Estimating Electric Vehicle Driving Range with Machine Learning

David Albuquerque

1,2

, Artur Ferreira

1,2 a

and David Coutinho

1,2 b

ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Polit

ecnico de Lisboa, Portugal

Instituto de Telecomunicac¸

oes, Lisboa, Portugal

Keywords:

Dataset Construction, Driving Range Estimation, Electric Vehicle, Feature Engineering, Machine Learning,

Regression, Supervised Learning.

Abstract:

In the past years, we have witnessed an increase on the use of electric vehicles (EV), which are now widely

accepted as reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key

parameters of choice for the consumer is its driving range (DR) capability. The DR depends on many factors

that should be addressed when predicting its value. In some cases, the existing heuristic techniques for DR

estimation provide values with large variation, which may cause driver anxiety. In this paper, we explore the

use of machine learning (ML) techniques to estimate the DR. From publicly available data, we build a dataset

with EV data suitable to estimate the DR. Then, we resort to regression techniques on models learned on the

dataset, evaluated with standard metrics. The experimental results show that regression techniques perform

adequate and smooth estimation of the DR value on both short and long trips, avoiding the need to use the

previous heuristic techniques, thus minimizing the drivers anxiety and allowing better trip planning.

1 INTRODUCTION

In recent years, car manufacturers have moved to the

manufacturing of electric vehicles (EVs), due to some

factors, such as the concern on climate change, the

dispositions of the Paris Agreement, and the need

to be eco-friendly. EVs are accepted as a sustain-

able transport solution, which have grown in popu-

larity. Vehicle manufacturers have increased the com-

petitiveness on the vehicle’s performance, namely the

driving range (DR) capability, since it is a key factor

for consumers (Egbue and Long, 2012).

The EV driving range at a given point in time of

a trip, deﬁned as the eRange, is an estimate of the re-

maining driving distance, expressed in kilometers. Its

proper estimation eases the drivers anxiety on a trip

to a charging station and allows the driver to do ad-

equate trip planning (Smuts et al., 2017; Song and

Hu, 2021). However, the accurate estimation of the

eRange value is a challenging task, since it depends

on many dynamic driving data parameters, such as:

vehicle design; drivers behavior; weather conditions;

road inclination; commute type - city driving or high-

way driving; battery state-of-charge (SoC).

Figure 1 depicts the eRange and SoC con-

cepts (Coutinho, 2021b). Figure 2 shows the main

https://orcid.org/0000-0002-6508-0932

https://orcid.org/0000-0001-7558-9663

inﬂuencing forces on a vehicle that lead to the ac-

tual battery energy consumption. The accurate es-

timation of the eRange allows consumers to rely on

its vehicle for longer travel time and efﬁcient charg-

ing plans. The challenges and difﬁculties posed in the

eRange estimation have lead to recent studies on this

topic (De Cauwer et al., 2017; Varga et al., 2019).

In the past years, machine learning (ML) techniques

have shown their effectiveness in different ﬁelds. This

is due to its nature of learning models from existing

data to gradually achieve better results making it a

widely recognized tool for many problems.

The existing eRange estimation techniques are

based on some heuristics, such as the analysis of the

average consumption of energy in the past minutes,

on a trip. These simple estimations may not produce

accurate results, since they account only for a small

Figure 1: An example of the EV eRange and state-of-charge

concepts, along with other indicators (Coutinho, 2021b).

336

Albuquerque, D., Ferreira, A. and Coutinho, D.

Estimating Electric Vehicle Driving Range with Machine Learning.

DOI: 10.5220/0011672100003411

In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023), pages 336-343

ISBN: 978-989-758-626-2; ISSN: 2184-4313

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Figure 2: The key inﬂuencing forces on a moving vehicle.

( F

-inertial force; F

-tractive force; F

-gravitational force;

-rear rolling resistance force; F

f r

-front rolling resistance

force; F

-aerodynamic (air) drag; F

-normal force; CG-

center of gravity; α-the road slope ).

set of factors and DR estimation depends on differ-

ent variables. It is expected that the use of ML meth-

ods and models will provide better estimates, by us-

ing datasets with more variables than the ones con-

sidered in heuristic methods. Although, the training

of the models may take more time than the existing

approaches, it is expected that their use will improve

prediction accuracy. The challenge is to learn accu-

rate models with adequate response time when placed

on-board of the vehicles.

