Battery Electric Vehicles Research Articles

SWOT Analysis of Future Trends in BEV-related Technologies: Xiaomi Automobile Co., Ltd's Latest Product as a Case Study

The new energy vehicle sector has benefited from numerous subsidies in response to the World Environmental Protection Organization's and governments' calls for a reduction in carbon emissions. Battery-electric vehicles (BEVs), the kind of EV with the lowest carbon emissions, have gained popularity in the automotive industry. Using a battery system as a power system not only improves efficiency but also ensures that it does not pollute the environment. Along with other technological advancements like the incorporation of cutting-edge chips in the central controls of the vehicle and latest AI systems, the convenience of BEVs for consumers keeps increasing. This paper introduces the new technology systems included in the latest products launched by Xiaomi, such as the new battery system, smart driving system, and all-round AI system. Then more analyses of the strengths, weaknesses, opportunities, and threats of BEVs in the future with the impact of new technology. The analysis not only raises some of the technical issues that need to be addressed in BEVs today but also points to the crisis that will exist in the BEV market in the future. After analyzing the situation, some recommendations and directions for in-depth research were given. And take Xiaomi's latest BEV product as an example to further do a SWOT analysis of Xiaomi's strategic development into the BEV market.

Anticipatory climate policy mix pathways: a framework for ex-ante construction and assessment applied to the road transport sector

ABSTRACT Globally, climate policy implementation is failing to deliver on the ambitions outlined in the Paris Agreement and countries’ Nationally Determined Contributions. We argue that a more strategic and anticipatory approach to policy mix design and implementation can help contribute to realizing these ambitions. Policy mixes need to co-evolve as energy transitions progress, in order to effectively target multiple transition challenges as these change over time. Accordingly, policy assessments need to both consider interactions between instruments and adopt a dynamic perspective, in terms of design logics and evaluative criteria. We highlight the need for a construction and assessment methodology for designing policy mix pathways which explicitly considers durability risks which could weaken support and lead to dismantling, and anticipatory design principles which can help mitigate these risks. Our paper addresses this gap by proposing and illustrating a novel framework for policy instrument mix pathway construction and ex-ante assessment. We identify generalizable durability challenges for effective anticipatory design: dynamic cost effectiveness, the distributive impacts and acceptance of pathways, along with fiscal and governance requirements for effective implementation. We demonstrate the value of the approach by application to the German light-duty vehicle sector transition. We construct and comparatively assess three illustrative pathways promising to deliver the German LDV sector 2030 GHG targets. The pathways differ in logic, and which of three main market dynamics driving a diffusion-stage transition they emphasize: battery electric vehicle (BEV) purchase, vehicle stock usage, and internal combustion engine (ICE) vehicle scrappage. Accordingly, the pathways utilize different instrument combinations, design features and calibrations. We also assess the illustrative pathways and discuss trade-offs. We argue that this approach can help improve climate policy planning and implementation processes, and increase the likelihood attaining ambitious mitigation targets.

Empirical Research on the Impact of Technological Innovation on New Energy Vehicle Sales

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China’s NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government’s public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation.

Concept Comparison of Electrically Driven Coolant Pumps for Battery Electric Vehicles

Concept Comparison of Electrically Driven Coolant Pumps for Battery Electric Vehicles

Deep reinforcement learning-based energy management system enhancement using digital twin for electric vehicles

Compared to conventional engine-based powertrains, electrified powertrain exhibit increased energy efficiency and reduced emissions, making electrification a key goal for the automotive industry. For a vehicle with a hybrid energy storage system, its performance and lifespan are substantially affected by the energy management system. Reinforcement learning-based methods are gaining popularity in vehicle energy management, but most of the literature in this area focuses on pure simulation, while hardware implementation is still limited. This paper introduces the digital twin methodology to enhance the reinforcement learning-based energy management system for battery and ultracapacitor electric vehicles. The digital twin model can exploit the bilateral interdependency between the virtual model and the actual system, which improves the control performance of the energy management system in real-time control. The physical model is established based on a hardware-in-the-loop simulation platform. In addition, battery degradation is also considered for prolonging the battery lifespan to reduce operating costs. The validation results of the trained reinforcement learning agent illustrate that the digital twin-enhanced Q-learning energy management system improves the energy efficiency by 7.08 % and reduces the battery degradation by 25.28 %.

