Construction of Battery Electric Vehicle Driving Cycles Based on Improved Grey Wolf Optimized K-Means Clustering: A Case Study for Qingdao, China

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

PurposeThis study compares the environmental impacts of transitioning from a business-as-usual (BaU) internal combustion engine vehicles (ICEVs) pathway to one adopting battery electric vehicles (BEVs) in Qatar from 2022 to 2050. The analysis is based on geographically representative empirical data, focusing exclusively on the light-duty, personal vehicle sector. The research explores environmental performance trends, uncertainties, and potential implications of transitioning from ICEVs to BEVs within the Qatar National Vision (QNV) 2030 framework.MethodsUtilising the ReCiPe method, this time-dynamic life cycle assessment (LCA) assessed a range of relevant environmental impact categories: global warming potential, particulate matter, human toxicity, acidification and resource depletion. This analysis incorporates different light-duty vehicle (LDV) types such as sedans, sport utility vehicle (SUVs) and sport vehicles. The impacts of potential technological advancements, such as in fuel efficiency for ICEVs and charging electricity supply and/or battery technology for the BEVs, were included to provide a more encompassing view of the environmental implications of both vehicle types.Results and discussionDecreasing environmental impact for ICEVs and BEVs is observed, with BEVs’ greater potential in reducing Qatar’s transport sector’s carbon footprint. Uncertainties emerged as this potential decrease was not seen in all impact categories, nor vehicle technology or timeframe. This stresses the BEV’s transition importance of production location and energy sources. This was observed for the carbon footprint and overarching environmental impact of battery production, exacerbated in regions reliant on fossil fuel electricity. Qatar, endowed with substantial fossil fuel reserves, relies on natural gas for electricity provision; therefore, the potential benefits of introducing BEVs are limited without strong shifts to renewables. Further research in vehicle production, disposal and technological advancements will prove essential, especially in a maturing sector like electric vehicle production and processing.ConclusionsBEVs have the potential to reduce the environmental impacts of Qatar’s transport sector. Yet, the short payback period for newer BEVs is linked with the greenhouse gas intensity of electricity production, emphasising the dual challenge for Qatar with its reliance on fossil fuels. Considering environmental, economic and societal facets, a transition taking into account all facets of sustainability and not purely the introduction of BEVs is imperative in aligning with Qatar’s 2030 sustainable vision.RecommendationsA clear understanding of the socio-economic and environmental aspects of the ICEV-BEV transition is urgently required, emphasising production, disposal and technological innovations. Exploring alternative batteries and recycling methods can offer pathways to mitigate environmental concerns associated with BEVs. Regions like Qatar are underrepresented in the available literature, yet should be part of the research on sustainable transitions to provide insights on the opportunity and co-benefits that arise from the development of relevant sustainability transition planning.

Generation of a driving cycle for battery electric vehicles：A case study of Beijing

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.

Development and application of life-cycle energy consumption and carbon footprint analysis model for passenger vehicles in China

Developing an Eco-Cooperative Automated Control System (Eco-CAC)

The challenges in the development of plug-in electric vehicle (PEV) powertrains are efficient energy management and optimum energy storage, for which the role of driving cycles that represent driver behaviour is instrumental. Discrepancies between standard driving cycles and real driving behaviour stem from insufficient data collection, inaccurate cycle construction methodology, and variations because of geography. In this study, we tackle the first issue by using the collected data from real-world driving of a fleet of 76 cars for more than one year in the city of Winnipeg (Canada), representing more than 44 million data points. The second issue is addressed by a proposed novel stochastic driving cycle construction method. The third issue limits the results to mainly Winnipeg and cities that have similar features, but the methodology can be used anywhere. The methodology develops the driving cycle using snippets extracted from recorded time-stamped speed of the vehicles from the collected database. The proposed Winnipeg Driving Cycle (WPG01) characteristics are compared to eight existing standard driving cycles and are more able to represent aggressive driving, which is critical in PEV design. An attempt is made to isolate how many differences could be attributed to the sample size and the methodology. The proposed construction methodology is flexible to be optimized for any selection of driving parameters and thus can be a recommended approach to develop driving cycles for any drive train topology, including internal combustion engine vehicles, hybrid vehicles, plug-in hybrid, and battery electric vehicles. Characterization of vehicle parking durations and types of parking (home, work, shopping), critical for duty cycles for PEV powertrains, are reported elsewhere. Here, the focus is on the mathematical approach to develop a drive cycle when a large database with high resolution of driving data is available.

