For this study, we constructed a system for pre-estimation of electric vehicle (EV) energy consumption on unfamiliar roads. Drivers of EVs must make plans based on estimated energy consumption because they fear that an EV might run out of power and stop on the road. Our constructed system pre-estimates ranges at which an EV can be expected to be forced to stop on a road. The range is calculated using EV driving simulation on a route that is input by a driver. The driving simulation requires only map data and the EV car specifications. Moreover, we assessed a system using a real EV. Results show that the system produced suciently correct ranges on the roads used for experimentation. Additionally, we evaluated the accuracy of ranges output by our system. For evaluation, we used numerous accumulated daily driving logs for EVs.
In recent years, energy-eciency and CO2 emission reduction have become necessary worldwide because of climatic variation and scarcity of fossil fuels. Given that background, electric vehicles (EVs) are attracting global attention. Reportedly, EVs present the benefit that so-called well-to-wheels CO2 emissions are lower than those of internal combustion vehicles (ICVs). In addition, EVs have no emissions when they are running. Many countries have formulated EV deployment goals for the future. Therefore, EVs are expected to penetrate markets gradually worldwide.
Nevertheless, many diculties arise when a user operates an EV. One is the diculty of EV travel planning when a user navigates unfamiliar roads. Planning must be done while considering an EV travel range and when and where one might stop at a charging station. However, EV travel ranges change drastically because of road gradients and traffic conditions. Therefore, average users have diculty making a precise plan for unfamiliar routes.
Proceedings of the VLDB 2019 PhD Workshop, August 26th, 2019. Los Angeles, California. Copyright (C) 2019 for this paper by its authors. Copying permitted for private and academic purposes.
As described in this report, we propose a system that supports an EV user’s travel planning on unfamiliar roads. We present a solution for pre-estimating the EV energy consumption range: minimum energy consumption Emin and maximum energy consumption Emax. We present Emin and Emax to assist planning. In addition, if Emin and Emax are correct, actual energy consumption Ereal is in the range of Emin – Emax. Therefore, to evaluate the accuracy of the proposed system, we conducted EV driving experiments on roads with two conditions and confirmed that Ereal is in the range of Emin – Emax. 2.
Studies of many types have estimated EV energy
consumption and therefore the EV travel range. Using a
motion equation model and actual driving logs collected by a
probe car, most of these studies have produced methods to
calculate EV energy consumption or travel range.
Grubwinkler et al. estimated EV energy consumption from statistical
analysis of driving data generated from large amounts of
collected driving data[
Moreover, many studies have solved optimization
problems of energy consumption and driving using motion
equations and other data. Karbowski et al. proposed a means
of controlling plug-in hybrid EVs (PHEVs) using an energy
consumption simulation generated from trac, road maps,
and Markov Chain[
To pre-estimate EV energy consumption, the EV user inputs only an origin and a destination and anticipated stop locations (sightseeing spots or stores, etc.) during a trip. Next, the system generates origin-destination (OD) trip simulation logs running on candidate routes at a constant speed vc from these inputs. Trip simulation logs are normalized by time. We set speed vc in advance, for example, a speed limit on a road.
Then, trip simulation logs are input to an EV energy
consumption model. We use a model based on a motion
equation[
This subsection presents a description of minimum energy Emin calculation. For this report, Emin is defined as “energy consumption when an EV runs at constant speed vc and does not stop.”
First, an EV energy consumption log is calculated every second by inputting a trip simulation log into the EV energy consumption model. Expression (1) represents the EV energy consumption model. Table 1 presents variables of Expression (1), in which c represents 1/3600/1000 J/kW h, and t denotes a time.
et =
1 c(( ⇢C dAv2 + µM g cos ↵ + M g sin ↵ 2
dv 1 +(M + Mi) dt ) ⇥ ⌘ ⇥ v) [kW h]
Finally, Emin is calculated as the summation of et (Expression (2)). Also, n represents the number of simulation logs of an OD trip.
Emin = n X et [kW h] t=0 (1) (2) 3.2
This subsection presents our description of how to calculate maximum energy Emax. We define Emax as “energy consumption when an EV runs at constant speed vc, and outward homeward accelerates and decelerates when stopping at every stop location and every signal, assuming bad conversion eciency during acceleration and deceleration.”
Therefore, we define Emax as shown in Expression (3) because we want to express it easily. Eacc is described in Expression (4). Additionally, N stands for the number of stops when an EV stops at every stop location and every signal.
Emax = Emin + Eacc [kW h] Eacc = N ⇥
We conducted EV driving experiments for long trips in Hokkaido in 2017 and 2018. Hokkaido has an area that is the longest interval in Japan between charging stations. In this area, a fundamental problem arises of whether an EV can arrive at the next charging station after it leaves a charging station. Therefore, we conducted experiments on three routes to ascertain which route is best for an EV driver.
We simulated kitami 1 and kitami 2 using the system in 2017. Figure 2 shows kitami 1 and kitami 2 routes. We designated charging points as CPs. The kitami 1 travel distance is greater, but the elevation di↵erence is smaller. Furthermore, the kitami 2 travel distance is shorter, but the elevation di↵erence is greater. We simulated kitami 10 in 2018 (Fig. 3) because a new charging station is located there. 4.1.2
Table 3 shows the pre-estimated results. We conducted
EV driving experiments using a Nissan Leaf (Nissan Motor
Co. Ltd.) as the experiment EV in 2017 and 2018. We used
estimated energy consumption logs[
After we accumulated EV energy consumption logs[
As described in this report, we proposed a system for preestimation of maximum and minimum electric vehicle (EV) energy consumption for use with unfamiliar roads. We defined the minimum energy consumption is achieved when an
CP to Destination
Ereal 5.81 kWh 6.40 kWh EV travels at a constant speed. Maximum energy consumption occurs when an EV travels at a constant speed, but also stops at controlled intersections and set places, such as sightseeing spots and stores.
Moreover, we conducted actual driving experiments, which yielded actual energy consumption logs with data in a range showing the estimated minimum energy consumption and estimated maximum energy consumption. Our next challenge is to estimate a range that is specialized for individuals and routes using numerous daily life logs. Another challenge is consideration of an air conditioner’s energy consumption to output more correct EV energy consumption.
Part of this work is supported by JSPS KAKENHI Grant Number 18K11750.