1.1 Our Proposal

In this paper, we address the eRange estimation prob-

lem with ML regression techniques, through a three

phase approach: the dataset construction and pre-

processing; learning ML models; evaluation of the

learned ML models, with standard metrics.

The remainder of this paper is organized as fol-

lows. Section 2 refers to the state-of-the-art on ex-

isting eRange estimation solutions and their use with

the available datasets. In Section 3, we present the ap-

proach and methodologies adopted in this work. The

experimental evaluation and discussion are reported

in Section 4. The paper ends in Section 5 with some

concluding remarks and directions for future work.

2 STATE OF THE ART

In this section, we address the literature and resources

for EV research, such as the eRange estimation prob-

lem, the availability of public datasets, and existing

approaches without and with ML techniques.

2.1 Research on EV

The study of EV related topics, has been the focus of

many works such as statistical measurement of charg-

ing (Brighente et al., 2021), eRange prediction (Varga

et al., 2019), charging topologies (Yilmaz and Krein,

2013), and regenerative braking (Yoong et al., 2010).

The eRange prediction is an important EV feature

to provide to consumers, as it reduces the driver’s

anxiety while driving and allows better trip planning.

When devising a solution to the eRange estimation

problem, real EV driving data in the form of a dataset

is required to learn and evaluate the proposed mod-

els. It is also necessary to compare the learned models

with the existing ones.

Vehicle manufacturers typically do not disclose

vehicle driving data or prediction algorithms. As a

good eRange prediction is a competitive factor among

EV manufacturers, it is not disclosed to the public.

2.2 Public Domain EV Datasets

Some datasets regarding EV eRange estimation are

publicly available, being composed by vehicle data

and trip data, with mainly two types of features: time-

series features, where the data points vary as a func-

tion of time; trip-invariant features, in which a given

value is kept for the entire trip. Time-series fea-

tures are usually the SoC, energy consumption, speed,

acceleration, and elevation. The trip-invariant fea-

tures refer to vehicle information such as battery ca-

pacity, average energy consumption (AEC), full bat-

tery energy (FBE), full driving distance (FDD) also

known as full battery distance (FBD), vehicle weight,

trip information such as commute type (city or high-

way), total energy consumption, and total distance.

Table 1 summarizes the key publicly available EV

datasets, namely: Vehicle Energy Dataset (VED) (Oh

et al., 2019); Emobpy dataset (Gaete-Morales et al.,

2021); Classic EV X project (Coutinho, 2021b)

dataset; Charge Car project of the CREATE Lab at

Carnegie Mellon University Robotics Institute, avail-

able at https://www.chargecar.org; the EV dataset of

the national big data alliance of new energy vehicles

(NDANEV), http://www.ndanev.com.

The VED dataset (Oh et al., 2019) provides 54 dif-

ferent EV driving trip data records for estimation, but

lack trip and vehicle information as well as EV model

variety. It contains data from three distinct EVs, all

from the same model, the 2013 Nissan Leaf.

The Emobpy Python tool (Gaete-Morales et al.,

2021) focuses on EV trip and charge data generation

through empirical mobility statistics and customiz-

able assumptions. This approach provides an inﬁnite

supply of EV trips as well as proper vehicle informa-

tion. This dataset has some missing features such as

speed, elevation, trip, and commute type.

The Charge Car project of the CREATE Lab at

Carnegie Mellon University publicly supplies crowd-

Estimating Electric Vehicle Driving Range with Machine Learning

337

Table 1: Public domain datasets with EV data and their key properties. N/A means that the data parameter is not available.

Ensures table is placed on correct location

VED dataset Emobpy Classic EV X project ChargeCar NDANEV

Trips 507 Unlimited 3 373 2372

EV Models 1 102 1 N/A 1

Number of EV 3 N/A 1 N/A 5

Features

timestamp,

speed,

location,

battery SoC,

battery voltage,

battery current,

AC power,

heater power,

outside air temperature (OAT)

timestamp,

distance,

instant energy

consumption (IEC),

consumption,

average power,

state

timestamp,

IEC,

remain battery

energy (RBE),

speed

timestamp,

elevation,

planar distance,

adjusted distance,

speed,

acceleration,

model power,

actual power*,

current*,

voltage*

timestamp,

speed,

total voltage,

total current,

battery SoC,

temp. range,

motor voltage,

motor current,

mileage

sourced data that has served previous eRange predic-

tion models (Zheng et al., 2016). This dataset has a

large vehicle diversity due to the open nature of the

platform, allowing any user to upload combustion en-

gine based vehicle information as well as the location

data, speed, and weather, among other features.

