Development Of Battery Electric Vehicles Research Articles

Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

Abstract Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

Aging Aware Battery Operation and State of Health Evaluation in Energy Storage Systems

The sources of performance loss in lithium-ion batteries are many and can vary widely dependent on battery usage [1]. Understanding these mechanisms and coupling usage patterns to dominating degradation phenomena can allow smarter use and longer lifetime of the batteries. This can especially bring value in stationary applications, as the usage patterns have different characteristics and second life applications for batteries are considered as well [2].Driven by the development of battery electric vehicles, batteries with higher power and energy densities have revolutionized the market. The lifetime of the batteries is another crucial criterion. A significant amount of work has been performed studying the degradation phenomena and evaluating stress factors. High/low potentials, high C-rates, and temperature deviations from room temperature should be avoided to keep the degradation rate low [3-5]. The power demand for batteries in stationary energy storage systems is lower than vehicle applications and some of these extremes could therefore be avoided [6]. Finding optimal sizing of the batteries, with high energy throughput and low degradation rate, is therefore of interest. The energy delivered by the storage system is further part of the power trading market, hence motivating the need to accurately correlate delivered energy and battery performance loss from different services.In this work the degradation of commercial 18650-type Nickel Cobalt Manganese (NCM)/Graphite cells, in stationary applications is studied. Different scenarios including type of service, sizing, and second-life applications are studied through accelerated testing. The work expands upon a previous study focusing on the degradation effects of combining services [7]. The degradation analysis involves different techniques such as the capacity evolution (Figure 1), electrochemical impedance spectroscopy (EIS), as well as changes in the electrodes revealed by differential voltage analysis (DVA). The electrode balancing, capacity and cell polarization is evaluated in terms of battery state-of-health and important characteristics highlighted. Preliminary results show no additional degradation introduced by scaling the current amplitude 50%. A clear relationship between state-of-charge window and degradation rate is shown, where the graphite two-phase regions are of importance. These regions are also affected by the electrode slippage observed in the DVA. Strategies for battery characterization and operation strategies for second-life applications will be further investigated.Figure 1. Preliminary results of the normalized capacity development of the cells in the study, as an averaged value of duplicate cells. Frequency regulation (FR) is a mild cycle around 50% SOC, current maximum 1C. FR_high has a current maximum of 1.5C. Peak shaving (PS) is a 1C constant current cycling between 22-78% SOC, PS_low has a 0.5C constant current cycling. FRPS is the two cycles combined.References Birkl, C. R.; Roberts, M. R.; McTurk, E.; Bruce, P. G.; Howey, D. A., Degradation diagnostics for lithium ion cells. Journal of Power Sources 2017, 341, 373-386. Martinez-Laserna, E.; Gandiaga, I.; Sarasketa-Zabala, E.; Badeda, J.; Stroe, D. I.; Swierczynski, M.; Goikoetxea, A., Battery second life: Hype, hope or reality? A critical review of the state of the art. Renewable and Sustainable Energy Reviews 2018, 93, 701-718. Baure, G.; Dubarry, M., Battery durability and reliability under electric utility grid operations: 20-year forecast under different grid applications. Journal of Energy Storage 2020, 29. Keil, P.; Schuster, S. F.; Wilhelm, J.; Travi, J.; Hauser, A.; Karl, R. C.; Jossen, A., Calendar Aging of Lithium-Ion Batteries. Journal of The Electrochemical Society 2016, 163 (9), A1872-A1880. Ecker, M.; Nieto, N.; Käbitz, S.; Schmalstieg, J.; Blanke, H.; Warnecke, A.; Sauer, D. U., Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries. Journal of Power Sources 2014, 248, 839-851. Dubarry, M.; Devie, A.; Stein, K.; Tun, M.; Matsuura, M.; Rocheleau, R., Battery Energy Storage System battery durability and reliability under electric utility grid operations: Analysis of 3 years of real usage. Journal of Power Sources 2017, 338, 65-73. Ohrelius, M.; Berg, M.; Wreland Lindström, R.; Lindbergh, G., Lifetime Limitations in Multi-Service Battery Energy Storage Systems. Energies 2023, 16 (7). Figure 1

