Electric vehicles (EVs) are widely regarded as a promising solution to reduce air pollution in cities and key to a low carbon mobility future. However, their environmental benefits depend on the temporal and spatial context of actual usage (journey energy efficiency) and the rolling out of EVs is complicated by issues such as limited range. This paper explores how the energy efficiency of EVs is affected and shaped by driving behavior, personal driving styles, traffic conditions, and infrastructure design in the real world. Tests have been conducted with a Nissan LEAF under a typical driving cycle on the Beijing road network in order to improve understanding of variations in energy efficiency among drivers under different urban traffic conditions. Energy consumption and operation parameters were recorded in both peak and off-peak hours for a total of 13 drivers. The analysis reported in this paper shows that there are clear patterns in energy consumption along a route that are in part related to differences in infrastructure design, traffic conditions, and personal driving styles. The proposed method for analyzing time series data about energy consumption along routes can be used for research with larger fleets of EVs in the future.
Among many innovative technologies to decarbonize urban transport, electrification of the vehicle fleet has been viewed by many as an effective way to reduce carbon emissions, energy consumption, air pollution, and oil dependence [
However, the energy consumption and air pollution during the generation of the electricity used to power EVs cannot be neglected. Although the substitution of EVs for internal combustion engine powered vehicles (ICEVs) can have huge environmental benefits with, for instance, reductions in GHG emissions of 33% in the USA [
Yet, the environmental benefits of EVs depend not only on electricity generation or even the production and afterlife of vehicles and batteries [
There has been research about reducing EV energy consumption in the usage phase that focuses on the vehicle side through, for instance, optimization of the powertrain system, upgrading of motor control strategy, and improvements in battery power density. Stockar et al. [
Driving behavior and driving styles, dispositions to drive in particular ways that have been acquired over time, have been shown to result in fluctuations of vehicle energy efficiency for conventional vehicles, where, for example, aggressive drivers can consume 30% more fuel [
Consequently, there is a gap in the literature regarding the impact of driving behaviors on EVs’ energy consumption in the real-world operation phase. There have been some modeling and simulation of EVs with specific driving cycles such as ECE-15, FUDS, NYCC, and Japanese 10–15 mode cycle [
The remainder of the paper is organized as follows. After a review of relevant literature on EV energy consumption, Section
Research has sought to better understand and estimate the energy consumption of electric vehicles from different perspectives. One strand of research analyzes energy consumption through field tests on dedicated tracks. Cenex (the UK’s center of excellence for low carbon and fuel cell technologies) has a test track composed of different parts to simulate four driving cycles, including a high-speed circuit, city course, hill route, and a handling route with a total length of 11.8 km [
Another study on the same Cenex test site suggested that opportunities for regenerative energy capture were the largest on the high-speed circuit [
Other than field tests, simulation is a feasible method in resource-constrained conditions for analysis. Zhang and Yao [
Meanwhile, some authors have proposed a platform or model for EV energy consumption at a regional scale. Lee and Wu [
Notably, although largely neglected in behavior-oriented EV energy research, there has been a separate strand of studies focused on energy consumption in the vehicle heating, ventilation, and air-conditioning systems (HVAC) of EVs. To assess the geographical and environmental influence on energy consumption as well as the effect of preconditioning, Kambly and Bradley [
In sum, en-route EV energy consumption is a process affected by different factors on multiple levels (Figure
A hierarchical map of influencing factors for EV energy consumption (factors considered in the empirical study printed in italic).
In this part, we first describe the design of the experiment in which energy consumption and other data were collected; then the methods used to analyze energy consumption are introduced.
A battery electric vehicle Nissan LEAF 2011 model was used as the test vehicle. An On-Board Diagnostics (OBD) device data logger was connected to the vehicle Electronic Control Unit (ECU) along the actual driving tests, and the data were later uploaded to a computer for analysis. The OBD provided second-by-second information including vehicle speed, motor torque, motor speed, battery pack current, and voltage. Derivative values such as instant acceleration and energy consumption could be calculated accordingly. A portable GPS was also used during the test to capture the location for each second, and ambient temperature data were recorded using an electric thermometer.
