-based methodology. It analyzes the behaviors of electric vehicles, charging patterns, cyber threat occurrences, and the performance of machine learning models. An analysis of electric vehicle (EV) data shows that there are differences in battery capacity and distances covered, suggesting the presence of possible weaknesses across different cars. Cyber threat logs provide a comprehensive view of the various levels of threat severity and the time it takes to discover them, illustrating the ever-changing nature of cyber threats in the network. Machine learning models have varying performance; ML003 and ML005 exhibit excellent accuracy and precision in threat identification, whilst ML002 shows significantly lower metrics. These results highlight the need of implementing flexible cybersecurity solutions to handle different electric vehicle behaviors and effectively reduce cyber risks. This research emphasizes the need of using proactive threat detection tactics in order to effectively address high-severity attacks. It also highlights the need for ongoing improvement of machine learning models to strengthen network security. This study enhances our comprehension of cybersecurity obstacles in electric transportation networks, highlighting the crucial significance of machine learning-based analysis in strengthening network resilience against ever-changing cyber threats.

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

The rapid introduction of electric vehicles (EVs) in the transportation market has initiated the concept of vehicle-to-grid (V2G) technology in smart grids. However, where V2G technology is intended to facilitate the power grid ancillary services, it could also have an adverse effect on the aging of battery packs in EVs. This is due to the instant depletion of power during the charge and discharge cycles, which could eventually impact the structural complexity and electrochemical operations in the battery pack. To address this situation, a median expectation-based regression approach is proposed for parameter estimation of vehicle batteries in V2G systems. The proposed method is built on the property of uncertainty prediction of Gaussian processes for parameter estimation while considering the cell variations as an exogenous function. First, a median expectation-based Gaussian process model is derived to predict the fused and individual cell variations of a battery pack. Second, a magnitude-squared coherence model is developed by the error matrix to detect and isolate each variation. This is obtained by extracting the cross-spectral densities for the measurements. The proposed regression-based approach is evaluated using experimental measurements collected from lithium-ion battery pack in EVs. The parametric analysis of the battery pack has been verified using D-SAT Chroma 8000ATS hardware platform. Performance evaluation shows an accurate estimation of these dynamics even in the presence of injected faults. [ABSTRACT FROM AUTHOR]

Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.

The battery management system (BMS) is the main safeguard of a battery system for electric propulsion and machine electrification. It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels. In addition to effectively monitoring all the electrical parameters of a battery pack system, such as the voltage, current, and temperature, the BMS is also used to improve the battery performance with proper safety measures within the system. With growing acceptance of lithium-ion batteries, major industry sectors such as the automotive, renewable energy, manufacturing, construction, and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefined the world of energy storage. Hence, the functional safety considerations, which are those relating to automatic protection, in battery management for battery pack technologies are particularly important to ensure that the overall electrical system, regardless of whether it is for electric transportation or stationary energy storage, is in accordance with high standards of safety, reliability, and quality. If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events, then the environment, people, and property could be endangered. This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems, particularly in areas concerned with hazardous environment. The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards. A comprehensive evaluation of the components, architecture, risk reduction techniques, and failure mode analysis applicable to BMS operation was also presented. The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration. [ABSTRACT FROM AUTHOR]

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution.

Transportation sector in Pakistan has been rapidly growing, leading to an increase in diesel and petroleum consumption, heightened energy import bill, air pollution, and traffic congestion. The China-Pakistan economic corridor (CPEC) is a cluster of different multi-billion-dollar projects with a significant emphasis on enhancing regional connectivity, and the transportation sector is considered its backbone. Electric road transportation appears to be a game-changer to tackle energy, economy, and environmental issues of a country. This research paper is focused on a scheme and valuation of deploying electric transportation (e-trans) for mass transit under the CPEC umbrella in Pakistan. This paper identifies barriers and challenges and explores various technological options for adopting suitable electric vehicles (EVs) for mass transportation. The paper investigates technical infrastructural requirements, financial, and policy implications for the successful deployment of the proposed electric transportation. In particular, it proposes an EV charging infrastructure based on solar power generation to avoid any electricity burden to recharge EVs from the national power grid. In order to be comprehensive, it presents a case study to develop an EV charging station network with an estimation of the cost for accommodating charging requirements of one thousand electric trucks and hundred mobile charging trucks. Obtained results demonstrate that merely 0.29% of the CPEC's energy sector approved budget will be incurred for deploying the proposed charging station network to support e-trans over the studies road segment, which appears to be a feasible initiative. Important factors which may influence the proposed scheme for implementation of e-trans under the CPEC is analyzed using SWOT analysis. The recommended set of policies for deploying electric transportation and its quantitative financial analysis will help the concerned stakeholders to discover and execute the necessary steps to ensure sustainable electric transportation under the CPEC in Pakistan.