Environmental impacts of battery electric light-duty vehicles using a dynamic life cycle assessment for qatar’s transport system (2022 to 2050)

Environmental impacts of battery electric light-duty vehicles using a dynamic life cycle assessment for qatar’s transport system (2022 to 2050)

Hybrid LSTM-GBRT based machine learning technique implementation for electric vehicle sales prediction analysis

ABSTRACT The investigation of machine learning models in Europe and India for predicting future sales of electric cars (EVs) worldwide has been the focus of present research. In this study, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), Linear Regression, hybrid long- and short-term memory neural network model and gradient boosted regression trees model (LSTM-GBRT) models based machine learning techniques are compared for performance analysis to predict EV sales. Moreover, in this study, a hybrid LSTM-GBRT technique was developed, which forecasts EV sales, particularly for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) over the next five years. During the prediction of EV sales, it has been validated that hybrid LSTM-GBRT technique has better predictive power. In addition, the present study attempts to provide estimated sales statistics for the designated locations. The results indicate that EV industry is evolving, with India showing a strong growth rate in BEV sales and establishing itself as a major participant in the global EV scenario by showing a notable increase in its share of worldwide EV sales volume by 2027. Conversely, the performance of EV sales in Europe has been analysed and validated through the proposed LSTM-GBRT model.

Harvesting heterogeneity: Selective expertise versus machine learning.

The heterogeneity of outcomes in behavioral research has long been perceived as a challenge for the validity of various theoretical models. More recently, however, researchers have started perceiving heterogeneity as something that needs to be not only acknowledged but also actively addressed, particularly in applied research. A serious challenge, however, is that classical psychological methods are not well suited for making practical recommendations when heterogeneous outcomes are expected. In this article, we argue that heterogeneity requires a separation between basic and applied behavioral methods, and between different types of behavioral expertise. We propose a novel framework for evaluating behavioral expertise and suggest that selective expertise can easily be automated via various machine learning methods. We illustrate the value of our framework via an empirical study of the preferences towards battery electric vehicles. Our results suggest that a basic multiarm bandit algorithm vastly outperforms human expertise in selecting the best interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Urban Transport Policies and Net Zero Emissions in the European Union

This review discusses the role of road transport policies in achieving the European Union's ambitious zero-emission target for the transportation sector by 2050 and provides an overview of the empirical literature that studies the effectiveness of urban transport policies. The analysis is divided into two parts. First, we study policies that are relevant in the transitional phase from internal combustion engine vehicles to battery electric vehicles (BEVs), then we discuss policies that will remain relevant as we switch entirely to BEVs. In the best-case scenario, the transition can be driven by fuel taxes, but in case fuel taxes are not feasible or acceptable, a combination of penalties for buying polluting vehicles and subsidies for scrappage of such vehicles might be the best compromise. In the long run, road space will be increasingly at a premium, and to ensure its efficient use, increasingly sophisticated road-pricing policies will be needed.

The role of battery electric vehicles in off-peak hour deliveries: Sustainability assessment of a case study in Stockholm

