Battery Electric Vehicle Market 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.

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project.

An Empirical Study of the Policy Processes behind Norway’s BEV-Olution

Norway’s large battery electric vehicle (BEV) market and fleet are not the result of a comprehensive policy plan. Using the multiple streams (MS) framework and document analysis, it was identified that the most important Norwegian BEV policy decisions were made using inadequate policy processes that fall outside of traditional politics. This is contrary to the MS framework postulate that three independent streams of problems, policy solutions, and politics must align to pave the way for new policies. Politicians had limited information about the effects of policies they introduced in this “learning by doing process”. Impact assessments were rarely made. The decision rationale was often not documented. The future market expectation and thus the national budget consequences were low when important policy decisions were made, whereas the political gain was high. The processes were more aligned with traditional politics after 2014. The ambitious ZE vehicle targets for 2025 and the climate policy targets for 2030 locked in incentives, despite rising tax losses. In sum, these developments created the world’s largest per-capita BEV market. To avoid negative issues and keep the BEV policies’ potential to support the BEV transition, politicians should ensure that sufficient knowledge is available when making decisions about future policies. Such decisions should be taken transparently within traditional politics, be properly assessed as with EU policy processes, and regularly reviewed as with the California ZEV mandate process. The required knowledge should be developed in open-access research.

Market Data Exploitation: Exemplified by the Battery Electric Vehicle Market

We propose a systematic data-driven approach for complexity reduction for metric data given as a table where the columns reference variables (features, attributes, characteristics, etc.) and the rows to samples (cases, products, machines, etc.). The approach introduces new variables, called principal components, which are a linear superposition of the original variables and capture most of the information to differentiate the several samples. The number of principal components is less than that of the original variables (complexity reduction). The analytical method is based on the Principal Component Analysis (PCA). Utilizing spectral theory, the PCA finds orthogonal directions in the original variable space where the highest variance occurs. These directions form the principal components. The proposed method is applied to the battery electric vehicle (BEV) market. Three principal components, interpretable as smallness, efficiency, and fun per euro, are identified, which cover 98 % of all the information on the market to differentiate offered electric vehicles. Furthermore, the positioning according to the original equipment manufacturers’ smallness, efficiency, and fun per euro is illustrated. The developed method can be applied to various problems, such as finding the most relevant aspects, gaining insights into complex situations, or identifying the most important drivers for system optimization.

Performance Assessment of Battery Electric Vehicles Using the TOPSIS Method

Worldwide interest in "hybrid and battery electric vehicles" has increased recently as a result of their ability to save on fuel, lessen reliance on foreign oil, and reduce greenhouse gas emissions. The effectiveness of the sub-systems that these vehicles are built with determines their overall success in large part. It is necessary to estimate these subsystems' parameters with great accuracy to improve their performances. "Battery electric vehicles (BEVs)", an eco-friendly type of vehicle, are crucial given that the automotive industry contributes significantly to carbon emissions. Due to the recent quick growth of the BEV market, it has grown to be a substantial challenge to evaluate BEV alternatives fully from the perspective of the consumer. By examining the fundamental characteristics of each BEV, this evaluation can be made. The use of "multiple criteria decision making (MCDM)" techniques is a useful tool for making the best BEV buying choice. Therefore, six BEVs are selected as options in this work. These vehicles are then ranked using TOPSIS based on technical specifications, such as Battery capacity, Range, Top speed, Quick charge time, Acceleration and Purchasing price. In this study TOPSIS method analyses the rank of Mercedes-Benz EQS as first, Audi e-tron GT as fourth, Porsche Taycan as fifth, Audi e-tron as third, Audi RS e-tron GT as second and Mercedes-Benz EQC as sixth. So, the result from the TOPSIS method shows that Mercedes-Benz EQS is highlighted as the best choice of the selected battery electric vehicles followed by the Audi RS e-tron GT.