Increasing awareness about global warming, and the unsteadiness of oil prices has increased interest in sustainable transportation among consumers as well as researchers. Electric vehicles (EVs) are considered as one of the sustainable road transportations for coping with these challenges. This paper presents a study of the economical, technical, social & attitudinal, infrastructural, and policy and regulatory barriers to the adoption of EVs within an Indian context. Additionally, the study also considered different stakeholders' perspectives on adopting EVs. Various stakeholders viz. consumers/public, businesses, governments, and the scientific community will boost the supply and demand of EVs. All stakeholders' coordinated efforts are creating the groundwork for a stronger EV environment, which will result in increasing acceptability. This study aims to identify the significant barriers to EV adoption. Based on an extensive literature review first, we identified the various barriers, and after that, we prioritized the identified barriers through the Fuzzy Analytical Hierarchy Process (FAHP). FAHP is employed to study the relevance and prioritize the barriers with the help of expert opinions from different stakeholders. The results strongly suggest that a technical barrier is the most critical one among all the stakeholders. The ranking of EV barriers provides a decision-making framework for managers and experts to pay more attention to the critical barriers/sub-barriers in order to eradicate them from the system for the successful adoption of EVs. It recognizes the value of a robust theoretical contribution to the area and intends to investigate theories or models that might offer a more solid foundation for the study in order to support the theoretical framework of this research. The study provides a stronger rationale for the research's importance and potential impact by introducing a comprehensive theoretical framework. [Display omitted] [ABSTRACT FROM AUTHOR]

Lithium-ion batteries having high energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. A noble supervised K nearest neighbors (KNN), support vector regressor (SVR), decision tree (DT), and random forest (RF) regressors machine learning algorithm are developed with different accuracy and usefulness for the state of health estimation from direct measurable indices of voltage, current, and temperature without inherited electrochemical characteristics like voltage hysteresis, aging, degradation level, operational, and environmental effect. Based on the battery dataset from Sandia National Laboratories, the proposed technique is validated. The comparison characteristics are provided with the help of mean absolute percent error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) techniques. The MAPE error at 15 °C for different regression algorithms SVR, DT, KNN, and RF are 2.791E-02, 1.607E-03, 9.957E-04, and 4.323E-03 respectively. The MAE error at 25 °C for different regression algorithms SVR, DT, KNN, and RF are 5.593E-02, 2.379E-03, 2.429E-03, and 5.073E-03 respectively. The RMSE error at 35 °C for different regression algorithms SVR, DT, KNN, and RF are 5.062E-02, 6.862E-03, 6.333E-03, and 1.275E-02 respectively. The MSE error percentages for the testing sample at 35 °C for different regression algorithms are 0.37 %, 0.01 %, 0.01 %, and 0.02 % respectively. The results indicate that KNN and DT exhibit higher precision when applied to lithium-ion batteries for electric vehicles. Collaborating with hardware manufacturers to exchange data has the potential to foster the creation of innovative battery health monitoring systems. [Display omitted] • Development of supervised ML algorithms for the state of health (SOH) estimation. • SOH estimation through current, voltage, temperature, and discharge capacity. • Capacity loss at 15 °C, 25 °C, and 35 °C at 0.5C charging and 1C discharging. • Algorithms comparison with the help of error metrics MAPE, MSE, RMSE, and MAE. [ABSTRACT FROM AUTHOR]

The time-varying nature of consumption patterns is critical in the development of reliable electric vehicles and real-time schemes for assessing energy autonomy. Most of these schemes use battery voltage observations as a primary source of information and neglect variables external to the vehicle that affect its autonomy and help to characterise the behaviour of the battery as main energy storage device. Using an electric bicycle as case study, we show that the incorporation of external variables (e.g., altitude measurements) improves predictions associated with evolution of the battery voltage in time. We achieve this by proposing a novel kernel adaptive filter for multiple inputs and with a data-dependent dictionary construction. This allows us to model the dependency between battery voltage and altitude variations in a sequential manner. The proposed methodology combines automatic discovery of the relationship between voltage and altitude from data, and a kernel-based voltage predictor to address an important issue in reliability of electric vehicles. The proposed method is validated against a standard kernel adaptive filter, fixed linear filters and adaptive linear filters as baselines on the short- and long-term prediction of real-world battery voltage data. [ABSTRACT FROM AUTHOR]