In order to be representative of average daily driving behavior, the selected test route started from the residential area of Haidian District (near the 5th ring road) and ended in Sanlitun CBD (between the 2nd and 3rd ring road) in Chaoyang District. All the drivers were instructed to drive on the same route at the same time of day. This route to some degree simulates a typical daily commuting routine as employment is spatially concentrated in the inner area of Beijing. The test route contains multiple road types, including an arterial road, a bypass, an expressway, and a highway inside the 5th ring of Beijing. Figure
Road types (a) and map of the test route (b).
Since morning peak hour is commonly defined as 7:00–9:00 a.m. in Beijing (e.g., [
As pointed out in a survey carried out by Xing and colleagues [
Drivers’ background information.
Although battery capacity might fade and increase impedance during cycling [
Traditional statistical tools, such as Mann–Whitney test and correlation analysis, can be used to analyze the characteristics of the whole trip for each driver but are less appropriate to examine the variation and autocorrelation in energy consumption along the route. The best way to identify patterns in energy consumption along the route for each individual in a manner that maintains the completeness of the dataset is to use feature extraction methods as applied in pattern recognition [
A relatively new method known as Singular Spectrum Analysis (SSA) is a powerful technique based on the decomposition of time series [
Detailed descriptions of the SSA algorithm are available elsewhere [
For a standardized time series
Given the high dimensionality of the spatial sequence data, it is highly beneficial to extract and visualize the structure of similarity and differences between the drivers. Clustering is a powerful tool to reveal and visualize the structure of data. The choice of methods for measuring similarities/dissimilarities has a significant impact on the clustering results. According to Izakian et al. [
An agglomerative clustering method (Ward’s Method) is used with DTW as the distance function. Ward’s Method has been chosen because of its suitability for small datasets concerning robustness and efficiency compared to the
This section summarizes the results from our experiments and starts with statistical analysis of various characteristics at the level of the whole trip (temporal domain analysis). It then turns attention to variations in energy consumption between moments along the trial route (spatial domain analysis).
Figure
Energy consumption and time cost for all drivers.
Real road traffic conditions affected vehicles during the driving process, which included repeated episodes of start, acceleration, deceleration, and stop operations. The trip was divided into four types of driving status: acceleration, deceleration, constant speed, and idling. The idling mode is defined as the condition in which the battery power is turned on to supply the motor although the actual vehicle speed is 0; the acceleration mode is defined by the acceleration speed
Driving status distribution for all drivers.
The average total number of episodes of acceleration, deceleration, constant speed, and idling were 214, 206, 309, and 30 for the departure trip (peak hour) and 145, 140, 218, and 9 for the return trip (off-peak). As shown in the Mann–Whitney test in Table
Mann–Whitney test for episode frequency by driving status according to traffic condition (congested versus smooth).
Congested | Smooth |
|
|
---|---|---|---|
Number of idling episodes | 30 | 9 | 0.000 |
Number of constant speed episodes | 309 | 218 | 0.001 |
Number of deceleration episodes | 206 | 140 | 0.000 |
Number of acceleration episodes | 214 | 145 | 0.001 |
Although the lateral comparison does not indicate a strong positive relation between acceleration share and energy consumption, the longitudinal comparison for individual drivers does give some hints. The driver (S5) with the largest change in energy consumption between the smooth (off-peak) trip and the peak congested (peak hour) trip (decrease by 18.4%) showed a decrease in total deceleration and acceleration shares with 2.0 percentage points. In contrast, the driver with the smallest change in energy consumption (S10, decrease by 3.3%) displayed an increase of the summed deceleration and acceleration shares with 3.0 percentage points in the smooth trip.
Correlation analysis has been conducted to obtain a better understanding of the relationships between energy consumption and other trip attributes and among the latter (Table
Pearson’s correlation coefficients for various trip attributes, by road traffic conditions.