A dataset collected through probe data from

nearly 500 battery EVs by the Japan automobile re-

search institute (JARI) from February 2011 to Jan-

uary 2013 has the following features: time, location,

vehicle state (driving, normal charging, or fast charg-

ing), speed, air-conditioner, heater state, and SoC.

Although useful and featured in some papers (Liu

et al., 2017; Liu et al., 2018; Sun et al., 2015; Sun

et al., 2016), for the research conducted in this paper,

the authors were unable to access this dataset from

https://www.jari.or.jp/research-database.

The cloud based EV dataset supplied by the

NDANEV has been used in similar eRange prediction

approaches (Zhao et al., 2020). This dataset includes

battery cell temperature information, which measures

the battery cell inconsistency.

As some datasets do not explicitly provide ve-

hicle information, the EV-database website, https:

//ev-database.org offers a public database for exist-

ing EV, displaying AEC, DR, and usable battery en-

ergy. The availability of this data enables that datasets

which lack this feature can be used in eRange predic-

tion models.

2.3 EV Autonomy Estimation

The eRange estimation has been an interesting topic

in research in recent years, in part due to the increase

in EV usage, as they become more efﬁcient. Its pre-

diction difﬁculty is in part due to the fact that there

are many factors to take into account when measur-

ing it, such as battery and road information, previous

vehicle trips, and vehicle weight. This has motivated

researchers to ﬁnd solutions for this problem, resort-

ing to ML techniques.

Related work has shown the use of eRange estima-

tion on EV, stating the need for different types of ac-

curacy on eRange estimation as a function of the SoC

state. In (Zhang et al., 2012), the approach is to min-

imize the performance impact of minimum cost route

searching from high accuracy eRange prediction.

The proposal in (Coutinho, 2021a) estimates the

eRange through a basic approach (BA) algorithm,

which uses manufacturers invariant vehicle informa-

tion such as FBE and AEC, as well as the instant SoC

value. In detail, the BA estimates eRange using:

• the maximum charge an electric vehicle battery

can store, known as FBE;

• the average energy consumption (AEC) of the EV,

which depends on the use of the air-conditioner

system (AcS) and on the type of trip (highway or

city driving).

It also requires the battery SoC value, at the time of

the eRange estimation. Thus, the BA eRange estima-

tion is given by

eRange =

FBE

AEC(AcS)

× SoC [km]. (1)

The eRange estimation with an adaptive history-

based approach (HBA), proposed by (Coutinho,

2021b), relies on the past 10 minutes AEC informa-

tion gradually inﬂuenced by the vehicle instant con-

sumption energy, as well as by the SoC. Once the ﬁrst

10 minutes had passed, HBA applies a conﬁgured en-

ergy step to the previous prediction, depending on the

computed AEC. This approach yields more optimistic

eRange results, with slightly higher values than those

of BA, thus easing consumer’s anxiety when a higher

energy consumption does not have a linear impact on

the eRange. HBA relies on parameters such as the

full battery energy (FBE), SoC, where an instant en-

ergy consumption (IEC) is used to calculate, at each

minute, an adaptive AEC (AAEC). Thus, the eRange

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

338

estimation for the m-th minute is

eRange



FBE

AAEC

× SoC



, (2)

where ⌊.⌋ is the ﬂoor operator. This algorithm

requires three additional parameters: delta energy

step, ∆S, which is the amount of energy incre-

ment/decrement, at each minute m; constant AEC

(CAEC), as provided by the EV manufacturer; min-

imum instance energy. AAEC

is updated by

AAEC







CAEC, m ≤ N

AAEC

m−1

− ∆S, MAAEC

< AAEC

m−1

AAEC

m−1

+ ∆S, MAAEC

≥ AAEC

m−1

(3)

by adding or subtracting the pre-conﬁgured delta

step, ∆S, to the previous AAEC calculation of the pre-

vious m minutes. Initially, AAEC is equal to the pre-

conﬁgured CAEC, until it is possible to calculate the

moving average with minimum number of N samples.

