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The electric vehicles (EVs) are drawing much attention worldwide due to low fuel consumption and local pollution. However, the range of EVs is still not parallel to that of internal combustion engine vehicles. Moreover, the EV's range suffers a significant decrease in cold and hot climate due to the climate control load of the air conditioning system. These two combined making the range extension strategies a hot spot in EV research. In this paper, the review on EV range extension strategies is provided. First, the climate control load reduction strategies are reviewed. The characters of heating load in cold climate and the reduction methods are introduced. Second, the review on heat pump heating is conducted based on the different structures of heat pump systems. The combination of load reduction and heat pump heating seems to be a promising way in solving the range reduction problems. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the 'Electric Vehicles–Power Grid–Traffic Network' fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners' charging behavior. [ABSTRACT FROM AUTHOR]

• Air conditioning load reduction strategy in vehicles using recirculated air is provided. • Utilizing the recirculated air can extent the annual driving range by 11−30%. • Energy saving of heat pump heating in electric vehicle air conditioning is provided. The stock of electric vehicle is booming recently. However, the range of the electric vehicle is still a main barrier in its adoption. Air conditioning system is the second largest energy consumption device on vehicle, which will cause significant range reduction during operation. In this paper, the energy-saving effect of utilizing the recirculated air in air conditioning system is evaluated. The outside air ventilation rate based on CO 2 limits and windshield anti-fog requirements are calculated, and the energy-saving effect of this strategy in 30 cities across China is evaluated using an annual energy consumption model. It is found that utilizing the recirculated air can save 14–46% heating energy when using PTC heater, and 33–57% heating energy in heat pump AC system. Throughout a year, utilizing the recirculated air can extend the driving range by 11–30%. Image, graphical abstract [ABSTRACT FROM AUTHOR]

With the popularization and promotion of electric vehicles (EVs), their interactions with power grids and traffic networks have increasingly deepened. Accurate modelling of EV behaviour can faithfully depict the characteristics of EV driving and charging. However, most existing modelling researches fail to adopt real-world travel data and consider realistic perceptual decision-making psychology of owners. Thus, this paper proposes a novel behavioural modelling for EVs based on a data-driven approach combined with behavioural economics theory. To characterize the driving behaviour of owners using actual data, a systematic data mining and modelling approach is firstly proposed based on the open-source 'Didi' traffic travel data set, which obtains the traffic operation rules and the regenerative behaviour characteristics data. According to the subjective perceptual characteristics of social economic man, a Cumulative Prospect Theory-based modelling framework is further developed to quantify the uncertain and stochastic charging decision-making behaviour of EV users. Moreover, the user's preferences and attitudes are evaluated by calculating their cumulative prospect value when choosing charging stations. Finally, the most suitable charging station is recommended for EVs with charging requirements. Case studies are conducted within a practical zone in Nanjing, China. The results demonstrate that the traffic travel rules of vehicle owners have typical date types and functional area distribution characteristics. And the travel time and space of private and commercial vehicles are relatively regular, whereas the travel rules of public vehicles are random. Besides, this proposed methodology can not only effectively capture the irrational decision-making characteristics of EV users' charging behaviour, but also achieve promising performance in terms of reducing the charging waiting cost. The user's decision-making regarding charging behaviour exhibits a higher risk-seeking preference than a risk-aversion preference. Proposed a comprehensive and systematic analysis methodology of traffic travel data mining and modelling Depicted the driving behaviour portrait of urban vehicle owners based on the open-source 'Didi' traffic travel dataset Developed a Cumulative Prospect Theory-based modelling framework to quantify the stochastic uncertain charging decision-making behaviour of Electric Vehicle users. Considered the subjective perceptual characteristics of social economic man, the irrational behaviour modelling approach more accurately perceive Electric Vehicle charging energy demand. [ABSTRACT FROM AUTHOR]