The impact of human behavior on vehicle efficiency has been vastly explored for internal combustion engine (ICE) vehicles. However, human behavioral impacts on vehicle efficiency have not yet transitioned to include battery electric vehicles (BEVs). Understanding the impact of human behavior that achieves BEV efficiency is essential globally, as BEVs begin to retain a significant portion of the automotive market share. BEV sales trends in the US have seen consistent growth since 2010, amounting to over 200,000 units sold by 2015. Globally, the total amount of BEVs and plug-in hybrid electric vehicles (PHEVs) is expected to be 40-70 million by 2025. In light of the growth estimates, defining behavior that induces efficient energy consumption when driving BEVs is essential as these vehicles have a traveling distance constrained to 60-120 miles and can require 1-8 hours to attain a fully charged battery at commercial charging stations. With firm traveling distances and long charging times, defining human behavioral impacts on BEV efficiency will allow drivers to get the most range out of their vehicle. In order to develop categories of BEV drivers in terms of efficiency, an empirical experiment was conducted to determine if clustering drivers on their energy consumption profiles invokes significant categories. The driving attributes that defined the clusters were extracted to compare whether or not efficient BEV driving is similar to eco-driving in ICE vehicles. Furthermore, BEV drivers can suffer from anxiety that stems from limited traveling distance, a phenomenon known as range anxiety. However, there exist other sources of anxiety-related human driving behavior, three of which can be measured using the driving behavior survey (DBS). The three anxiety measures from the DBS were contrasted against the BEV efficiency clusters found from this research, to determine if the anxiety factors defined by the DBS were responsible for efficient BEV driving. The results from this research found two significantly different clusters of BEV driving efficiency, which were defined as efficient and inefficient BEV driving. In comparison to eco-driving in ICE vehicles, both aggressive speed and acceleration were found to be contributing factors to BEV efficiency. The results from the DBS proved that anxiety was not a contributing factor to BEV efficiency, as both clusters had similar answers. The information accumulated through this research can be used to guide new BEV drivers to adopt sustainable driving behaviors, which can help maximize their traveling distance on a single charge. Behavioral contributions to

SummaryEnergy management strategies (EMSs) play an important role in battery electric vehicles (BEVs). However, the efficiency of an EMS is significantly affected by the driving cycle (DC). On the one hand, because of the differences in driving type, torque characteristics, and speed response of BEVs differ from those of internal combustion‐powered vehicles. Meanwhile, on the other hand, typical DCs that are widely used as evaluation indexes cannot reflect changes in the road slope and the driving characteristics of BEVs in a specific city. To solve this problem, a novel EMS based on combined DC prediction (CDCP) is proposed, and three efforts are made. First, a large volume of driving data is collected based on a BEV. The DC for a specific city is constructed using the principal component analysis and K‐means cluster algorithm combined with 15 characteristic parameters of speed, acceleration, slope, and running state rate. Second, CDCP is adopted to overcome the disadvantage that the speed cannot be predicted initially using standard rolling prediction. The state transmission matrix based on the constructed DC is employed to predict the speed when the vehicle starts to run. Third, a fuzzy EMS that considers the CDCP is proposed in order to adapt to the real‐time changes of DCs. Compared with the other predictions, the simulation results show that the proposed EMS has a longer driving range and lower energy consumption rate.

<div class="section abstract"><div class="htmlview paragraph">The previously developed power-based fuel consumption theory for Internal Combustion Engine Vehicles (ICEV) is extended to Battery Electric Vehicles (BEV). The main difference between the BEV model structure and the ICEV is the bi-directional character of traction motors and batteries. A traction motor model was developed as a bi-linear function of positive and negative traction power. Another difference is that the accessories and cabin heating are powered directly from the battery, and not from the powertrain. The resulting unified model for ICEV and BEV energy consumption has linear terms proportional to positive and negative traction power, accessory power, and overhead, in varying proportions. Compared to the ICEV, the BEV powertrain has a high marginal efficiency and low overhead. As a result, BEV energy consumption data under a wide range of driving conditions are mainly proportional to net traction power, with only a small offset. Powertrain ‘bottom-up’ data and vehicle ‘top-down’ data are well described by the model. The observed powertrain transfer function can be used to estimate the energy consumption changes when it is used in different vehicle variants or under different driving conditions. Analytically derived range estimates are then possible for variants of a base BEV, just as analytically derived fuel economy can be estimated for ICEV variants.</div></div>