In Equation (3), MAAEC

is the moving average of

the current m minute IEC values, where every non-

zero IEC value is averaged for its calculation by

MAAEC

N−1

∑

i=0

× AAEC

m−i

, (4)

where N is the number of past minutes of an ob-

servation moving window. The average weights are

(i+1)

, such that the most recent AAEC values

have higher importance. The minimum instance en-

ergy’s role is to prevent the algorithm from perform-

ing an eRange calculation when the average IEC val-

ues for the current m minute are less than a prede-

ﬁned threshold value. This is done so that in the event

the vehicle consumes negligible power, it would not

cause an ∆S decrement or increment on the eRange

estimation, thus preventing inaccurate eRange results.

2.4 The Use of Machine Learning

The use of ML for a multitude of cases (Amershi

et al., 2019) in ﬁelds such as big data (Zhou et al.,

2017) and data mining (Bose and Mahapatra, 2001)

has proven its robustness on solving different prob-

lems. As a result, some approaches for the eRange

estimation problem have resort to supervised learning

techniques. The use of decision trees (DT) (Alpay-

din, 2020), random forest (RF) (Breiman, 2001), and

K-nearest-neighbor (KNN) (Alpaydin, 2020) in en-

semble stacked generalization (ESG) approach (Ullah

et al., 2021), through the JARI dataset (Ullah et al.,

2021) shows better results than the use of its individ-

ual base models to predict the EV energy consump-

tion. Recent models using gradient boosted regres-

sion tree (GBRT) have combined extreme gradient

boosting (XGBoost) and light gradient boosted ma-

chine (LightGBM) to provide better predictive per-

formance from these ensemble methods (Zhao et al.,

2020) with the NDANEV dataset. This approach

classiﬁes four driving patterns from three parameters

(speed, motor current, and change rate of motor cur-

rent), through K-means clustering algorithm and thus

inﬂuencing the resulting eRange due to their different

energy consumption rates.

The use of unsupervised clustering approaches

by self-organizing maps (SOM) (Kohonen, 2001)

have been addressed to cluster big data into driv-

ing patterns, prior to range estimation (Lee and Wu,

2015). The hybrid version of SOM with regres-

sion tree (RT) (Hastie et al., 2009) has taken ad-

vantage of SOM’s neurons storage feature of near-

ing related neighbor information being kept closely

together. Therefore, avoiding bushy trees and im-

proving upon previous solutions by keeping mean-

ingful knowledge extraction (Zheng et al., 2016) both

approaches used different datasets from undisclosed

monitored data sources.

Reinforcement learning has also been used for

external energies disturbances on the speed proﬁle

of a driving proﬁle so that it could then be com-

bined with multiple linear regression (MLR) for the

estimation (De Cauwer et al., 2017), using EVte-

clab’s dataset. Although more complex than previ-

ous statistical-based approaches, the use of ML for

eRange estimation reduces the error, and thus further

justifying its use in this paper.

3 PROPOSED APPROACH

In this section, we detail the approach and the

methodology that we have followed on this work.

Figure 3 depicts the generic diagram of the approach

in which we highlight that the ultimate goal is to pro-

vide the eRange estimation from the data in the input

dataset, using ML techniques.

Dataset

Existent EV

trip datasets

Process Dataset

Features

Machine

Learning

Expected

eRange [km]

Predictor

Real time driving

information *

Predicted

eRange [km]

Performance

Evaluation

* To be implemented

Figure 3: The detailed steps of the proposed approach with

ML to estimate the eRange value, from a EV dataset.

Estimating Electric Vehicle Driving Range with Machine Learning

339

3.1 Dataset Construction

As this work focuses on a relatively recent ﬁeld of

study, the dataset availability is both scarce and diver-

gent, thus yielding the need to perform dataset con-

struction and preprocessing phases. A dataset was

created from historical trafﬁc data with personally

recorded vehicle trips, as well as external existing and

publicly available datasets from both the VED, Clas-

sic EV X (Coutinho, 2021b) and ChargeCar datasets,

integrated into our dataset. The resulting dataset con-

tains multiple trips with their respective vehicle speed

(in km/h), distance (in km) battery information such

power consumption (in kW), current (in amps) and

state of charge (in %) on a time series format.

The composed dataset is used to train the se-

lected eRange prediction models on the learning

phase through ML, allowing it to ﬁt its eRange es-

timation for each trip on the dataset. Figure 4 shows

the dataset construction phase.

