Battery Electric Vehicles Research Articles

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Single- and Three-Phase Dual-Active-Bridge DC–DC Converter Comparison for Battery Electric Vehicle Powertrain Application

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications.

Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models

Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H2/day and a reduction of 0.4–1.0 Mt-CO2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H2 to 3–4 C$/kg-H2. The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally.

Investigating the effects of variable pulse charging on temperature during charging of battery electric vehicles

This study investigates the efficacy of variable pulse charging (VPC) on charging 18,650 secondary battery packs (12 V, 20 Ah) with NMC chemistry. VPC, a modern technique applied to secondary battery charging, aims to mitigate effects like a thermal runaway and thermal propagation caused by increased charging temperature. VPC involves varied duty factors (10 % to 90 %), charging rates (0.5C, 1C, 1.5C), and an optimal switching frequency determined through frequency response analysis. Its digital model is based on in situ electrochemical impedance spectroscopy measurements and MATLAB/Simulink simulations. At charging rates of 0.5C (10 A), 1C (20 A), and 1.5C (30 A), the temperature of the battery pack reaches 42.46 °C, 57.87 °C, and 70.37 °C, respectively. However, implementing VPC at a 50 % duty factor yields temperature reductions of 3.66 °C, 5.06 °C, and 5.42 °C, respectively. Similarly, employing VPC at a 10 % duty cycle results in temperature reductions of 11.2 °C, 17.7 °C, and 19.1 °C, respectively. The results indicate a significant reduction in charging temperature compared to constant current charging. Furthermore, the 1C condition is validated using a custom-made dual active bridge DC-DC variable pulse charger. In conclusion, applying optimal frequency-based VPC with specific duty factors demonstrates the potential to reduce temperature elevation during battery pack charging significantly.

The Influence of Incentives, Quality, and Product Availability on Attitude toward Purchase Decision of Battery Electric Vehicle at the Greater Jakarta

Transport and mobility have significant contributions to the emissions in urban cities in Indonesia. The Indonesian government has supported the adoption of electric vehicles (EVs) as part of the commitment to zero emissions by 2060. According to The Association of Indonesia Automotive Industries (GAIKINDO), in 2022 the electric vehicle contributes 1.49% of the total domestic passenger car sales in Indonesia. This EV population was too small compared to what the government targeted for the adoption of EVs. This study aims to analyze the influence of government incentives, product quality, and product availability on consumer’s attitudes toward purchase decisions of battery electric vehicles (BEV). A sample of 149 respondents, who have a driving license, live in the greater area of Jakarta were collected through a questionnaire-based primary data. Through a rigorous procedure of structural equation modeling (Smart-PLS), it is found that variables of government incentives and product quality have no significant effect on consumer purchase decision for BEVs. Product availability and attitude variable has positive and significant effect on consumer purchase decision for BEVs. Moreover, the product availability variable is imperative since it is found to be the most significant variable construct. Thus, as the managerial implication, BEV dealer and manufacturer could consider the availability of various choices of electric cars with the price and driving range capabilities. Future research is recommended to cover all types of EVs, collect wider respondents on different areas and also explore different variables that influence EV customer purchase decision.

Optimization Principles Applied in Planning and Operation of Active Distribution Networks

Optimization principles play an important role in the planning and operation of active distribution networks (ADNs), which are designed to handle the inrush of distributed generation (DG) resources like renewables and, nowadays, battery energy storage systems (BESSs) and electric vehicles (EVs) [...]