City logistics faces critical challenges in optimising resource management, utilising time effectively, and reducing costs, particularly in the context of growing environmental concerns. Battery Electric Vehicles (BEVs) and Off-Peak Hour Deliveries (OPHD) have emerged as promising solutions. Despite considerable research efforts, more research is needed to evaluate the impacts of using BEVs during OPHD. To address this need, this paper describes how data from a case study in Stockholm were used to develop a comprehensive overview of the sustainability impacts of OPHD carried out with BEV. A multi-method approach was applied, including an indicator framework, Life Cycle Costing, and a Social Life Cycle Assessment to consider all sustainability dimensions holistically. Our findings reveal that, while BEVs are not yet cost-competitive with Internal Combustion Engine vehicles for daytime operations, OPHD can mitigate these costs by increasing the vehicle utilisation and distances travelled. Furthermore, OPHD with BEV can offer substantial benefits by increasing accessibility and reducing emissions. However, challenges such as increased employee-related transport costs and infrastructure needs must be addressed. The main contributions of this paper are practical, as we add knowledge about the impacts of OPHD using BEV, and methodological, as we used a multi-method approach to holistically assess sustainability impacts. Future research should focus on long-term impacts and refine assessment methods to support the sustainable development of urban logistics. This study highlights the importance of using holistic sustainability assessment to inform and enable stakeholders and decision-makers to leverage the full potential of OPHD when shifting to more sustainable transport.

A cost-effectiveness comparison of renewable energy pathways for decarbonizing heavy-duty vehicles in China

Decarbonizing heavy-duty vehicles is becoming increasingly important, yet multiple renewable-based decarbonization pathways have not been compared systematically. To fill this gap, this study develops a techno-economic and environmental model that integrates the fuel cycle and vehicle cycle to assess these pathways for China. The model includes two energy storage technologies: batteries and hydrogen, three energy transmission options, and two vehicle types: fuel cell electric vehicles and battery electric vehicles. Five distinct low-carbon pathways are evaluated on a ton-kilometer basis, including cost, greenhouse gas emissions, and abatement cost relative to conventional diesel trucks. Results indicate that the most cost-effective battery electric vehicle pathway offers a 7 % lower ton-km cost than diesel trucks for a 49-ton gross vehicle weight. Battery electric vehicle charging combined with hydrogen for power generation is more cost-effective than direct use of hydrogen in fuel cell electric vehicles. All studied pathways can achieve over 80 % emission reduction compared to diesel trucks, with fuel cell electric vehicle pathways having the highest abatement costs of 103–117 USD/tonCO2. Analysis of the impact of gross vehicle weight and vehicle range indicates that battery electric vehicle pathways outperform fuel cell electric vehicle pathways across most dimensions. Costs of battery electric vehicle pathways do increase sharply for vehicle ranges beyond about 700 km, and fuel cell electric vehicles become more cost-effective relative to battery electric vehicles for ranges above approximately 1100 km, although all pathways are much more costly than diesel trucks at long ranges. The sensitivity analysis highlights the importance of powertrain efficiency and cost projections show that fuel cell electric heavy-duty vehicles can achieve cost-parity before 2030. These results highlight the desirability of promoting the adoption of heavier battery electric vehicles and providing more targeted subsidies for long-range fuel cell electric vehicles.

Regional vehicle energy consumption evaluation framework to quantify the benefits of vehicle electrification in plateau city: A case study of Xining, China

Vehicle electrification holds significant potential for driving both decarbonization and improved energy efficiency within the realm of road transportation. However, accurate quantification of energy consumption advantages offered by electric vehicles (EVs) has remained elusive, thus impeding their long-term development due to variations across cities and vehicle types. To address this challenge, we propose a novel regional vehicle energy consumption assessment framework that integrates a region-specific driving cycle (DC) database and advanced high-precision energy consumption models. The regional DC database is constructed using a Markov chain approach. The database is stochastic and diverse, while taking into account real driving features and regional geographic environments. Energy consumption models for internal combustion engine vehicle (ICEV), plug-in hybrid electric vehicle (PHEV) and battery electric vehicle (BEV) are constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with R2 of 0.86, 0.86 and 0.77 on the test set, respectively. According to the interpretability of the models, vehicle acceleration and vehicle specific power are considered to be the most important features affecting vehicle energy consumption. According to the evaluation results, compared to using ICEV, the promotion of PHEV and BEV in Xining, a plateau city, can reduce energy consumption by 27.83 % and 32.05 %, respectively. The framework not only unifies the scale of energy consumption evaluation among different vehicle types, but also its good generalization ability can be migrated and extended to the evaluation of vehicle energy consumption in various regions. The establishment of this framework will contribute to the promotion and popularization of electric vehicles, as well as the advancement of energy-saving initiatives in the transportation sector.

