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Energy consumption directly determines the environmental benefits and driving range of electric vehicles. The energy consumptions of vehicles are generally evaluated using standardized driving cycle tests; however, the results from standardized driving cycle tests deviate from the results characterizing real driving and present opportunities for cheating. The evaluation of the real-world driving energy consumptions of electric vehicles is becoming a requirement in proposed vehicle regulations. In this paper, a method for evaluating the energy consumption characteristics of electric vehicles under real-world driving conditions is proposed. A simplified analytical function for estimating the energy consumption of an electric vehicle is derived. Using regression analysis, the effects of driving conditions are decoupled, and the independent energy consumption characteristics are obtained. Simulation and experimental data are used to validate the proposed method. The results show that the independent energy consumption characteristics obtained by the proposed method perfectly represent the energy consumptions of electric vehicles under different driving conditions. Therefore, the proposed method represents a possible alternative mechanism for extending the scope of energy consumption evaluations of electric vehicles, providing a basis for the comprehensive assessment of the environmental benefits of electric vehicles. [ABSTRACT FROM AUTHOR]

To meet increasingly stringent emission legislation, electric vehicles are expected to offer promising sustainable mobility in the future. However, the driving range of battery electric vehicles (BEVs) is limited as compared with hybrid electric vehicles (HEVs). Additionally, the grid power supply in China is highly dependent on coal-based thermal power generation, which leads to high grid-carbon intensity and increased well-to-tank (WTT) emission for BEVs. Therefore, the tradeoff between electric vehicle driving range and environmental impact has become a critical problem in BEV development in China. In this study, a BEV model is built and validated. The energy consumption and well-to-wheel (WTW) CO 2 emission rates of different driving ranges and test cycles are simulated. To determine the impact of driving patterns on BEV energy consumption, the distribution of vehicle energy consumption is analyzed and an analytical model is proposed to generalize the energy consumption of BEVs in standardized driving cycles to real-world driving with only two statistical characteristics: the average and the variance of the speeds. It is found that BEVs have a great advantage in terms of energy saving only at driving cycles with low average speeds and frequent stops. While driving at highway speeds, the energy consumption of BEVs can be very high. With an understanding of driving pattern impact, parameter variation analysis of the BEV WTW CO 2 emission rates for different driving ranges is simulated. Simulation results show that the rolling coefficient and battery energy density have a significant impact on driving range, followed by the drag area. However, grid-carbon intensity is more efficient for reducing WTW CO 2 emissions. Currently, optimization of the rolling coefficient and drag area is the most viable option for increasing the battery range and decreasing the WTW CO 2 emission rate. Finally, to reduce the energy and environmental impact of BEVs in China, short driving ranges (<250 km) and low driving speeds (<80 km/h) are suggested for current BEVs, and optimization of the vehicle design and reduction of grid-carbon intensity are considered to be the most critical issues for the future application of BEVs. [ABSTRACT FROM AUTHOR]

Electric vehicles produce zero tailpipe emissions during operation and have thus been considered a most promising method for providing mobility while reducing the greenhouse gas emissions of the transportation sector in the future. The life-cycle cost of electric vehicles has been widely studied to evaluate their competitiveness compared to conventional vehicles. However, the competitiveness of electric vehicles is highly dependent on government promotion policies, and the effects of non-economic incentive policies are currently difficult to include in life-cycle cost analysis. These non-economic effects are usually measured by the intangible cost. Traffic policies represent typical non-economic incentive policies. In China, electric vehicles are exempted from purchase restrictions (license plate control policy) and driving restrictions; thus, the intangible cost of traffic policies has significant effects on the comparison of electric vehicles and conventional vehicles. In this paper, from the consumers’ perspective, the intangible cost of purchase and driving restrictions is modeled and expressed in monetary terms; then, the impact of these non-economic incentive policies are compared with subsidies and other costs of vehicles. Thus, a more comprehensive comparison between electric and conventional vehicles can be provided. Using three selected typical battery electric vehicles and three correspondingly similarly sized conventional vehicles in China, the private life-cycle costs of battery electric vehicles and conventional vehicles are calculated and compared, a parametric variation analysis is performed, and the effects of economic and non-economic incentive policies in different cities are discussed. The comparison shows that, considering the tangible costs of vehicles, battery electric vehicles are not currently economically competitive compared with conventional vehicles, and both national and local subsidies are necessary for battery electric vehicles to compensate the cost gap between battery electric vehicles and conventional vehicles in the short term. However, considering the intangible costs, the advantages of traffic policies are very prominent in mega-cities but are significantly smaller in second-tier cities. With the increasingly worse traffic and environmental problems China facing, it is suggested that the promotion of electric vehicles in mega-cities be prioritized and that electric vehicle promotion policies based on taxes, subsidies and traffic control be balanced. [ABSTRACT FROM AUTHOR]

With the surge of electric vehicles, accurate estimation of their energy consumption becomes increasingly critical. Data-driven models have been widely used for estimating the energy consumption of electric vehicles; however, their applications often face limitations due to inadequate training data, resulting in over-fitting and poor generalizability. In this paper, a physically rational data augmentation approach is proposed to expand the driving trip dataset. By incorporating physical coherence into the augmentation process, new driving trips are synthesized with rational physical context. The effectiveness of the proposed approach is validated by applying it to three data-driven models for estimating the energy consumption of electric vehicles across different validation scenarios. Compared with two baseline data augmentation approaches, our proposed approach demonstrates superior model training performance with less data synthesized. In the best case, the proposed approach achieved a 34% accuracy improvement over the raw data and an 11% improvement over the best-performing baseline. This proposed approach shows considerable promise in facilitating the effective adoption of advanced machine learning algorithms in industrial applications by significantly reducing the data collection requirements. [Display omitted] • The requirements of small-scale data for BEV data-driven models are discussed. • An approach that synthesizes physically rational BEV trip samples is proposed. • Experiments show that conventional data augmentation may produce invalid samples. • The proposed approach effectively improves the performance of data-driven models. [ABSTRACT FROM AUTHOR]