Battery electric vehicles in underground mines: Insights from industry

The implementation of battery electric vehicles (BEVs) in underground mining is relatively recent. BEVs offer several advantages over diesel machines, including enhanced working conditions through reduced noise and heat and the lack of toxic exhaust gases or diesel particulate matter. In addition to reducing greenhouse gases, they have the potential to reduce ventilation and air conditioning costs. Nevertheless, there are certain concerns about BEVs, in areas of productivity, fire safety, economic viability, user-friendliness, and potential electrical-related issues. To address these, two surveys were conducted, one among underground mine management and the other among mine personnel to ascertain their opinions and experiences of BEVs. The results indicated the primary motivators for mines to adopt BEVs were to create a healthier working environment and reduce carbon emissions. The factors hindering the implementation were high costs and the lack of proven reliability. Mine personnel appreciated BEVs for their quietness and reduced fluids and components; however, they had concerns about fire safety and limited battery duration. This study presents the extent of BEV use in underground mining and associated fire incidents and a summary of the survey results.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

Decarbonization costs for the Swedish heavy-duty road fleet: Circular economy insights on electric truck batteries

This study analyzes decarbonization costs for the Swedish heavy-duty road fleet under five scenarios, one taken from the EUCalc model as a reference scenario and four others driven by interviews: a high-speed transition with 100% battery electric vehicles (BEVs) across all distances; a high-speed transition with BEVs taking 100% of the market in local and regional distances and 40% in long distances, with the remaining 60% being fuel cell vehicles (FCVs) by 2050; a low-speed transition with BEVs market share increasing by 15% every five years, starting at 10% from 2025 for local and 2030 for regional and long distances; and a low-speed transition similar to the previous scenario, but with 60% of the electrified long-distance fleet to be FCVs. The system's expenses are then calculated through numerical modeling. The study links research on the costs of sustainability transition to a circular economy by analyzing the effect of charging range and temperature on battery degradation for BEVs and their impact on the batteries' valorization. In full electrification scenarios, despite lower operating expenses, the system incurs a higher total cost because of higher investment expenses. Charging–discharging pattern and temperature impact the remaining capacity, and therefore salvage value, of end-of-life batteries.

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

An Analysis of and Improvements in the Gear Conditions of the Automated Mechanical Transmission of a Battery Electric Vehicle Considering Energy Consumption and Power Performance

The design of the gear quantity and transmission parameters of a vehicle has large effects on its economical and power performance. This paper mainly researches the gear conditions (including the gear quantity and each gear’s transmission parameters) of two-gear and three-gear AMT (Automated Mechanical Transmission). This research uses Cruise software to build a multi-gear simulation model of a BEV (Battery Electric Vehicle) and adopts the LHS (Latin hypercube sampling) method to design an experiment plan and conduct a simulation experiment. This paper proposes a systematic method for influencing factor analyses and the optimization of transmission parameters, combining fuzzy theory, multiple regression, and particle swarm optimization. The research results show that the gear quantity allowing for optimal overall performance is three. The highest score obtained in the results of the simulation experiment for three-gear AMT is 11.15% higher than that of the two-gear AMT. The optimal design plan for the two-gear AMT is a small ig1 with a big k1, in which case the highest score of the regression model increases by 2.67% compared with that before modeling. The optimal design plan for the three-gear AMT is a big k1 with a big k2, in which case the highest score of the regression model increases by 12.78% compared with that before modeling. Then, this research uses PSO (particle swarm optimization) to further optimize the regression models and compares the difference between the highest scores in the results of the simulation experiment. The difference between the highest scores of the three-gear and two-gear AMT further increases to 21.95% after optimization. As shown in the results, the key factor influencing the performance of two-gear and three-gear AMT is gear quantity.

