scholarly journals Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhenbo Lu ◽  
Qi Zhang ◽  
Yu Yuan ◽  
Weiping Tong
Keyword(s):  
Electric Vehicles ◽  
Travel Demand ◽  
Copula Function ◽  
Battery Electric Vehicles ◽  
Trip Chain ◽  
Behavioral Variables ◽  
Driving Range ◽  
Optimal Driving ◽  
Daily Trip ◽  
Daily Travel

This paper proposes a simulation approach for the optimal driving range of battery electric vehicles (BEVs) by modeling the driving and charging behavior. The driving and charging patterns of BEV users are characterized by reconstructing the daily travel chain based on the practical data collected from Shanghai, China. Meanwhile, interdependent behavioral variables for daily trips and each trip are defined in the daily trip chain. To meet the goal of the fitness of driving range, a stochastic simulation framework is established by the Monte Carlo method. Finally, with consideration of user heterogeneity, the optimal driving range under different charging scenarios is analyzed. The findings include the following. (1) The daily trip chain can be reconstructed through the behavioral variables for daily trips and each trip, and there is a correlation between the variables examined by the copula function. (2) Users with different daily travel demand have a different optimal driving range. When choosing a BEV, users are recommended to consider that the daily vehicle kilometers traveled are less than 34% of the battery driving range. (3) Increasing the charging opportunity and charging power is more beneficial to drivers who are characterized by high daily travel demand. (4) On the premise of meeting travel demand, the beneficial effects of increased fast-charging power will gradually decline.

Using Longitudinal Mobile Phone Data to Understand the Stability of Individual Travel Patterns

2017 ◽  
Vol 2643 (1) ◽  
pp. 166-177 ◽  
Author(s):  
Zhengyu Duan ◽  
Chun Wang ◽  
H. Michael Zhang ◽  
Zengxiang Lei ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Activity Space ◽  
Mobile Phone Data ◽  
Travel Patterns ◽  
Trip Chain ◽  
Demand Models ◽  
The Stability ◽  
Daily Trip ◽  
Daily Travel

Most travel demand models assume that individuals’ daily travel patterns are stable or follow a fixed routine. This hypothesis is being questioned by more and more researchers. In this study, longitudinal mobile phone data were used to study the stability of individual daily travel patterns from three aspects, including activity space, activity points, and daily trip-chain patterns. The activity space was represented by the number of nonhome activity points, the radius of nonhome activity points, and the distance from home. The visitation pattern of activity points was analyzed by entropy and predictability measures. The stability of trip-chain patterns was described by the number of distinct trip chains, the typical trip chain, and the typical trip-chain ratio. Analysis of 21 days of mobile phone data from three communities in Shanghai, China, revealed that individuals’ daily travel patterns showed considerable variation. Although individuals’ visitation patterns to activity points were very regular, the day-to-day variations of individual trip-chain patterns were quite significant. On average, an individual exhibited about eight types of daily trip chains during the 21-day period. The daily travel patterns of residents in the outskirts were more stable than those of residents in the city center. Individuals’ travel patterns on weekdays were more complex than those on weekends. As individuals’ activity spaces increased, the stability of their travel patterns decreased.

Optimal battery capacity for an electric-vehicle-sharing-model in the People’s Republic of China

2016 ◽  
Vol 231 (11) ◽  
pp. 1471-1488 ◽  
Author(s):  
Ning Wang ◽  
Runlin Yan ◽  
Gangzhan Fu
Keyword(s):  
Life Cycle ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Economic Benefits ◽  
Government Subsidy ◽  
Republic Of China ◽  
Battery Electric Vehicles ◽  
Battery Capacity ◽  
Daily Travel Distance ◽  
Daily Travel

A project on electric vehicle sharing has been previously carried out as a demonstration operation in Shanghai, Beijing, Hangzhou and Shenzhen in the People’s Republic of China. The high initial investment caused by the high cost of batteries limits commercialization of an electric-vehicle-sharing model. Therefore, a key problem that the operators must solve is to choose the appropriate battery capacity for shared electric vehicles based on different urban driving cycles. Based on three new energy vehicles (i.e. electric vehicles) for demonstration cities of different scales as represented by Shanghai, Shenzhen and Hefei, a whole-life-cycle evaluation model of economic benefits for shared battery electric vehicles was established in this paper. The optimal battery capacity for different substitution rates was calculated using MATLAB software. Then, the influences that the substitution rate, the urban driving cycle, the average daily travel distance, the service price, the charging price, the battery (cycle) life, the battery pack cost and the government subsidy have on the optimal battery capacity in the life-cycle economic benefit model was explained. Suggestions for the optimal battery capacity are provided for operators in different cities. The results indicate that the purchasing cost, the energy consumption cost and the battery depreciation cost are the three main components of the life-cycle cost, which account for more than 80%. The average daily travel distance and the local government subsidy affect the optimal battery capacity only for certain substitution rates. The life-cycle economic benefits of one shared electric vehicle is found to have the most influence on the service price. This paper suggests that shared battery electric vehicles with different battery sizes of 44.5 kW h, 34.9 kW h and 36.96 kW h are suitable for use in metropolitan cities, in large-sized to medium-sized cities and in medium-sized to small-sized cities respectively, as represented correspondingly by Shanghai, Shenzhen and Hefei.