Warpage modeling for semiconductor power module manufacturing flow improvement

The hybrid and battery electric vehicle market is heavily increasing the demand for semiconductor power modules. The manufacturing of such products is more complex than discrete devices. Some parameters, such as module deformation (warpage), play a major role. Power modules have to comply with customer Geometric Dimensioning & Tolerancing (GD&T) targets in order to avoid coolant leakage during operation and to guarantee mechanical compatibility with the driving board. Compliance with an appropriate flatness tolerance reduces rejects during manufacturing processes such as ultrasonic welding, and the consequent impact on reliability. Due to these requirements, there is a need to implement dedicated countermeasures during manufacturing flows and to develop the proper methodologies to monitor flatness tolerances. Appropriate component selection at the design stage also limits flatness through the prediction of deformations during assembly flows using finite element model. This activity outlines the manufacturing flow for a direct cooled semiconductor power module and presents a method for product optimization in terms of flatness tolerance using a dedicated finite element model that calculates the warpage deformation induced by baseplate soldering and different ceramic substrate layouts. Furthermore, we describe experimental methodologies for measuring power module flatness and straightness across the manufacturing phases. Dedicated experiments were conducted for methodological and design validation.

Spatial analysis of the BEV market and the corresponding charging infrastructure in Hungary

The adoption of fuel-efficient vehicles, especially battery electric vehicles (BEVs), is becoming an emerging priority on the global level. At first, it may seem that the major issue from the consumer side is the high price of BEVs that are not competitive with internal combustion (IC) vehicles. However, aspects like technological developments, battery safety measures, implemented safety features, design, range anxiety, charging infrastructure, environmental consciousness and sustainability also play a significant role in the decision-making process of BEV purchasing. This paper examines the connection between BEV registrations and Hungary's available public charging infrastructure. Data on the number of new BEV registrations, public charging stations and energy consumption from public charging are gathered for each county and region in Hungary. Until 2020, a division between the Eastern and Western parts of the country can be detected considering BEV adoption, but in 2021 this difference will diminish. Even though it can be seen from the raw data that the number of BEV registrations is growing faster than the number of available charging stations, existing stations prove to be properly located, covering regions that either have high BEV registrations or are part of transit paths with high traffic. Also, it is shown that BEV registrations grow proportionately higher in regions where more charging stations are available.

Falling short in 2030: Simulating battery-electric vehicle adoption behaviour in the Netherlands

The widespread adoption of battery electric vehicles (BEVs) is a crucial element in climate policy for the transport sector. To design effective policies to stimulate the uptake of BEVs, it is essential to understand the barriers and drivers that influence consumers' choices for a BEV. To this aim, we present a computational model named CODEC, a hybrid choice model that estimates the future market share of different vehicle types. The model gives insight into the different effects of technical, financial and other behavioural factors that influence the adoption decision. We included social factors and routine behaviour, which are rarely analysed in other research. We assessed the share in sales of BEVs and gasoline vehicles in the Netherlands between 2020 and 2030 for privately owned new cars. To initialize the model we used data from a survey on the perceptions of prospective car buyers (n = 1522). Our analysis shows that the BEV market share in new car sales will be between 26 and 40 % in 2030, well below the government target of 100 %. The analysis also shows that current barriers for BEV adoption: higher purchase price and lower driving range, will become less important over time. In 2030 routine purchasing behaviour and social factors are the main barriers for widespread BEV adoption. New policy measures are needed to lower these barriers. Factors that affect BEV adoption positively have a relatively small effect, so also measures to reduce the attractiveness of gasoline vehicles should be considered.

Subsidy Phase-Out and Consumer Demand Dynamics: Evidence from the Battery Electric Vehicle Market in China

Abstract This paper quantifies the impact of battery electric vehicle (BEV) subsidy program in China. We build a structural model of dynamic demand and Bertrand Nash supply to study price elasticity and changes in production costs. The model highlights four channels through which the subsidy program impacts the market: the temporal elasticity, in response to a current price change, the intertemporal elasticity, in response to a future price change, and the multiplication effects through peer and learning by doing (LBD). Combining these estimates, we simulate outcomes under four subsidy schemes and find a phase-out policy could be the most costeffective while achieving higher sales promotion compared with alternative policies that provide larger subsidies over more prolonged periods.