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Demand for battery electric vehicles has grown significantly in recent years as global oil prices have climbed. The current development of battery electric vehicles is still in its infancy, and its energy consumption also requires great attention. Five same model battery electric vehicles' driving condition data of a certain brand in Tianjin area are collected for a whole year, and their energy consumption is analyzed. The results show that the energy consumption of the actual driving conditions in the four seasons is generally higher than the test results under New European Driving Cycle, and only some vehicles' energy consumption in spring and autumn is consistent with the test results under Worldwide Harmonized Light Vehicles Test Cycle. Energy consumption tends to increase at both ends with the change of temperature. The impact of low temperature is greater, because the air conditioner usage rate and power in Winter are higher than those in Summer. Through principal component contribution rate analysis and K-means clustering calculation of micro-trips, the results show that the average energy consumption in urban, suburban and high-speed driving conditions decreases gradually. Driving habits such as rapid acceleration ratio and rapid deceleration ratio also have an impact on energy consumption. The decrease of energy braking recovery indirectly caused by rapid deceleration ratio increase makes the energy consumption per 100 km increase. And the increase of rapid acceleration ratio leads to an increase in energy consumption per 100 km. Finally, the Spearman rank correlation analysis shows that energy consumption has the greatest correlation with the average vehicle speed and air conditioner usage status, followed by the constant speed proportion, idle speed proportion and temperature, and finally the recovery of braking energy per 100 km, acceleration proportion, deceleration proportion and rapid acceleration proportion.

A constrained DRL-based bi-level coordinated method for large-scale EVs charging

With the vigorous development of battery electric vehicles (BEVs), BEVs’ charging scheduling is essential for better economy and safety. In this paper, we aim to minimize the electricity purchasing cost considering a large number of BEVs and distributed energy. This problem is challenging to get the optimal charging policy due to a large number of uncertainties and dimension disasters caused by a large scale of BEVs and renewable energy. To meet these challenges, we propose an improved bi-level schedule framework, which decomposes the primal problem into two sub-problems to reduce the computational complexity and designs a communication mechanism to ensure the consistency of optimality between different levels. Then the problem is modeled as constrained multi-level Markov decision processes (CMMDP). In the upper level, a constrained deep reinforcement learning method (CDRL) is proposed to get the total charging or discharging energy of BEV groups. An action constraint module is constructed to ensure the feasibility of chosen actions and a novel reward shaping function is designed to optimize action selection. In the lower level, an optimal descending order charging policy (DOCP) is taken to fast decide the charging or discharging behavior for each BEV based on the upper level’s decision. Numerical experiments show that our method has obvious superiority in training efficiency and solution accuracy compared with state of art DRL methods, and reduces the cost by 12% to 28% compared with an experience charging policy.

Electric Vehicle Modelling for Future Technology and Market Penetration Analysis

The transportation sector is generally thought to be contributing up to 25% of all greenhouse gases (GHG) emissions globally. Hence, reducing the usage of fossil fuels by the introduction of electrified powertrain technologies such as hybrid electric vehicle (HEV), battery electric vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV) is perceived as a way towards a more sustainable future. With a seemingly more significant shift towards BEV development and roll-out, the research and development of BEV technologies has taken on increasing importance in improving BEV performance and ensuring its competitiveness. Numerical simulation, using MATLAB, is performed as a tool to investigate and to improve the overall performance of BEVs. This study provides an overview of the possible technology outcome and market consequences for future compact BEVs along with HEVs, FCEVs and internal combustion engine vehicles (ICEV). The techno-economics of BEVs, market projection and cost analysis up to 2050 are investigated, as are important BEV characteristics alongside those of other types of vehicles. Well-to-wheel analysis of BEVs is also studied and compared with HEV, FCEV and ICE.

National Culture and the Market Development of Battery Electric Vehicles in 21 Countries

Electric vehicles (EVs) have become a symbol of the fight against climate change. However, in many countries, the market development of EVs is sluggish, and adoption rates greatly vary worldwide. This study examines the impact of Hofstede’s six dimensions of national culture on the development of the electric car market using sales data from 2019 and 2020 in 21 OECD countries. Results of (OLS) linear regression analyses show that uncertainty avoidance, individualism, masculinity, and indulgence have a significantly negative impact on the sales of battery electric cars, while long-term orientation positively influences their market share. The results indicate that national culture is an important cross-country factor that influences the rate and direction of the adoption electric cars. The research also explains how cultural values are translated into the purchase of EVs and provides marketers and policymakers with an insight into how they can increase the spread of alternative-fuel vehicles.

Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm

In recent years, electric ride-hailing has increased considerably in the taxi industry with the development of battery electric vehicles (BEVs) and implementation of greenhouse gas (GHG) emission regulations. Private BEVs are charged mostly in private garages, while electric ride-hailing services require public charging stations (CSs). This work uses an improved genetic algorithm (GA) to locate public CSs by considering the investment of CS operators and the travel costs of BEV owners. A case study is presented with large-scale order data collected from the ride-hailing fleet of the city of Haikou and charging data from the electric ride-hailing fleet of the city of Shanghai. The elastic demand for electric ride-hailing is also considered by incorporating feedback between congestion at the CS and the geographical area. The proposed methodology uses the multipopulation genetic algorithm (MPGA) to provide more feasible allocations for public CSs and could reduce the total cost by 7.6%.

Can Electric Vehicles Meet Highway-Trip Requirements?: Exploration of the Real-World Impact on Highway Driving Range Derating

Battery electric vehicles (BEVs) have limited driving ranges compared to conventional vehicles (CVs). Therefore, whether BEVs meet highway-trip requirements poses a critical concern for consumers. The real-world driving range is significantly different from laboratory test results due to a lack of real-world factors. Here, a hybrid physical-stochastic model is proposed to assess the real-world performance of BEVs in highway trips by comprehensively considering the technical and social factors of vehicle performance, driver behavior, and trip plan. The results provide much improved realworld insight for both technical and nontechnical professionals concerning the development of BEVs.

An empirical study on the relationship among perceived value, satisfaction and behavioral intentions of battery electric vehicle user in China

With the rapid development of battery electric vehicle, it has a profound impact on the driving behavior and habits of users. However, studies on the evaluation and users’ behavioral intentions of battery electric vehicle are rare. To promote the further development of battery electric vehicle, a better understanding of battery electric vehicle users is needed. This paper aims to develop a model to investigate the relationship among perceived value, user satisfaction and behavioral intentions of battery electric vehicle. Specifically, we divide perceived value into two dimensions of functional value and symbolic value. Data was collected through questionnaires from battery electric vehicle users in major cities in China. The analysis results of the structural equation model indicate that: 1. the functional value and symbolic value have a direct positive impact on battery electric vehicle user satisfaction and the symbolic value palys a greater role; 2. the functional value and symbolic value have a direct positive effect on the behavioral intentions of battery electric vehicle users; 3 battery electric vehicle user satisfaction and behavioral intentions have a high positive correlation.

Comprehensive Evaluation of the Sustainable Development of Battery Electric Vehicles in China

Due to the rapid growth in the total number of vehicles in China, energy consumption and environmental pollution are serious problems. The development of electric vehicles (EVs) has become one of the important measures for solving these problems. As EVs are in a period of rapid development, sustainability research on them is conducive to the timely discovery of—and solution to—problems in the development process, but current research on the sustainability of EVs is still scarce. Based on the strategic development direction of EVs in China, battery electric vehicles (BEVs) were chosen as the research object of this study. The theory and method of the life cycle sustainability assessment (LCSA) were used to study the sustainability of BEVs. Specifically, the indicators of the life cycle assessment (LCA) were constructed, and the GaBi software was used to assess the environmental dimensions. The framework of life cycle costing (LCC) was used to assess the economic dimensions from the perspective of consumers. The indicators of the social life cycle assessment (SLCA) of stakeholders were constructed to assess the social dimension. Then, the method of the technique for order preference by similarity to ideal solution (TOPSIS) was selected for multicriteria decision-making in order to integrate the three dimensions. A specific conclusion was drawn from a comparison of BEVs and internal combustion engine vehicles (ICEVs). The study found that the life cycle sustainability of ICEVs in China was better than that of BEVs. This result might be unexpected, but there were reasons for it. Through sensitivity analysis, it was concluded that the current power structure and energy consumption in the operation phase of BEVs had a higher environmental impact, and the high cost of batteries and the government subsidy policy had a higher impact on the cost of BEVs. Corresponding suggestions are put forward at the end of the article.

The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle

Due to the fact that demand for battery power increasing dramatically with the fast development of battery electric vehicles (BEVs), and poor power density prevents batteries absorbing more braking energy, leads to the so-called range phobia and presents a significant barrier to BEV commercialization. As one of the promising solutions, a supercapacitor-based hybrid energy storage system (HESS) is fast becoming the automotive industry trend, and there are widespread attempts of integrating multi-speed transmission in BEVs to improve electric machine efficiency. As part of the response to the above barriers, an electrified continuously variable transmission (CVT) is proposed along with HESS for BEV in this study. The improvements of passenger BEV in terms of vehicle dynamic performance, energy saving, and battery life extending through applying electrified CVT and HESS will be investigated. The impact of CVT on battery health improvement via HESS will also be analyzed. Specifically, a designed gear ratio varying control strategy is developed for electrified CVT to improve energy efficiency and vehicle performance. A vehicle performance comparison of electrified CVT and traditional single speed BEV is provided. Additionally, the potential benefit of the HESS and CVT combination to driving range, battery life and manufacturing/user cost is reported. Results reveals that the proposed CVT not only provides considerable benefit to BEV dynamic and economic performance, it also shows potential to improve the performance of HESS in terms of extending battery lifetime.