Congested | Energy consumption | Travel time | Acc share | Dec share | Const share | Idling share | Energy regeneration | Average Acc | Average Dec | Ambient temperature |
---|---|---|---|---|---|---|---|---|---|---|
Energy consumption | 1.00 | |||||||||
Travel time | 0.32 | 1.00 | ||||||||
Acc share |
|
0.39 | 1.00 | |||||||
Dec share | 0.04 |
|
0.13 | 1.00 | ||||||
Const share |
|
|
|
|
1.00 | |||||
Idling share | 0.29 |
|
|
|
|
1.00 | ||||
Energy regeneration | 0.56 | 0.02 |
|
|
|
0.20 | 1.00 | |||
Average Acc |
|
0.21 |
|
0.43 |
|
0.42 |
|
1.00 | ||
Average Dec | 0.55 |
|
|
0.51 |
|
0.24 |
|
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1.00 | |
Ambient temperature | 0.40 | 0.10 | 0.32 |
|
|
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0.35 | 0.43 |
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|
||||||||||
Smooth | Energy consumption | Travel time | Acc share | Dec share | Const share | Idling share | Energy regeneration | Average Acc | Average Dec | Ambient temperature |
|
||||||||||
Energy consumption | 1.00 | |||||||||
Travel time | 0.26 | 1.00 | ||||||||
Acc share | 0.36 | 0.46 | 1.00 | |||||||
Dec share | 0.21 |
|
|
1.00 | ||||||
Const share |
|
|
|
|
1.00 | |||||
Idling share |
|
0.36 |
|
0.03 |
|
1.00 | ||||
Energy regeneration |
|
0.33 |
|
0.45 |
|
|
1.00 | |||
Average Acc | 0.49 | 0.35 |
|
0.38 |
|
|
|
1.00 | ||
Average Dec | 0.18 | 0.16 | 0.19 |
|
|
0.01 |
|
|
1.00 | |
Ambient temperature | 0.40 | 0.44 | 0.52 |
|
|
0.32 |
|
0.38 | 0.27 | 1.00 |
The energy regeneration ratio is positively correlated with acceleration and deceleration share (
Regeneration ratio versus journey energy efficiency by traffic conditions.
High-resolution spatial data on energy consumption has rarely been studied directly before due to the limited availability of the data in the case of conventional vehicles. However, the highly electrified system in EVs makes it possible to record instant energy consumption based on battery pack current and voltage. This data can be used to examine how driving behaviors and styles, traffic conditions, and infrastructure conditions such as road curvature, traffic signals, and exits affect energy consumption along the trial route.
As a first step of analysis, we have plotted the geographical distribution of averaged speed and energy consumption (and the respective standard deviations) along the route for the 13 drivers in Figure
Speed and energy distribution along the route.
As described in Section
An appropriate window length
Let us use as an example to illustrate the SSA process with the departure trip data of driver S10. We have used MATLAB to perform the SSA. Choosing
Logarithms of the 30 eigenvalues.
The drop in values around component 8 could be interpreted as the start of the noise floor. Together the first eight components account for 83.3% of the variation in the original sequence. The respective reconstructed components for the first eight components are shown in Figure
The first eight reconstructed components plotted as time series.
RC 1 represents the slowly varying trend component which excludes oscillations. Based on the closeness of corresponding eigenvalues and the similarity in frequency, RC 2, RC 3, RC 4, and RC 5 are paired as the harmonic components which show the pattern of periodic oscillation in the original series. The harmonic component could probably be interpreted as the periodic impact of interferences, including recurrent congestion points. The rest of the eigenvectors are categorized as noise. Figure
Reconstructed trend (a), harmonic (b), and noise (c).
This SSA process was performed twice for each driver and for all 13 drivers separately to extract the trend component which we define as the main feature of interest for each driver. The harmonic component is correlated with the trend component to a certain degree, yet of a much more versatile nature. In the present study, we focus only on the trend component which accounts for over 60% of the variation in the original sequential data.
After extracting the trend components of energy consumption for each individual, the DTW calculation and agglomerative clustering process are carried out with the standardized values (
Dendrograms of the clustering results, congested (a) and smooth (b).
The energy consumption profiles (
Energy consumption pattern for different clusters in congested traffic condition: (a) Cluster A: S2, S7, S10, S11, and S13; (b) Cluster B: S3, S5, S8, S9, and S12; (c) Cluster C: S1 and S6; (d) anomaly: S4.
The energy consumption profiles (
Energy consumption pattern for different clusters in smooth traffic condition: (a) Cluster A: S1, S2, S3, S8, and S9; (b) Cluster B: S5, S11, S12, and S13; (c) Cluster C: S6 and S10; (d) anomaly: S4 and S7.
To better understand the differences in energy consumption profile, we have mapped both the original energy consumption (the sum of the trend, harmonic, and noise components) and speed along the route for each cluster using QGIS (Figures
Speed and energy consumption profiles for different clusters in congested condition.