Promoting Sustainable Transportation: Factors Influencing Battery Electric Vehicle Adoption Across Age Groups in Thailand

Battery Electric Vehicles (BEVs) are a crucial innovation for achieving sustainable transportation and reducing greenhouse gas emissions, which are major contributors to global warming and climate change. While previous studies have explored attitudes towards BEV technology acceptance, few have examined the interplay of external factors such as government measures and adoption barriers in promoting sustainable mobility. This study addresses this gap by investigating the roles of government policies, usage obstacles and innovation diffusion in stimulating BEV purchase intentions, while applying the Innovative Diffusion Theory (IDT). Data from 3632 respondents in Thailand were analyzed using structural equation modeling (SEM) to examine causal relationships between factors. The results indicate that government policies supporting BEV users enhance innovation diffusion in society, leading to increased adoption intentions. Furthermore, effective policies help mitigate barriers to BEV usage, further encouraging adoption. The study also reveals that causal relationships of BEV usage intentions vary across age groups, highlighting the need for targeted approaches in promoting sustainable transportation. These findings contribute to the development of evidence-based policy recommendations to accelerate BEV adoption, supporting Thailand’s Carbon Neutrality goals and broader sustainable development objectives. By elucidating the complex dynamics of BEV adoption, this research provides valuable insights for policymakers and stakeholders working towards a more sustainable and environmentally friendly transportation sector.

Analisis Stakeholder dalam Percepatan Penggunaan Kendaraan Bermotor Listrik Berbasis Baterai di Kota Jambi

This study analyzes the role and influence of stakeholders in accelerating the adoption of Battery Electric Vehicles (BEVs) in Jambi City, Indonesia. Using a qualitative approach with a literature study method, this research identifies and maps key stakeholders, analyzes their influence and interests, and examines the implementation of BEV policies in Jambi City. The results indicate that despite commitments from various parties, BEV implementation still faces significant challenges, particularly in terms of infrastructure, regulation, and public awareness. The Jambi City Government plays a crucial role as facilitator and regulator, but successful implementation heavily depends on effective synergy and collaboration among stakeholders. This study recommends comprehensive strategies to optimize stakeholder roles, including developing local regulations, accelerating infrastructure development, and enhancing public education. With a holistic and collaborative approach, Jambi City has the potential to become a model for successful BEV adoption in medium-sized cities in Indonesia.

Exploration of the Traffic Safety of Battery Electric Vehicles: A Case Study of Tesla Vehicle-Involved Crashes in Pennsylvania, USA

After decades of efforts, the number of battery electric vehicles (BEV) has increased greatly around the world. Meanwhile, their unique technical features also bring many new traffic safety challenges. Using traffic crash data from Pennsylvania from 2018 to 2021, this study aims to identify characteristics and trends of BEV crashes by comparing those crashes involving Tesla vehicles (the premier BEV brand in the auto market of the United States) with crashes involving internal combustion engine vehicles (ICEV). First, Tesla and ICEV crashes are compared for crash severity, collision type, spatial and temporal distributions, and environmental features. Tesla crashes show no significant difference from those of ICEVs in severity, but they have many more rear-end and angle collisions. In addition, Tesla crashes are found to concentrate in peak hours and around noon, occurring mainly in urban areas, at intersections, and on state roads. A logistic regression model is then built to identify the important factors influencing the severity of Tesla crashes. The findings are expected to provide new insights which will help researchers understand better traffic safety issues around BEVs.

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Green light for bidirectional charging? Unveiling grid repercussions and life cycle impacts

Bidirectional charging, such as Vehicle-to-Grid, is increasingly seen as a way to integrate the growing number of battery electric vehicles into the energy system. The electrical storage capacity in the system can be enhanced by using electric vehicles as flexible storage units. However, large-scale applications of Vehicle-to-Grid may require significant expansion of distribution grids. Previous studies lack a comprehensive environmental assessment of related impacts. Contributing to this research gap, this article combines techno-economic grid simulations with scenario-based Life Cycle Assessments. The case study focuses on rural distribution grids in Southern Germany, projecting the repercussions of different charging scenarios by 2040. Besides a Vehicle-to-Grid scenario, a mixed scenario of Vehicle-to-Home, Vehicle-to-Grid, and direct charging is investigated. Results indicate that Vehicle-to-Grid charging increases grid impacts due to higher charging simultaneities and power losses, especially when following spot market prices. Despite these challenges, the secondary use of battery electric vehicles as storage units can offset adverse environmental effects. Bidirectional charging allows for higher use of volatile renewable energies and can accelerate their integration into the power system. When considering these diverse environmental effects, bidirectional charging scenarios show overall lower impacts on climate change per battery electric vehicle compared to direct charging. The insights provided are valuable for researchers, industry, utilities, and policymakers to understand the potential positive and negative impacts of large-scale battery electric vehicle integration. The article highlights the most influential parameters that should be considered before large-scale penetration.