A Preliminary Methodological Framework of the Potential Hydrogen Economy for a Sustainable Transport in Malaysia

The transition to a hydrogen economy is vital for achieving sustainable transport in Malaysia, particularly in mitigating the environmental impact of heavy-duty mobility. This paper proposes a preliminary methodological framework to evaluate hydrogen's potential as a transformative solution for decarbonizing the transportation sector, focusing on key areas such as hydrogen production, storage, distribution, and vehicle integration, with an emphasis on green hydrogen from renewable sources. Our assessment reveals the significant potential of hydrogen to reduce greenhouse gas emissions and air pollutants, positioning it as a viable alternative to fossil fuels and battery-electric vehicles, especially in heavy-duty applications. The study assesses the technological feasibility, economic viability, and environmental benefits of adopting hydrogen in Malaysia, considering current infrastructure, production capabilities, and regulatory frameworks. Key challenges identified include the high costs of hydrogen production and infrastructure, technological barriers in storage and vehicle integration, and gaps in policy and market readiness. To address these, we propose strategic recommendations for stakeholders, highlighting the need for government support, public-private partnerships, and international collaborations. This framework aims to guide Malaysia’s transition to a hydrogen-powered transport sector, aligning with the nation’s sustainability and energy security goals while positioning Malaysia as a leader in hydrogen technology in Southeast Asia.

Energy system models should consider evolving charging profiles

Abstract Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle (LDV) electrification scenario in the U.S. from 2020-2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ~300 GW more solar PV) and activity (up to ~460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g., day, night, uncontrolled) but also how these evolve over time.

The Environmental Impacts of Future Global Sales of Hydrogen Fuel Cell Vehicles

During the last decade, developing more sustainable transportation modes has become a primary objective for car manufacturers and governments around the world to mitigate environmental issues, such as climate change, the continuous increase in greenhouse gas (GHG) emissions, and energy depletion. The use of hydrogen fuel cell technology as a source of energy in electric vehicles is considered an emerging and promising technology that could contribute significantly to addressing these environmental issues. In this study, the effects of Hydrogen Fuel Cell Battery Electric Vehicles (HFCBEVs) on global GHG emissions compared to other technologies, such as BEVs, were determined based on different relevant factors, such as predicted sales for 2050 (the result of the developed prediction model), estimated daily traveling distance, estimated future average global electricity emission factors, future average Battery Electric Vehicle (BEV) emission factors, future global hydrogen production emission factors, and future average HFCBEV emission factors. As a result, the annual GHG emissions produced by passenger cars that are expected to be sold in 2050 were determined by considering BEV sales in the first scenario and HFCBEV replacement in the second scenario. The results indicate that the environmental benefits of HFCBEVs are expected to increase over time compared to those of BEVs, due to the eco-friendly methods that are expected to be used in hydrogen production in the future. For instance, in 2021, HFCBEVs could produce more GHG emissions than BEVs by 54.9% per km of travel, whereas in 2050, BEVs could produce more GHG emissions than HFCBEVs by 225% per km of travel.

Electrification of transportation: A hybrid Benders/SDDP algorithm for optimal charging station trading