Battery electric vehicles (BEVs) are now clearly a promising candidate in addressing the environmental problems associated with conventional internal combustion engine vehicles (ICEVs). However, BEVs, unlike ICEVs, are still not widely accepted in the automobile market but continuing technological change could overcome this barrier. The aim of this study is to assess and forecast whether and when design changes and technological improvements related to major challenges in driving range and battery cost will make the user value of BEVs greater than the user value of ICEVs. Specifically, we estimate the relative user value of BEVs and ICEVs resulting after design modifications to achieve different driving ranges by considering the engineering trade-offs based on a vehicle simulation. Then, we analyze when the relative user value of BEVs is expected to exceed ICEVs as the energy density and cost of batteries improve because of ongoing technological change. Our analysis demonstrates that the relative value of BEVs is lower than that of ICEVs because BEVs have high battery cost and high cost of time spent recharging despite high torque, high fuel efficiency, and low fuel cost. Moreover, we found the relative value differences between BEVs and ICEVs are found to be less in high performance large cars than in low performance compact cars because BEVs can achieve high acceleration performance more easily than ICEVs. In addition, this study predicts that in approximately 2050, high performance large BEVs could have higher relative value than high performance large ICEVs because of technological improvements in batteries; however low performance compact BEVs are still very likely to have significantly lower user value than comparable ICEVs until well beyond 2050.

Life Cycle Assessment (LCA) of BEV’s environmental benefits for meeting the challenge of ICExit (Internal Combustion Engine Exit)

A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

Battery Electric Vehicles (BEVs) are considered to have higher energy efficiency and advantages to better control CO2 emissions compared to Internal Combustion Engine Vehicles (ICEVs). However, in the context that a large amount of thermal power is still used in developing countries, the CO2 emission reduction effectiveness of BEVs can be weakened or even counterproductive. To reveal the impact of the electricity generation mix on carbon emissions from vehicles, this paper compares the life cycle carbon emissions of BEVs with ICEVs considering the regional disparity of electricity generation mix in China. According to Life Cycle Assessment (LCA) analysis and regional electricity carbon intensity, this study demonstrates that BEVs in the region with high penetration of thermal power produce more CO2 emissions, while BEVs in the region with higher penetration of renewable energy have better environmental performance in carbon emission reduction. For instance, in the region with over 50% penetration of renewable energy, a BEV can reduce more CO2 (18.32 t) compared to an ICEV. Therefore, the regions with high carbon emissions from vehicles need to increase the proportion of renewable generation as a priority rather than promoting BEVs.

Battery electric vehicle (BEV) sales have significantly increased in recent years. They have different energy consumption patterns compared to the fuel consumption patterns of internal combustion engine vehicles (ICEVs). This study quantified the impact of intersection control approaches—roundabout, traffic signal, and two-way stop controls—on BEVs’ energy consumption. The paper systematically investigates BEVs’ energy consumption patterns compared to the fuel consumption of ICEVs. The results indicate that BEVs’ energy consumption patterns are significantly different than ICEVs’ patterns. For example, for BEVs approaching a high-speed intersection, the roundabout was found to be the most energy-efficient intersection control, while the two-way stop sign was the least efficient. In contrast, for ICEVs, the two-way stop sign was the most fuel-efficient control, while the roundabout was the least efficient. Findings also indicate that the energy saving of traffic signal coordination was less significant for BEVs compared to the fuel consumption of ICEVs since more regenerative energy is produced when partial or poorly coordinated signal plans are implemented. The study confirms that BEV regenerative energy is a major factor in energy efficiency, and that BEVs recover different amounts of energy in different urban driving environments. The study suggests that new transportation facilities and control strategies should be designed to enhance BEVs’ energy efficiency, particularly in zero emission zones.