Electric vehicle adoption in a mature market: A case study of Norway

The transportation sector, led by the automobile, is the major contributor to greenhouse gas emissions and increasing global warming. Transport electrification is one of the effective solutions to reduce oil dependency and mitigate environmental and climatic impacts from road transportation. Norway aims to be a major force in the electric vehicle market and has the ambition to have all newly sold light-weight vehicles be zero-emission by 2025. The share of battery electric vehicles (BEVs) in the auto market in Norway had reached up to 64.5% in 2021, indicating that Norway has recently become a well-matured BEV market. Some previous studies have investigated the drivers of BEV adoption in different countries but primarily in areas with low BEV market share. The drivers of a well-mature market with high BEV adoption remain poorly understood. Additionally, some existing works ignored the impacts of climatic conditions on BEV adoption, although climate conditions have been considered as one of the most important factors that impact BEV performance. In this study, we estimated how socioeconomic drivers and climatic conditions affect the spatial patterns of Norwegian BEV adoption in 2019 using the conditional autoregressive models. We found that the BEV adoption in Norway have a significant clustering pattern. Norwegians in urban areas with more available charging stations and having higher income levels and demand for travel are more likely to adopt BEVs. Municipalities with lower minimum temperatures and a higher proportion of elderly people often have a lower BEV adoption rate. Our findings emphasized the impacts of climatic conditions on BEV adoption and could provide important insights into the late-stage BEV adoption market for many countries in which the current BEV market is still in development.

New technology product introduction strategy with considerations for consumer-targeted policy intervention and new market entrant

The emergence of new technology products brings systematic changes to the market, and appropriate responses to such changes are essential for the survival and success of firms. This study investigated producers' optimal new product introduction (and positioning) strategies in various situations that require different sets of decisions. Specifically, we considered the diffusion of hybrid electric vehicles and battery electric vehicles (BEVs) in the South Korean automotive market. A model incorporating the strategic, forward-looking behaviors of both producers and consumers was used to derive producer's optimal new product introduction plan based on long-term market forecasting. Then, we conducted simulation analyses to investigate the benefit of forward-looking planning and how optimal strategies change depending on the policy interventions and market entrance of the new producer. The results show that a forward-looking strategy could improve the market performance of new products by up to 40 % compared to the myopic strategy, and the plan adjustment of the incumbent in response to competition could reduce competition-related losses by up to 9 %. Finally, stronger policy intervention and new market competition will motivate producers to introduce more, less-expensive BEVs in their product portfolio. Such responses significantly expand the BEV market, which is also a socially desirable outcome.

The relationship between public charging infrastructure deployment and other socio-economic factors and electric vehicle adoption in France

Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) offer a promising choice to replace fossil-fuel dependent Internal Combustion Engine Vehicles (ICEVs) with a low-emission transport solution. Governments, automotive manufacturers, and charging infrastructure operators have deployed market-boosting initiatives to incentivize purchase. Yet, their market diffusion is limited by several barriers. To shed light on the main barriers, and based on an extensive state-of-the-art, we used an original database and statistically analyzed the relationship of 21 socio-demographic, technical, and economic factors on BEV and PHEV market shares, separately, in 94 French departments from 2015 to 2019, using mixed-effect regression. We find different covariates related to BEV and PHEV sales, respectively, suggesting the two markets respond to different incentives. The number of available BEV/PHEV models and energy prices are positively correlated with BEV and PHEV adoption. Fast, ultrafast charger density and local incentives positively relate with BEV sales, while slow-and-normal charger density to PHEV sales. Contrarily, national subsidies, relative to vehicles’ prices, are negatively correlated with PHEVs sales and is open for further studies. Based on the results, policy recommendations are considered for the automotive industry, charging operators, and local authorities to draw a roadmap for electric mobility transition in France.

Choices of Potential Car Buyers Regarding Alternative Fuel Vehicles in South Korea: A Discrete Choice Modeling Approach

This study analyzes consumer preferences in South Korea for alternative fuel vehicles (AFVs), including battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs), instead of conventional fuel vehicles. A survey targeting 1500 potential car buyers in three years was conducted wherein the subjects stated their preferences depending on the varying conditions of AFV attributes and charger accessibility. Cluster-based multinomial logit and mixed logit models were developed to identify influential factors affecting consumer preferences. The models incorporated the sociodemographic characteristics of users, attitudinal perceptions, and vehicle attributes to capture their interactive impacts. The results of the estimated models suggest that a reduction in purchase price can substantially boost AFV sales, particularly those of HFCVs, with a direct elasticity of 1.78. Additionally, the models demonstrated that attitudinal perceptions, such as perceived environmental and economic benefits are significant factors. Moreover, potential car buyers who plan to buy one vehicle within one year showed the least preference for purchasing BEVs, indicating the importance of technology maturity in the BEV market. These findings can provide reasonable guidelines for establishing marketing strategies and stronger support to achieve the targeted market penetration of AFVs in a city or country.