Synergistic Impacts of China’s Subsidy Policy and New Energy Vehicle Credit Regulation on the Technological Development of Battery Electric Vehicles

With the phasing down of subsidies, China has launched the new energy vehicle (NEV) credit regulation to continuously promote the penetration of electric vehicles. The two policies will coexist through 2020 and definitely pose a dramatic impact on the development of the Chinese and even the global electric vehicle market. However, few studies have systematically investigated the relationship between the two policies as well as the synergistic impacts during the overlap period. This paper interprets the rationales of China’s subsidy policy and NEV credit regulation and establishes a bottom-up model to estimate the synergistic impacts of the two policies on the technological trends of battery electric vehicles (BEVs) from the perspective of credit cost-effectiveness. The results suggest that the subsidy policy still maintains strong support for the development of electric vehicles in China. For small BEVs whose driving ranges are higher than 300 km, subsidies even account for 40–50% of the manufacturing cost. In addition, we conclude that the two policies will complement each other in the transitional period and small BEVs are preferred by both policies. Under the NEV credit regulation, 350 km will consistently be the optimal driving range, which will definitely limit the development of other ranges. With the addition of the subsidy, the limitation will be amended in the short run. However, the effect of the subsidy is decreasing and is going to be canceled after 2020, so the focus should be on the optimization of the NEV credit regulation.

The impact of electric vehicles on the outlook of future energy system

Active promotion of electric vehicles (EVs) and technology of fast EV charging in the medium term may cause significant peak loads on the energy system, what necessitates making strategic decisions related to the development of generating capacities, distribution networks with EV charging infrastructure, and priorities in the development of battery electric vehicles and vehicles with electrochemical generators. The paper analyses one of the most significant aspects of joint development of electric transport system and energy system in the conditions of substantial growth of energy consumption by EVs. The assessments of per-unit-costs of operation and depreciation of EV power unit were made, taking into consideration the expenses of electric power supply. The calculations show that the choice of electricity buffering method for EV fast charging depends on the character of electricity infrastructure in the region where the electric transport is operating. In the conditions of high density of electricity network and a large number of EVs, the stationary storage facilities or the technology of distributed energy storage in EV batteries – vehicle-to-grid (V2G) technology may be used for buffering. In the conditions of low density and low capacity of electricity networks, the most economical solution could be usage of EVs with traction power units based on the combination of air–aluminum electrochemical generator and a buffer battery of small capacity.

China’s Participation is an Upturning for the Battery Electric Vehicles’ Development

Development and use of battery electric vehicles and light trucks will yield remarkable benefits environmentally and economically to nations by reducing reliance on importing petroleum, urban air pollution, and greenhouse gas emissions. Battery electric vehicles industry could revolutionize energy and transportation markets, increase their worth by more than $1 trillion US dollars annually in the next 20-25 years. Battery electric vehicles strategy is vital to every long-sighted country in the long run, both in sustainable development and industrial restructuring. Meanwhile, given the superiority in advanced material science, Internet technology, and strong governmental executive ability, China’s participation is an upturning for the development of battery electric vehicles.

Comparison of the Short-Term Forecasting Accuracy on Battery Electric Vehicle between Modified Bass and Lotka-Volterra Model: A Case Study of China

The potential demand of battery electric vehicle (BEV) is the base of the decision-making to the government policy formulation, enterprise manufacture capacity expansion, and charging infrastructure construction. How to predict the future amount of BEV accurately is very important to the development of BEV both in practice and in theory. The present paper tries to compare the short-term accuracy of a proposed modified Bass model and Lotka-Volterra (LV) model, by taking China’s BEV development as the case study. Using the statistics data of China’s BEV amount of 21 months from Jan 2015 to Sep 2016, we compare the simulation accuracy based on the value of mean absolute percentage error (MAPE) and discuss the forecasting capacity of the two models according to China’s government expectation. According to the MAPE value, the two models have good prediction accuracy, but the Bass model is more accurate than LV model. Bass model has only one dimension and focuses on the diffusion trend, while LV model has two dimensions and mainly describes the relationship and competing process between the two populations. In future research, the forecasting advantages of Bass model and LV model should be combined to get more accurate predicting effect.

