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Abstract Battery electric vehicles (BEVs) have positive effects on the reduction of petroleum dependence and vehicle emissions. However, limited driving range of BEVs contributes to the range anxiety of drivers. Therefore, accurately estimating remaining driving range is a critical issue for BEV manufacturers to help drivers alleviating range anxiety. In this paper, by using the real-world data collected from a BEV operating in Beijing, China, the nonlinear estimation models for remaining driving range under different temperature conditions are established based on the data-driven method. The models consider the State of Charge (SOC), speed and temperature conditions as the impacting factors for remaining driving range. The significant nonlinear relationship between speed and driving distance per SOC is explored and considered in the model. The robust nonlinear regression method is used to determine the parameters of the models. Model verification results confirm the accuracy of the model. Moreover, the models are used to explore the economical driving speeds for the BEV under different temperature conditions. The results indicate that the economical driving speeds have an increasing trend as temperature increases. The economical driving speeds under low, moderate and high temperate conditions equal to 48.97 km/h, 50.89 km/h and 51.37 km/h, respectively. Highlights • The data from Beijing are used to model the remaining driving range of BEVs. • Energy consumption under different speeds and temperature conditions is explored. • Robust nonlinear regression method is adopted to establish the models. • Economical driving speeds are explored by using the models. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are playing a key role in supporting transportation electrification and reducing air pollution and greenhouse gas emissions. The increased number of EVs may also bring about some issues concerning energy system structure optimization and efficiency enhancement. User behavior analysis and simulation is an important method to solve these issues. A stochastic model for describing the usage of vehicle is essential to handle simulation models and behavior models. Therefore, a more comprehensive understanding of EV usage patterns is necessary for the model establishment. The paper focuses on the 2,047,222 charging events and 8,382,032 travel events collected from 26,606 battery electric vehicles operating in Beijing, China, in 2018, based on the open lab of National Big Data Alliance of New Energy Vehicles. With the large-scale data resource rather than limited samples, we provide some robust statistical results and some multi-dimensional comparative analysis in the paper, which can be applied in large-scale deployment environments and large population cities. The results can also provide information for charging infrastructures construction, gird management, vehicle charging scheduling, and so forth in Beijing and even other metropolises with similar situations. • Operational data of 26,606 battery electric vehicles in Beijing are collected. • The usage pattern including charging pattern and travel pattern is investigated. • Probability density function is used to analyze usage pattern characteristics. • The characteristics of vehicles in different application scenarios are compared. [ABSTRACT FROM AUTHOR]

The choice of battery range (all-electric driving range) for battery electric vehicles (BEVs) is an important issue for both BEV adopters and BEV makers. This paper proposes a model to identify the minimum BEV battery range that can satisfy given travel demands, considering the opportunities to charge at existing public charging stations and the uncertainties in charging decision making. We conducted a stated preference survey to study the charging decision making and analyzed the data using the Latent Class model to generate the model coefficients for charging decisions making. The proposed approach can better identify the needed battery range than the often-used simplified charging rules. We applied the model to a case study of Beijing to evaluate the needed battery range for taxis and private vehicles. For taxis, BEVs with 220-mile battery range are able to satisfy the travel demands for about 90% of the drivers. For private vehicles, a 300-mile range is needed to cover the travel demands of 90% of the drivers, while a 100-mile range battery is able to satisfy the need for 80% of the private drivers. Simplified charging rules tend to underestimate the range needs for taxis but overestimate the range needs for private vehicles. Image 1 • Proposed a Battery Range Optimization Model (BROM). • Integrated heterogeneous charging behaviors into battery range optimization. • Evaluated the battery range needs for taxis and private vehicles in Beijing as case studies. • Simple charging rules may lead to overestimated battery needs for taxis. • Simple charging rules may lead to underestimated battery range needs for private vehicles. [ABSTRACT FROM AUTHOR]