The key reasons for building such a dataset are

as follows. When training a ML model for regres-

sion problems, the accuracy of the results on test data

will depend on the diversity of the data. To ensure

model effectiveness on different vehicles and to avoid

over-ﬁtting, we opted for a diverse EV model dataset

built from existing available datasets, mentioned in

Table 1.

The algorithm integrates EV trip datasets for train-

ing, thus requiring EV trips time-series with the fol-

lowing features: SoC (in percentage), power con-

sumption (in kWh), distance (in km) and speed (in

km/h). We also have vehicle information: AEC

(kWh), FBE (kWh), and FBD (km). For this rea-

son, both VED, Classic EV X and ChargeCar datasets

were selected.

The trip type and the minimum driving time are

two variables found to inﬂuence ML methods perfor-

mance. On the preprocessing phase, some features

such as AEC, FBE, and FBD are sometimes missing

on certain datasets. These features can be obtained

from existing static EV information datasets such as

https://ev-database.org. Other features such as accel-

eration and distance, are trip dependent being com-

puted with mathematical formulas.

VED

Dataset

ChargeCar

Dataset

Classic EV X Project

Driving Range Prediction

Dataset

EV X Driving

Range Prediction

Dataset

Figure 4: Dataset construction and preparation.

The constructed dataset is mainly composed by

short vehicle trips with less than 20 minutes, with 457

out of 503 trips (90.9%) ﬁtting in this category. The

dataset is available at (Albuquerque, 2022).

This disparity in the training data could cause im-

precise prediction on longer trips where different con-

sumption proﬁles are observed, such as traveling on a

highway. Another version of the dataset to be imple-

mented in the future should contain longer EV trips,

as well as the eMini project (Coutinho, 2021a) inte-

gration, for newer trip monitoring.

3.2 Learning the Models

The target (expected/baseline) eRange values are

provided by an implementation of the HBA algo-

rithm (Coutinho, 2021b), since it is not possible to

obtain actual range values. Over the dataset, we ap-

ply this approach to compute the target eRange values

. This approach addresses real-time AEC values,

that relies on the vehicle’s past N = 10 minute win-

dow of the trip’s energy consumption history as well

as the real-time SoC value. HBA was designed as a

better alternative to BA, which was also included as a

benchmark algorithm.

3.3 Regression Techniques

In this paper, we have considered the following re-

gression techniques: linear regression (LR) (Hastie

et al., 2009); ensemble stacked generalization

(ESG) (Ullah et al., 2021).

LR is a well-known statistical technique (Hastie

et al., 2009), such that it models the relationship be-

tween a scalar response and one or more independent

variables. In this case, we have multiple linear regres-

sion, since we deal with several input variables and

one output variable. The relationships between the

input and output variables are modeled using linear

predictor functions, whose unknown model parame-

ters are estimated from the data.

The ESG algorithm follows the Wolpert stacking

technique (Wolpert, 1992), combining two models.

The ﬁrst one, named as base-model (Level-0) encom-

passes DT, RF, and KNN classiﬁers. The second

model (Level-1) is adaptive boosting (AdaBoost),

combining base model predictions to provide a single

output.

The ESG model follows the original (Ullah et al.,

2021) implementation with some differences. The

original application was the EV energy consump-

tion prediction and not eRange. Moreover, the lack

of availability of its JARI dataset could make this

implementation’s accuracy differ when training with

ICPRAM 2023 - 12th International Conference on Pattern Recognition Applications and Methods

340

our dataset. An additional ESG implementation

named ESG* was derived from the original ESG ap-

proach, changing some conﬁgurations to better ﬁt the

project’s constructed dataset. The maximum number

of features conﬁgured for DT and RF algorithms are

9 and 7, respectively. As for the KNN, we set K = 7

neighbors, the distance metric is minkowski with pa-

rameter p set to 1. Figure 5 depicts the ESG model.

After the training of these ML algorithms, they are

used for the prediction of eRange. One of the predic-

tors is then selected based on its performance with

standard metrics. Then, it can be used for future exe-

cution on a real-time trip.

4 EXPERIMENTAL EVALUATION

In this section, we report on the experimental evalua-

tion of our approach. First, we present the evaluation

metrics considered in this work. Then, we report on

experimental results on the built dataset.