Speed and energy consumption profiles for different clusters in smooth condition.
Figure
When departing from the highway and entering the expressway again, drivers in all clusters show a decrease in speed because the complex signalized intersection linking the highway to the expressway is a bottleneck. The energy consumption trend of Cluster A peaks near this bottleneck as the speed shows that it encounters a longer congested length compared to the other two clusters. After drivers enter the expressway again, the continuous low speed results in low energy consumption in all clusters. While clusters A and B maintain a constant trend until the end of the journey, energy consumption in Cluster C peaks at the bending point of the expressway (north 3rd ring road and east 3rd ring road). This pattern of Cluster C can be attributed to a lack of proactive slowing down behavior followed by “stop-and-go” driving in the congested sectors.
The occurrence of a net energy regeneration road sector (blueish sector) is always associated with a peak in the energy consumption profile (Figure
In smooth traffic conditions (Figure
It is worth mentioning that net energy regeneration sectors occur less often in smooth than in congested traffic conditions. While a freer driving environment might be expected to induce more variation in energy consumption, this is not borne out in our experiment. The setting for urban driving in megacities like Beijing is always quite constrained (speed limit, traffic signal, road curvature, road safety regulations, etc.), so the speed profile cannot be manipulated to the same extent as on test tracks as in previous research [
This paper has introduced an exploratory experiment of EV driving behavior which was undertaken to understand the variation of EV energy efficiency among different drivers in Beijing context. It is among the first attempts to systematically compare real-world spatial sequence data on energy consumption for EV drivers, and the approaches put forward in the paper can be used for data from large-scale EV fleets in the future. The significant heterogeneity among drivers’ revealed energy consumption along the trial route, which is not captured in the statistical results at the level of the total journey, meriting further attention in future research with a larger and more diverse fleet of EVs and greater numbers of drivers.
The paper has made two more specific contributions to the existing literature. First, it has shown that in combination the SSA method and agglomerative clustering using the DTW distance offer a feasible approach to simplify and decipher the heterogeneity in energy consumption profiles that are present in sequential but seemingly erratic data. Second, both the SSA method-based analysis and the earlier correlation analysis have revealed how energy efficiency is affected clearly by drivers’ behavior and through this by road infrastructure (e.g., type of road, curvature), traffic conditions (congestion), and personal driving styles. Of particular interest is that more heterogeneity exists among drivers in the same cluster in relatively smooth traffic than in congested, peak hour conditions. This suggests that recurrent congestion during peak hours places more constraints on driving behavior so that drivers with similar driving styles tend to converge in revealed driving behavior.
The analysis reported in this paper is unable to differentiate the impacts of the physical environment (recurrent congestion, road curvature) from those of individual-specific driving style. Nevertheless, a certain degree of consistency is observed in the driving behavior of more energy efficient drivers under different traffic conditions. While the study has not directly focused on the analysis of eco-driving behavior, the results are in line with the claim that eco-driving can have substantial influence on energy consumption in EVs (which usually are more energy efficient than ICEVs). In contrast, it seems likely that the “energy regeneration ratio” is a poor indicator of eco-driving. The use of energy regenerative function may bring about more local-scale net energy regeneration sectors on a particular trip, but this benefit is always associated with an overall trend of increased energy consumption. Our results imply that behavioral change in driving can lead to substantial energy efficiency improvements, even in EV fleets. It is particularly in congested traffic conditions where the benefits of EV eco-driving can be reaped.
The findings of this research point out the importance for car manufacturers to estimate the driving range more accurately by including personal driving style factor, infrastructure design, and traffic condition factors in the calculations and projections of EV energy consumption. Providing such information may help to overcome range limitations among drivers and assist them to modify their driving habits. It may also increase public trust in information on EV performance that is provided by manufacturers.
The authors declare that there are no conflicts of interest regarding the publication of this article.
The authors acknowledge the support from Nissan (China) Investment Co., Ltd., which provided the test vehicle for this study. The authors would also like to thank Mr. Simon Abele from School of Geography and the Environment, University of Oxford, for his help in the visualization of the spatial data. An early version of this work was presented at the 5th European Battery, Hybrid and Fuel Cell Electric Vehicle Congress (EEVC 2017).