scholarly journals A Model for the Estimation of the Residual Driving Range of Battery Electric Vehicles Including Battery Ageing, Thermal Effects and Auxiliaries

2021 ◽  
Vol 11 (19) ◽  
pp. 9316
Author(s):  
Gianmatteo Cannavacciuolo ◽  
Claudio Maino ◽  
Daniela Anna Misul ◽  
Ezio Spessa
Keyword(s):  
Thermal Management ◽  
Electric Vehicles ◽  
Thermal Effects ◽  
Urban Areas ◽  
Pollutant Emissions ◽  
Sustainable Mobility ◽  
Road Vehicles ◽  
Model Application ◽  
Battery Electric Vehicles ◽  
Driving Range

Sustainable mobility has recently become a priority of research for on-road vehicles. Shifting towards vehicle electrification is one of the most promising solutions concerning the reduction in pollutant emissions and greenhouse gases, especially for urban areas. Nevertheless, battery electric vehicles might carry substantial limitations compared with other technologies. Specifically, the electric range could be highly affected by the ageing process, non-optimal thermal management of the battery and cabin conditioning. In this paper, a model for the estimation of the residual range of electric vehicles is proposed accounting for the influence of battery state of health, battery pack temperature, power consumption of the main vehicle auxiliaries, and battery pre-heating on the residual driving range. The results of the model application to an L7 battery electric vehicle highlighted that the electric range can be highly affected by several factors related to real-world driving conditions and can consistently differ from nominal values.

Energy recovery for battery electric vehicles

2008 ◽  
Vol 222 (10) ◽  
pp. 1827-1839 ◽  
Author(s):  
M Ye ◽  
Z-F Bai ◽  
B-G Cao
Keyword(s):  
Electric Vehicles ◽  
Robust Stability ◽  
Energy Recovery ◽  
Dynamic Performance ◽  
Efficient Energy ◽  
Battery Electric Vehicles ◽  
Hybrid Controller ◽  
Recovery System ◽  
Driving Range ◽  
The Mathematical Model

An efficient energy recovery system for battery electric vehicles (BEVs) is developed and tested. The principle and characteristics are analysed in detail. Then the mathematical model is derived step by step. Combining the merits and the defects of H2 optimal control and H∞ robust control, the robust hybrid controller is designed to guarantee both the system performance and the robust stability. The comparative simulated and experimental results showed that the dynamic performance and robust stability of the proposed scheme were superior to those of the proportional-integral method; the vehicle recycled more kinetic energy during braking and the driving range was increased.

scholarly journals Real Driving Range in Electric Vehicles: Influence on Fuel Consumption and Carbon Emissions

2021 ◽  
Vol 12 (4) ◽  
pp. 166
Author(s):  
Carlos Armenta-Déu ◽  
Erwan Cattin
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Carbon Emissions ◽  
Combustion Engine ◽  
Polluted Environment ◽  
Characteristic Parameters ◽  
Different Types ◽  
Driving Range ◽  
Daily Trip

This paper is focused on the determination of real driving ranges for electric vehicles (EV’s) and how it influences fuel consumption and carbon emissions. A precise method to evaluate the driving range of an EV can establish the correct reduction in GEI amount, mainly CO and CO2, ejected to the environment. The comparison of the daily driving range between an internal combustion engine (ICE) vehicle and an EV provides a useful tool for determining actual fuel saved during a daily trip and a method to compute carbon emissions saved depending on the type of ICE vehicle. Real driving range has been estimated on the basis of a daily trip consisting of five different segments, acceleration, deceleration, constant speed, ascent and descent, which reproduce the different types of driving. The modelling has been developed for urban routes since they are the most common and the most polluted environment where the use of electric vehicles is applied. The effects of types of driving have been taken into account for the calculation of the driving range by considering three main types of driving: aggressive, normal and moderate. The types of vehicle in terms of shape and size as well as dynamic conditions and the types of roads have also been considered for the determination of the driving range. Specific software has been developed to predict electric vehicle range under real driving conditions as a function of the characteristic parameters of a daily trip.

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