4.1 Standard Evaluation Metrics

After training the ML algorithms with the dataset, we

perform eRange estimation. The resulting prediction

is then used for the computation of the evaluation met-

rics so than it can be compared with other algorithms.

As the estimation accuracy must be measured

for each eRange estimation algorithm, ﬁve standard

evaluation metrics were chosen for this task: mean

absolute error (MAE), mean squared error (MSE),

mean absolute percentage error (MAPE), root mean

squared error (RMSE), and R

metric, which is the

coefﬁcient of determination (Wright, 1921). These

evaluation metrics are employed in the k-fold cross

validation (CV) of the selected testing trip, minimiz-

ing the presence of bias.

Decision Tree

(DT)

K-Nearest Neighbor

(KNN)

Random Forest

(RF)

AdaBoost

K-Fold Cross Validation

Features

Figure 5: Ensemble Stacked Generalization (ESG)

model (Ullah et al., 2021).

4.2 Experimental Results

The experiments were carried out on a computer

with the Manjaro Linux operating system, 5.18.19-

3-MANJARO kernel, AMD Ryzen 9 3900X (24)

@3.800 GHz and 48 Gb of RAM. The Python run-

time version is 3.9, using Jetbrain’s Pycharm as the

integrated development environment (IDE).

The constructed dataset holds multiple EV trips,

most of them being short city commutes, which dif-

fer in consumption of traveling in highways on longer

trips. This can cause training bias with worse predic-

tions on longer trips due to the reduced samples in the

training data. To study this effect, a longer 47 minutes

Nissan Leaf 2013 model EV trip was chosen from the

VED dataset (Oh et al., 2019) for testing, while the re-

maining trips were used for training, deﬁning a mini-

mum trip time (MTT) required for a trip to be included

into the training set.

We have considered MTT ∈ {0, 10, 20, 30, 40}.

The number of trips available for training decreases,

as the MTT value increases. The eRange prediction

will be calculated for the selected test trip. We report

the time and evaluation metrics for the k-fold CV re-

sults, with k = 20 folds.

We compute different eRange estimation results

for the selected trip and estimation algorithms, pro-

viding an easy overview of the different dataset pa-

rameters, with multiple datasets.

Figures 6 and 7 present the eRange prediction

from the BA, HBA (the ML baseline), LR, ESG and

ESG* algorithms (in blue, red, purple, green, and

light blue, respectively) for MT T = 0. The heuristic

approaches provide similar estimates of the eRange

values. On the 12 minutes to 20 minutes time win-

dow, HBA yields a stable estimation, with segments

such that the minimum instance energy is not enough

to trigger a recalculation for the eRange while the BA

counterpart provides a continued decreasing estimate.

On ML approaches, LR provides a smoother pre-

diction evolution, while the ESG method attains esti-

mates with larger deviation and variance. This could

be the result of DT having high sensitivity to the train-

ing data, as our dataset contains a larger number of

smaller EV trips with different consumption proﬁles

than of longer highway trips. The ESG* which re-

Figure 6: eRange estimation with BA and HBA algorithms.

Estimating Electric Vehicle Driving Range with Machine Learning

341

Figure 7: eRange estimation by the HBA (ML baseline),

LR, ESG, and ESG* algorithms.

sulted from a better conﬁguration to our dataset has

yielded a better predictive accuracy than the origi-

nal model. These estimates can be smoothed with a

moving-average ﬁlter applied to the sequence of ESG

estimated values.

The test trip prediction metrics for the ML algo-

rithms are reported on Table 2, and the CV metrics

are presented on Table 3.

Table 2: LR and ESG metrics for all MTT. For each MTT

value (in minutes), the best result is in bold face.

ML MTT MAE ↓ MSE ↓ MAPE ↓ RMSE ↓ R

↑ Time (m,s,ms)

LR 0 0.63 0.63 0.01 0.79 0.98 (0,0,138)

ESG 0 2.61 17.17 0.05 4.14 0.56 (7,25,772)

ESG* 0 1.20 2.31 0.02 1.52 0.94 (14,10,940)

LR 10 0.59 0.60 0.01 0.77 0.98 (0,0,72)

ESG 10 2.31 12.13 0.04 3.48 0.68 (4,2,146)

ESG* 10 1.51 3.65 0.02 1.91 0.90 (7,37,998)