Is government R&D subsidy good for BEV supply chain? The challenge from downstream competition

The new energy vehicle industry has gained a considerable amount of attention in recent years. However, the high cost of battery R&D reduces the competitiveness of battery electric vehicle (BEV) products. In this context, government might choose to support BEV companies in product innovation. In this paper, we examine the performance of government R&D subsidy by considering supply chain downstream competition. To capture the impacts of downstream competition, we model a two-tier supply chain consisting of a high-end encroachment manufacturer and an incumbent manufacturer who source from a same battery supplier. Result shows that downstream competition might reduce upstream battery R&D. In addition, when the consumer preference for the high-end substitutable BEV is less than a threshold, the BEV market scale will diminish due to competition. Furthermore, downstream competition might hurt the supplier’s profitability. For the impacts of R&D subsidy, result suggests that it performs better under competition on increasing supplier R&D activity. Regarding supply chain stakeholders, R&D subsidy always increases supply chain members’ profits and consumer surplus without competition. While, under downstream competition, it only increases supplier profit, but does not always benefits manufacturers and consumer surplus. Specifically, we identify the Pareto regions in which R&D subsidy increases both profits and consumer surplus. We extend the models by considering supplier non-uniform pricing and monopoly by the high-end BEV manufacturer. The above analyses are robust in the extended models and new findings are derived. Our findings are instructive for both BEV supply chain stakeholders and policy makers.

Macro Study of Global Electric Vehicle Expansion

This study analyzes the diffusion of battery electric vehicles (BEV) in the world and evaluates the vehicle charging stations based on the European Union (EU) scenario. Initially, the global BEV sales data from 2005 to 2018 are fitted with the two most frequently used econometric logistics and Bass diffusion models. Further, the study identifies the different stage adopters, forecasts the consumption of BEVs, and examines the velocity and acceleration of BEV diffusion. Finally, future charging stations are examined to meet the BEV sales demand. Results suggest that the adoption of BEVs demonstrates a better fit on the Bass model where the global BEV market is estimated to grow from 5,3 millions in 2019 to near 40 millions units by 2030, and with the reference of the EU countries’ adoption scenario, the global charging stations will be increased from near 2 millions in 2019 to near 10 millions units by 2030.

A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies

Due to the ever-increasing harmful emissions affecting natural life and health seriously, it is inevitable the usage of renewable energy sources instead of fossil resources in the near future. Another drawback of fossil fuels is several threats like environmental pollution and global warming, which are potential risks for future generations. Given that the transportation sector makes a huge contribution to carbon emissions, the importance of battery electric vehicles (BEVs), which are an eco-friendly form of vehicles is obvious. Because the BEV market has been rapidly expanding recently, it has become a significant issue to assess BEV alternatives comprehensively from the customer's point of view. This assessment can be made by addressing the basic features of each BEV. Further, multiple criteria decision making (MCDM) techniques are efficient instruments for the right BEV purchase decision. In this work, therefore, ten BEVs are chosen as alternatives. These vehicles are then ranked using SECA, MARCOS, MAIRCA, COCOSO, ARAS, and COPRAS multi-criteria techniques on the basis of technical specifications, such as acceleration, price, battery, range, and so on. Afterward, results from various MCDM techniques are aggregated by applying the Borda count and Copeland ranking methodologies. “Price”, “permitted load,” and “energy consumption” are determined as the most three significant factors for BEV selection, respectively, whereas Tesla Model S is highlighted as the best choice. Further, the robustness and reliability of the results are performed by applying a sensitivity analysis. The proposed framework can be utilized as a basis for more detailed purchasing decisions.