This paper examines the electrification of transportation as a response to environmental challenges caused by fossil fuels, exploring the potential of battery electric vehicles and hydrogen fuel cell vehicles as alternative solutions. However, a significant barrier to their widespread adoption is the limited availability of charging infrastructure. Therefore, this study proposes the development of comprehensive charging stations capable of accommodating both battery and hydrogen vehicles to address this challenge. The energy is purchased from the day-ahead and intraday auction-based electricity markets, where the electricity price is subject to uncertainty. Therefore, a two-stage stochastic programming model is formulated while the price scenarios are generated utilizing a k-means clustering algorithm. Given the complexity of the proposed model, an efficient solution approach is developed through the hybridization of the Benders decomposition algorithm and stochastic dual dynamic programming. In the Benders master problem, day-ahead bidding variables are determined, whereas the Benders sub-problem addresses intraday bidding and charging station scheduling variables, employing stochastic dual dynamic programming to tackle its intractability. Additionally, we transform the mixed integer linear program model of the second stage problem into a linear program, confirming its validity through KKT conditions. Our model provides practical insights for making informed decisions in electricity markets based on sequential auctions. While the bidding curves submitted to the day-ahead market remain unaffected by scenarios, those submitted to the intra-day market show dependence on fluctuations in day-ahead market prices.

Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation. This study proposes an optimization methodology for public CS deployment, considering real charging behavior and interactions between battery electric vehicle (BEV) users and CSs. Realistic charging choice behavior is modeled based on surveys, and a dynamic charging decision chain is simulated, allowing interactions between BEV users and CSs through an agent-based modeling (ABM) approach. The charging-related activities are triggered by state of charge (SOC) levels randomly generated from distributions derived from real BEV operating data, including the random SOC levels at the start of a trip, the SOC level that prompts the user to charge the BEV, and the SOC level at which the user stops charging the BEV. A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs, to determine the location and the capacity of new CSs. The objective is to minimize the total time cost per BEV user, including travel time, charging time and waiting time in the queue. An application is conducted, for the deployment of fast CSs in Washington State, USA. The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems. The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs and leads to a more balanced utilization among CSs. The proposed approach is expected to contribute to better planning and deployment of public CSs, satisfaction of the booming charging demand and increased utilization of public CSs.

Establishing laser cutting of components for sulfide-based solid-state batteries

The demand for safe, power- and energy-dense, low-cost batteries is constantly increasing due to the global shift toward renewable energy sources and the associated need for battery electric vehicles and grid-level energy storage. However, current-generation lithium-ion batteries are struggling to meet the requirements and are reaching their physicochemical limits regarding energy density. Solid-state battery technology promises to improve the current state of the art in electrochemical energy storage and has proven itself in research, although not yet in commercial applications. One of the main challenges in bringing the technology to the market is the limited knowledge and research on the production of solid-state batteries with commercially viable technologies. This study investigates the application of laser cutting technology to improve the cutting process in the production of sulfide-based solid-state batteries. Challenges such as the production atmosphere, handling of the components, and differences compared to conventional battery components are discussed. Using a picosecond laser source, the influence of the pulse frequency, peak fluence, scanning speed, and laser passes was investigated to identify appropriate parameters for cutting sulfide-based composite cathodes. The findings were then successfully applied to the cutting of sulfide-based solid electrolyte separators. An improvement in edge quality compared to mechanical punching is demonstrated, marking a crucial step toward the commercialization of solid-state batteries.

Precharge switch based on metal–oxide–semiconductor‐controlled thyristor for power relay assembly of battery electric vehicles

AbstractThe power relay assembly (PRA) is an essential component to ensure the safety of an electric vehicle. We propose a semiconductor‐based precharge switch to overcome the shortcomings of the conventional mechanical relay, which is currently used as the precharge switch in the PRA. The gate‐driving circuit uses a photocoupler instead of a gate‐driving integrated circuit to reduce complexity and cost. Five devices, namely, two insulated‐gate bipolar transistors (IGBTs), two silicon carbide metal–oxide–semiconductor field‐effect transistors (SiC MOSFETs), and one metal–oxide–semiconductor‐controlled thyristor (MCT) are tested as candidate precharge switches. Each device is analyzed under varying battery voltage and fixed measurement conditions. The IGBT and SiC MOSFET burn below 600 V, while the MCT exhibits normal operation up to 800 V. The MCT precharge switch can solve shortcomings of the conventional mechanical relay and increase the performance with a simple gate‐driving circuit. Furthermore, the PRA with an MCT precharge switch can reduce the volume, weight, and cost while improving reliability.