LR 20 0.58 0.58 0.01 0.76 0.98 (0,0,29)

ESG 20 3.88 30.72 0.07 5.54 0.21 (1,33,534)

ESG* 20 2.18 7.52 0.04 2.74 0.80 (2,52,129)

LR 30 0.70 0.78 0.01 0.88 0.98 (0,0,14)

ESG 30 9.28 142.91 0.17 11.95 -2.66 (0,41,343)

ESG* 30 4.66 37.70 0.08 6.14 0.03 (1,11,669)

LR 40 0.90 1.47 0.01 1.21 0.96 (0,0,6)

ESG 40 7.17 95.45 0.13 9.77 -1.44 (0,9,706)

ESG* 40 5.11 31.93 0.10 5.65 0.18 (0,15,505)

For both tables, as MTT values increase we ob-

serve a decrease on the training time. The LR algo-

rithm training is always faster than ESG and ESG*

training. Moreover, LR achieves the best metric val-

ues.

Table 3: LR and ESG metrics for all MTT, with CV. For

each MTT value (in minutes), the best result is in bold face.

ML MTT MAE ↓ MSE ↓ MAPE ↓ RMSE ↓ R

↑ Time (h,m,s,ms)

LR 0 0.53 0.72 0.34 0.80 0.99 (0,0,30,64)

ESG 0 1.47 4.72 1.15 2.10 0.99 (4,3,54,919)

ESG* 0 1.27 2.99 1.10 1.67 0.99 (6,50,52,398)

LR 10 0.69 1.07 0.48 0.98 0.99 (0,0,14,543)

ESG 10 1.68 5.83 1.31 2.28 0.98 (1,57,29,701)

ESG* 10 1.33 3.34 1.05 1.77 0.99 (3,22,59,269)

LR 20 0.94 1.80 0.67 1.26 0.98 (0,0,3,805)

ESG 20 2.39 12.41 1.74 3.23 0.93 (0,41,38,272)

ESG* 20 1.71 5.42 1.36 2.21 0.96 (1,14,30,373)

LR 30 1.16 2.80 1.04 1.46 0.93 (0,0,1,143)

ESG 30 3.80 30.43 3.03 4.69 0.19 (0,18,14,883)

ESG* 30 2.83 14.02 2.47 3.39 0.51 (0,30,39,978)

LR 40 1.55 5.15 1.48 1.91 -1.61 (0,0,0,230)

ESG 40 3.26 25.78 2.91 4.17 -6.44 (0,5,26,732)

ESG* 40 2.87 14.41 3.01 3.43 -6.88 (0,8,40,895)

As the ML approaches were trained with HBA

as the baseline eRange target, future integration with

the eMini project would supply the algorithms with

real eRange, improving their prediction accuracy over

time.

5 CONCLUSIONS

The electric vehicle driving range estimation is a rele-

vant problem, since this estimate relieves the driver

anxiety on a trip and allows for a better trip plan-

ning. There are some useful heuristic approaches to

perform this estimation. However, these techniques

provide an estimate with some degree of error, which

may alarm the driver. Machine learning techniques

to provide this estimation have been proven adequate,

despite being applied to a recent ﬁeld of study. There

are some public domain datasets with electric vehicle

data, but their use is not straightforward, requiring a

demanding construction and pre-processing stage for

a reliable dataset with accurate and complete trip data.

In this paper, we have composed such a dataset in

which we have assessed the use of regression tech-

niques to estimate the driving range, based on differ-

ent variables with vehicle data and trip data. The ex-

perimental results have shown the impact of different

training conﬁgurations, on existing machine learning

models. We have compared the prediction accuracy of

these techniques with standard metrics and found that

the linear regression technique shows promising pre-

diction results as well as fast training. This model can

be deployed on-board of a vehicle as it aims to be inte-

grated with the eMini project (Coutinho, 2021a). The

source code is available on the Github (Albuquerque,

2022), incentivising reproducibility on further stud-

ies.

5.1 Future Work

As future work, we plan to perform the integration

of the developed application with the real-time data

of the electric vehicle, continuously providing up-

dated eRange estimations. The base model will be

the one provided by the linear regression technique.

We also plan to include more datasets and features,

such as driving patterns and road elevation. The ﬁne-

tuning of the ESG* parameters and results also de-

serves more attention. Moreover, additional machine

learning techniques can be added to the established

open source experimental setting.

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