Decarbonizing Sweden’s transportation sector is necessary to realize its long-term vision of eliminating net greenhouse gas (GHG) emissions from the energy system by 2050. Within this context, this study develops two scenarios for the transportation sector: one with high electrification (EVS) and the other with high biofuel and biomethane utilization (BIOS). The energy system model STREAM is utilized to compute the socioeconomic system cost and simulate an integrated transportation, electricity, gas, fuel refinery, and heat system. The results show that electrifying a high share of Sweden’s road transportation yields the least systems cost. However, in the least-cost scenario (EVS), bioenergy resources account for 57% of the final energy use in the transportation sector. Further, a sensitivity analysis shows that the costs of different types of cars are the most sensitive parameters in the comparative analysis of the scenarios.

In support of global decarbonization efforts, the adoption of electric vehicles is proceeding rapidly across transportation sectors, population groups, and regions. However, access to electric vehicle charging infrastructure remains sparse and inequitable, and technical, economic, and procedural challenges have encumbered its expansion. Charging-as-a-service, which can mitigate cost and effort risks of charging equipment ownership, adds a means of meeting many of these challenges. As charging-as-a-service has not been analyzed or addressed in the scholarly literature, we synthesize media, marketing, and analogous scholarly publications to categorize and describe the functions asserted by existing charging-as-a-service vendors. We demonstrate and quantify the benefits of many of these functions via four example business cases, sited at a detached residence, a multi-unit dwelling, a consumer-facing business, and a commercial fleet depot. Outcomes show that savings and revenues realized via charging-as-a-service can reduce charging costs, beneficially shape grid loads, and make charging installation economical where it otherwise would not be. Discussion highlights the need to deploy charging-as-a-service in cooperation with initiatives promoting societal benefits, including charging access equity. [Display omitted] • First review of the purpose and functions of EV charging-as-a-service (ChaaS). • ChaaS enables transfer of EV charging operation and ownership burdens. • Simplifies negotiating the requirements of transportation and electricity systems. • Functions and benefits are demonstrated in residential and commercial business cases. • ChaaS should be deployed to advance societal interests, e.g. charging access equity. [ABSTRACT FROM AUTHOR]

The escalation of power system promotes the development of energy storage technologies (ESTs). Among all of ESTs, battery technologies develop quickly and diversely because of its huge application market. Aqueous redox flow batteries (RFBs) are very attractive to customers in the energy grid system, and their noticeable technological innovations in past decades are driving them to gradually replace the conventional ESTs under certain circumstance. Here, the first fully-flow-able zinc–nickel flow battery (ZNFB) is preliminary reported in this paper, and its superior performance is supposed to be suitable for both large-scale storage need and carry-on powertrain in cars. Through using semi-solid fuel cell (SSFC) technology, we incorporates the beneficial features of Zn/Ni chemistry (essentially sustainable, eco-friendly and deposit-abundant) into RFB structure to make a “hybrid” flow battery system, which can take the advantage of both. The relationship between carbon loading and suspension conductivity is determined. Electrochemical properties of ZNFB as static test, cycling test, and fully flowing test are studied to demonstrate our design. [ABSTRACT FROM AUTHOR]

This paper is concerned with the simultaneous optimal component sizing and power management of a fuel cell/battery hybrid bus. Existing studies solve the combined plant/controller optimization problem for fuel cell hybrid vehicles (FCHVs) by using methods with disadvantages of heavy computational burden and/or suboptimality, for which only a single driving profile was often considered. This paper adds three important contributions to the FCHVs-related literature. First, convex programming is extended to rapidly and efficiently optimize both the power management strategy and sizes of the fuel cell system (FCS) and the battery pack in the hybrid bus. The main purpose is to encourage more researchers and engineers in FCHVs field to utilize the new effective tool. Second, the influence of the driving pattern on the optimization result (both the component sizes and hydrogen economy) of the bus is systematically investigated by considering three different bus driving routes, including two standard testing cycles and a realistic bus line cycle with slope information in Gothenburg, Sweden. Finally, the sensitivity of the optimization outcome to the potential price decreases of the FCS and the battery is quantitatively examined. [ABSTRACT FROM PUBLISHER]