Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

Wang, Jiangbo; Liu, Kai; Yamamoto, Toshiyuki

doi:10.3390/en10010129

Cited by 28 publications

(21 citation statements)

References 40 publications

(53 reference statements)

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“…However, in their test drives, the auxiliary power was very low and only contributed 7% on average to the energy consumption. Wang, Liu and Yamamoto [26] studied the use of low-frequency data to estimate EV consumption. The measurement sample time was one minute.…”

Section: State-of-the-artmentioning

confidence: 99%

Energy Uncertainty Analysis of Electric Buses

et al. 2018

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Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

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Section: State-of-the-artmentioning

confidence: 99%

Energy Uncertainty Analysis of Electric Buses

et al. 2018

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“…Vaz et al [34] predicted the driving range based on optimal trip parameters before the trip, enabling the users. Wang, Liu, and Yamamoto [35] proposed an estimation model by considering both the dynamic characteristics from sparse GPS observations and the unique attributes of EVs. Bi et al [36] established the nonlinear estimation models for the remaining driving range under different temperature conditions based on the data-driven method.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

2020

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Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

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“…The metric regarding fuel consumption [33] is not included in this work. A part of running CBs in Dalian are electric vehicles and the actual energy consumption of electric vehicles is influenced by many environmental factors [41] such as interactive effects of ambient temperature and vehicle auxiliary loads [42] and road gradient [43]. The differences in the fuel/energy consumption estimations between internalcombustion engine bus and electric bus make it meaningless to be calculated and compared.…”

Section: Travel Costmentioning

confidence: 99%

Role of Customized Bus Services in the Transportation System: Insight from Actual Performance

Wang

Yamamoto

Liu

2019

Journal of Advanced Transportation

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After attaining great prevalence from the end of 2013, customized bus (CB) transit services have experienced a huge decline in China. The feasibility of this new bus sharing system is thus being questioned. Therefore, it is imperative to investigate the actual role of CB services in the overall transportation system based on successful cases, as the role of the CB service determines its primary service object, system construction, marketing orientation, and even government function. To examine the role of CB services, this study investigates the practical performance, advantages, and spatial and temporal coverage of a successful CB system based on practical subscription data for more than two years. The results illustrate that the CB service is an eclectic choice that can balance service quality and cost between traveling by traditional public transportation (PT) and private cars/taxis. Even though the travel cost increased to a limited extent, the CB service significantly improved the travel experience in terms of the travel time, travel speed, number of stations, and difference arrival time compared to PT services. The multinomial logit model and regression models demonstrate a significant positive relationship between the relative advantage and amount of demand for the CB services. Furthermore, the CB service primarily serves trips generated during the peak traffic hours of the city and supplements traditional PT service in areas with poor coverage levels.

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Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

Cited by 28 publications

References 40 publications

Energy Uncertainty Analysis of Electric Buses

Energy Uncertainty Analysis of Electric Buses

Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

Role of Customized Bus Services in the Transportation System: Insight from Actual Performance

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