Determinants of Electric Car Sales in Europe

Abstract This article deals with determining statistically significant factors affecting the sale of battery electric vehicles in different European countries. Typical representative countries were selected on the basis of cluster analysis. The input data for multiple regression models and vector autoregressive models include data for the last decade and thus essentially cover the complete history of the electric car market. Attention is paid not only to the European leader but also to countries with a lower share of electric cars. The results of this study show the existence of a common factor in the countries with different development trends in the battery electric vehicle market. However, differences among individual countries are generally so significant that the identified factors vary from country to country.

Prediction of the “Tipping Point” to Widespread Uptake of Battery Electric Vehicles in China via Nonlinear Curve-Fitting Method

<p>The “Tipping Point” is a term that is widely used today to describe that a time or threshold once being surpassed would result in exponential growth in technology adoption or product sales in a specific industry. China’s BEV industry has grown tremendously in the past 10 years and recently, and China has been leading both BEV sales and manufacturing in the world. Thus, this paper aims to investigate the “Tipping Point” timeframe for Battery Electric Vehicles (BEVs) penetration in China. The major work is conducted in 3 steps. 1. This paper firstly defined the exactitude of “Tipping Point” as a 16% market penetration rate from Roger’s technology adoption model. 2. Then this paper used a simple exponential curve formula using the Levenberg–Marquardt Algorithm (LMA) calculation method to conduct nonlinear curve fitting modeling for various nations and testify the validity of our formula used. 3. Finally, after getting a positive result from these sample countries, this paper continues using this method to predict the 16% “Tipping Point” from several current predictions reports. It concludes with a calculated assumption that this 16% BEV market penetration rate would most likely occur by the end of 2024.</p>

Can China’s BEV market sustain without government subsidies?: An explanation using cues utilization theory

After several years of intensified implementing the subsidy policy for battery electric vehicles (BEVs) intending to boost China’s BEV market and the development of China’s domestic BEV industry, the Chinese government plans to totally abolish the government subsidy policy in 2020, one critical issue faced is that whether the BEV market can sustain without the subsidies. This work aims to answer this question, which firstly started by building a theoretical scheme for understanding the roles of China’s favorable government policies and market forces on motivating consumers to adopt BEVs based on the cues utilization theory. In particular, the commercial BEV demonstration policies and Chinese BEV brand reputation were considered in this scheme. And then by building several indexes to reflect the real effects of the market forces and government policies considered in this scheme, a structural equation model is constructed to explore how BEV sales and BEV battery technology were impacted during 2016/01–2019/12. The final results prove that China’s BEV policies succeeded a lot in providing consumers the extrinsic cues helping them to reduce perceived risks of purchasing BEVs, and thus contributed to boosting China’s BEV sales. Constructing more charging piles exerted the most significant effects, followed by the improving global reputation of Chinese BEV brands and increase in commercial BEV sales. The subsidy policy did not contribute the growth of China’s BEV sales directly, but indirectly through the mediation of improved BEV battery technologies and the magnitude is rather low, indicating that the growth of China’s BEV sales has already been less driven by the government subsidies, from which we can conclude that it is very possible for China’s BEV market to sustain without government subsidies. Policy implications for other governments are provided at the end.

Modeling the Effect of Fast Charge Protocol on the Temperature Rise of a Lithium-Ion Battery during Charge

The lithium-ion battery (LIB) is a preferred power source for battery electric vehicles (BEVs),because LIBs have higher energy density, longer life and higher power density than most other practical batteries. Although there is still a plenty of room to be improved for the cost, life, and safety of the LIB in BEV applications,another important issue to be overcome for the BEV battery is searching for an appropriate fast charge protocol to enable a rapid growth of BEV market. A short charge time, however, results in a rapid temperature rise of an LIB cell during charge. Therefore, it is essential to find an optimized fast charge protocol that can keep the peak temperature of LIB cell below the maximum allowable limit recommended by the LIB manufacturer.In this work, the effect of fast charge protocol on the temperature rise of an LIB cell during charge is analyzed based on the two-dimensional thermal modeling of the LIB cell under various fast charge protocols. The thermal modeling during fast charge is validated by the comparison of the modelling results with the experimental measurements using IR thermal images and thermocouples. The fast charge protocols are evaluated based on the peak temperatures of the LIB cell after charge.