The window of opportunity: a case study of the battery electric vehicle in Thailand

Purpose Thailand, although a late entrant, has emerged as a significant center for the production and export of internal combustion engine (ICE) vehicles in Asia. With the arrival of a disruptive battery-electric vehicle (BEV), this study aims to investigate whether Thailand can exploit this window of opportunity to leapfrog. Design/methodology/approach This paper uses data sourced from Thailand’s Department of Intellectual Property to analyze electric vehicle (EV) patent applications. This paper collected qualitative data and conducted interviews with several EV manufacturers. Findings Although Japanese automakers dominate essential EV technology patents, they trail behind Chinese automakers, which benefited from the Free Trade Agreement (FTA) in EV sales. The incumbents, Japanese and Western, choose to expand their production of hybrid electric vehicles (HEVs) and plug-in hybrid vehicles. ICE technology does not constrain new entrants, such as Chinese automakers, Japanese newcomers and Thai newcomers. Compared to Japanese carmakers stuck with ICE and HEV technologies, they have been transitioning to BEVs at a faster pace. BEV has opened the door for large Thai indigenous energy corporations to enter the automotive industry by focusing on niche areas (nonfour-wheel vehicles) and supporting businesses like charging stations. In summary, Thailand has successfully attracted foreign direct investment (FDI) into the EV sector, but it has not yet succeeded in developing indigenous technologies related to EVs. The Thai automobile sector fails to leapfrog because it is too tied up with Japanese automotive production networks and has an unfavorable FTA with China. Originality/value This study sheds light on the limitations of an industrial development strategy relying on openness to trade and FDI without adequately strengthening indigenous technologies and firms, as the country adopting the strategy fails to leapfrog when the window of opportunities created by disruptive technologies arrives.

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Scenario-Based Analysis of Electrification Effects on Value Creation and Employment Structures for the Automotive Industry in the Federal State of Baden-Wuerttemberg, Germany

The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for the European (EU27) passenger car as well as truck market 2040 with special focus on the highly export-oriented industrial automotive cluster in Baden-Wuerttemberg. To achieve this, both a moderate and a progressive market scenario were simulated using the scientifically validated DLR VECTOR21 vehicle technology scenario model, based on two different parameter sets derived from actual and possible framework conditions on the European car and truck markets. Based on a detailed analysis of the industrial branch, value creation, and employee structure in Baden-Wuerttemberg and its automotive cluster, the effects resulting from the transformation to electric mobility will be displayed. With detailed Fade-In and Fade-Out analysis, the shifts from internal combustion engine components to electrified components will be derived and illustrated for each employee segment in the automotive cluster in Baden-Wuerttemberg, which leads to completely new and original results at this level of detail. Furthermore, the detailed display of the automotive cluster in this study allows for regionalized statements on employment effects, considering, for the first time, not only the car but also the truck segment. The analysis shows that battery electric vehicles will achieve a share of 34% or 57% for new registrations on the German car market in 2030, depending on the scenario framework conditions. The resulting employment effects for the entire automotive cluster in Baden-Wuerttemberg reach −37,000 (−8%) or −66,000 (−14%) by 2030 with further negative development until 2040 (−155,000, −30%) for the respective scenarios. Employment segments, in particular powertrain-dependent production employees, are at risk, with a potential decline of up to −60%. R&D employees could be also be significantly, affected with a reduction in workforce of about −50%.