Experimental Electric Vehicle eŠus Gen2

Abstract In the introduction to the article, the conception and development of an experimental electric vehicle is described. It is followed by a description of the used mechanical and electrical components in combination with the design solutions of sub-units, such as the vehicle powertrain and traction battery. The choice of components and design solutions is evaluated here with regard to the current trends in the development of battery electric vehicles.

Coupling local renewable energy production with electric vehicle charging: a survey of the French case

The share of renewable energy sources (RES) production in energy mixes, especially the ones of PV panels and wind farms, has been continuously increasing during the last few years. Similarly, a strong development of battery electric vehicles (EV) is expected within the next years. However, these two new innovations could trigger local security issues on electrical grids. One way to mitigate these problems could be to combine the charging periods of the EVs with the local RES production. This paper aims at analysing the possibility to implement this kind of smart charging strategy in France by 2020, taking into account the wide diversity of local energy mixes in France and their seasonal dependencies. The results show the achievable green charging ratio for the EV fleet per season and per region, with and without a smart charging strategy.

Coupling local renewable energy production with electric vehicle charging: a survey of the French case

The share of renewable energy sources (RES)’ production in energy mixes, especially the ones of PV panels and wind farms, has been continuously increasing during the last few years. Similarly, a strong development of battery electric vehicles (EV) is expected within the next years. However, these two new innovations could trigger security issues on electrical grids, in particular local congestions and voltage deviations from the required standards. One way to mitigate these problems could be to combine the charging periods of the EVs with the local RES production. This paper aims at analysing the possibility to implement this kind of smart charging strategy in France by 2020, taking into account the wide diversity of local energy mixes in France and their seasonal dependencies. First, the authors identify four different energy mixes representative of most of the local French mixes, each one related to a region in France. Then, we model the charging load curves of the EVs at the substation level for the same regions. We deduce the “green charging ratio” (GCR) without any charging management strategy. The same ratio is then computed assuming that an Energy Management System (EMS), which goal is to maximize this ratio, controls the EV charging patterns. The results are provided for each region and seasons. They show a wide diversity of green charging ratio achievable: in very windy regions, the GCR without any EMS is already rather high; on the contrary, in sunny regions, the GCR is much improved with the implementation of the EMS. In such regions, the GCR can also be enhanced with the penetration of charging stations at working places.

Review of battery electric vehicle propulsion systems incorporating flywheel energy storage

The development of battery electric vehicles (BEV) must continue since this can lead us towards a zero emission transport system. There has been an advent of the production BEVs in recent years; however their low range and high cost still remain the two important drawbacks. The battery is the element which strongly affects the cost and range of the BEV. The batteries offer either high specific power or high specific energy but not both. To provide the BEVs with the characteristic to compete with conventional vehicles it is beneficial to hybridize the energy storage combining a high energy battery with a high power source. This shields the battery from peak currents and improves its capacity and life. There are various devices which could qualify as a secondary storage system for the BEV such as high power battery, supercapacitor and high speed flywheel (FW). This paper aims to review a specific type of hybridisation of energy storage which combines batteries and high speed flywheels. The flywheel has been used as a secondary energy system in BEVs from the early 1970s when the oil crises triggered an interest in BEVs. Since the last decade the interest in flywheels has strengthened and their application in the kinetic energy recovery system (KERS) in Formula 1 has further bolstered the case for flywheels. With a number of automotive manufacturers getting involved in developing flywheels for road applications, the authors believe commercial flywheel based powertrains are likely to be seen in the near future. It is hence timely to produce a review of research and development in the area of flywheel assisted BEVs.

Simulation based study of battery electric vehicle performance in real world cycles

The development of battery electric vehicles (BEV) must continue since this offers the leading route towards zero emission transport. However the limited range and high cost of the BEV remain important issues to be addressed. The battery is the element which strongly affects its cost and range. The batteries offer either high specific power or high specific energy, but not both. This paper presents the modelling of a BEV which is used to study the potential for improvement in its energy efficiency. The battery model types have been discussed. The vehicle and other component models have been described. The choice of model parameters and the control strategy has been explained. The simulations have been performed on homologation and real world cycles for different scenarios. Results show significant potential for improvement in the energy efficiency of the BEV in real world usage by the utilisation of a secondary energy storage device.