Electric vehicles’ energy consumption estimation with real driving condition data

Zhang, Rui; Yao, Enjian

doi:10.1016/j.trd.2015.10.010

Cited by 139 publications

(68 citation statements)

References 22 publications

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“…Vehicle simulation models require calibration and validation using real-life tests or roller bench tests, and use detailed speed profiles or drive cycles as input for their estimation. Statistical models rely on the availability of real-world data and vary in the extent to which they can be linked with the physical underlying principles and speed profile [16,[23][24][25]. An important part of any energy estimation model for the prediction of energy consumption in real-world circumstances is thus the prediction of the speed profile driven.…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12-14% of the average trip consumption, of which 7-9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

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Section: Introduction and State-of-the-artmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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“…Pevec et al proposed a data‐driven approach using predictive analytics to decide optimal charging station locations. Former studies have focused on determining the optimal station locations based on vehicles' movement and driving patterns . Khalaf and Wang have studied ways to introduce tariffs that may sway EVs charging patterns towards low grid impact times.…”

Section: Introductionmentioning

confidence: 99%

A realistic framework to a greener supply chain for electric vehicles

2019

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Summary The quantity of electric vehicles in the transport sector has steadily risen over the last 10 years. Most developed countries and China have laid out ambitious plans for electric vehicles penetration. However, there are several challenges that must be addressed on the supply‐chain side of the problem for a successful transition toward an alternative and less environmentally harmful transport system. This study proposes a methodology for the optimal plan and decision making of primary energy sources, electricity generation, electricity distribution to vehicles' charging stations, carbon capture and sequestration, and electric vehicles' charging stations network to satisfy the electricity demand of the overall economy including electric vehicles at a regional/countrywide level under operation and green constraints. The optimization problem was modeled as a mixed integer program in general algebraic modeling system (GAMS). The formulation was employed to propose the upcoming electricity supply chain for electric vehicles in the most populous German state (North Rhine‐Westphalia) in 2025. The optimization show that fossil‐based power still controls the generation in 2025, while carbon capture and sequestration along with higher renewable penetration help meeting the state's greenhouse gases (GHG) emission target. The charging stations network expansion consists of 12 820 charging points mainly alternating current (AC) chargers (22‐kW capacity).

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“…According to previous studies, a variety of factors affect energy consumption, including travel-related factors [4,5], environment-related factors [6][7][8][9], vehicle-related factors [10][11][12][13][14], roadway-related factors [15], traffic-related factors [16][17][18], driver-related factors [3,[19][20][21][22], the health and degradation condition of the battery [23][24][25][26], the efficiency of braking energy recovery [27][28][29][30][31], and the charge and discharge character of the battery [32]. Some previous studies collected the information through driving-cycle experiments in the lab [4,24] and Global Positioning System (GPS) observations in the real world [32][33][34], but some results showed the significant difference between experiments and real-world conditions [35,36], leading to a relative low accuracy and poor practicality of models.…”

Section: Introductionmentioning

confidence: 99%

“…Some previous studies collected the information through driving-cycle experiments in the lab [4,24] and Global Positioning System (GPS) observations in the real world [32][33][34], but some results showed the significant difference between experiments and real-world conditions [35,36], leading to a relative low accuracy and poor practicality of models. Particularly, it is very difficult for the experimental design of predetermined conditions to take some real-world conditions into consideration, including the behaviors of drivers on air-conditioner and heater usage, the influence of driving environment, real-world aerodynamic friction loss, lane changing behaviors, car following behaviors, driving behaviors, etc.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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Improving the estimation accuracy for the energy consumption of electric vehicles (EVs) would greatly contribute to alleviating the range anxiety of drivers and serve as a critical basis for the planning, operation, and management of charging infrastructures. To address the challenges in energy consumption estimation encountered due to sparse Global Positioning System (GPS) observations, an estimation model is proposed that considers both the kinetic characteristics from sparse GPS observations and the unique attributes of EVs: (1) work opposing the rolling resistance; (2) aerodynamic friction losses; (3) energy consumption/generation depending on the grade of the route; (4) auxiliary load consumption; and (5) additional energy losses arising from the unstable power output of the electric motor. Two quantities, the average energy consumption per kilometer and the energy consumption for an entire trip, were focused on and compared for model fitness, parameter, and effectiveness, and the latter showed a higher fitness. Based on sparse GPS observations of 68 EVs in Aichi Prefecture, Japan, the traditional linear regression approach and a multilevel mixed-effects linear regression approach were used for model calibration. The proposed model showed a high accuracy and demonstrated a great potential for application in using sparse GPS observations to predict the energy consumption of EVs.

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Electric vehicles’ energy consumption estimation with real driving condition data

Cited by 139 publications

References 22 publications

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

A realistic framework to a greener supply chain for electric vehicles

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

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