Abstract

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. The proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

References

1.
Lee
,
D.-Y.
,
Thomas
,
V. M.
, and
Brown
,
M. A.
,
2013
, “
Electric Urban Delivery Trucks: Energy Use, Greenhouse Gas Emissions, and Cost-Effectiveness
,”
Environ. Sci. Technol.
,
47
(
14
), pp.
8022
8030
.10.1021/es400179w
2.
Stumpf
,
R.
,
2019
, “
Americans Cite Range Anxiety, Cost As Largest Barriers for New EV Purchases: Study
,” The Drive, accessed Jan. 5, 2022, https://www.thedrive.com/news/26637/americans-cite-range-anxiety-cost-as-largest-barriers-for-new-ev-purchases-study
3.
Shaw
,
N.
,
2021
, “
Call for Better Range Prediction in Electric Cars
,” WalesOnline, accessed Jan. 5, 2022, https://www.walesonline.co.uk/whats-on/shopping/call-better-range-prediction-electric-19962096
4.
Zhang
,
B.
,
Sun
,
X.
,
Liu
,
S.
, and
Deng
,
X.
,
2019
, “
Recurrent Neural Network-Based Model Predictive Control for Multiple Unmanned Quadrotor Formation Flight
,”
Int. J. Aerosp. Eng.
,
2019
, p.
e7272387
.10.1155/2019/7272387
5.
Wu
,
Y.
,
Zhang
,
Y.
,
Li
,
G.
,
Shen
,
J.
,
Chen
,
Z.
, and
Liu
,
Y.
,
2020
, “
A Predictive Energy Management Strategy for Multi-Mode Plug-in Hybrid Electric Vehicles Based on Multi Neural Networks
,”
Energy
,
208
, p.
118366
.10.1016/j.energy.2020.118366
6.
Hu
,
Q.
,
Amini
,
M. R.
,
Wiese
,
A.
,
Seeds
,
J. B.
,
Kolmanovsky
,
I.
, and
Sun
,
J.
,
2022
, “
A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles
,”
ASME J. Dyn. Syst., Meas., Control
,
144
(
1
), p. 011105.10.1115/1.4052819
7.
Barcellona
,
S.
,
Grillo
,
S.
, and
Piegari
,
L.
,
2016
, “
A Simple Battery Model for EV Range Prediction: Theory and Experimental Validation
,” International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles International Transportation Electrification Conference (
ESARS-ITEC
), Toulouse, France, pp.
1
7
.10.1109/ESARSITEC.2016.7841441
8.
Kessels
,
J. T. B. A.
,
Rosca
,
B.
,
Bergveld
,
H. J.
, and
van den Bosch
,
P. P. J.
,
2011
, “
On-Line Battery Identification for Electric Driving Range Prediction
,”
IEEE Vehicle Power Propulsion Conference
, Chicago, IL, pp.
1
6
.10.1109/VP P C.2011.6043022
9.
Sarmiento-Carnevali
,
M.
,
Fly
,
A.
, and
Piecha
,
P.
,
2020
, “
Electric Vehicle Cold Start Range Estimation Through Battery-in-Loop Simulations Within a Virtual Driving Environment
,”
SAE
Paper No. 2020-01-0453.10.4271/2020-01-0453
10.
Hayes
,
J. G.
, and
Davis
,
K.
,
2014
, “
Simplified Electric Vehicle Powertrain Model for Range and Energy Consumption Based on EPA Coast-Down Parameters and Test Validation by Argonne National Lab Data on the Nissan Leaf
,” IEEE Transportation Electrification Conference and Expo (
ITEC
), Dearborn, MI, pp.
1
6
.10.1109/IT EC.2014.6861831
11.
Liu
,
K.
,
Wang
,
J.
,
Yamamoto
,
T.
, and
Morikawa
,
T.
,
2018
, “
Exploring the Interactive Effects of Ambient Temperature and Vehicle Auxiliary Loads on Electric Vehicle Energy Consumption
,”
Applied Energy
, 227, pp.
324
331
.10.1016/j.apenergy.2017.08.074
12.
Gebhardt
,
K.
,
Schau
,
V.
, and
Rossak
,
W. R.
,
2015
, “
Applying Stochastic Methods for Range Prediction in e-Mobility
,” 15th International Conference on Innovations for Community Services (
I4CS
), IEEE, Nuremberg, Germany, pp.
1
4
.10.1109/I4CS.2015.7294483
13.
Bolovinou
,
A.
,
Bakas
,
I.
,
Amditis
,
A.
,
Mastrandrea
,
F.
, and
Vinciotti
,
W.
,
2014
, “
Online Prediction of an Electric Vehicle Remaining Range Based on Regression Analysis
,” IEEE International Electric Vehicle Conference (
IEVC
), IEEE, Florence, Italy, pp.
1
8
.10.1109/IEVC.2014.7056167
14.
Wang
,
J.
,
Besselink
,
I.
, and
Nijmeijer
,
H.
,
2015
, “
Electric Vehicle Energy Consumption Modelling and Prediction Based on Road Information
,” World Electric Vehicle Journal (
WEVJ
), 7(3), pp.
447
458
.10.3390/wevj7030447
15.
Birrell
,
S. A.
,
McGordon
,
A.
, and
Jennings
,
P. A.
,
2014
, “
Defining the Accuracy of Real-World Range Estimations of an Electric Vehicle
,” 17th International IEEE Conference on Intelligent Transportation Systems (
ITSC
), IEEE, Qingdao, China, pp.
2590
2595
.10.1109/IT SC.2014.6958105
16.
Varga
,
B. O.
,
Sagoian
,
A.
, and
Mariasiu
,
F.
,
2019
, “
Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges
,”
Energies
, 12(5), p.
946
.10.3390/en12050946
17.
Deepak
,
S.
,
Amarnath
,
A.
,
Krishnan U
,
G.
, and
Kochuvila
,
S.
,
2019
, “
Survey on Range Prediction of Electric Vehicles
,” Innovations in Power and Advanced Computing Technologies (
i-PACT
), vol.
1
, IEEE, Vellore, India, pp.
1
7
.10.1109/i-PACT44901.2019.8960179
18.
Rahimi-Eichi
,
H.
, and
Chow
,
M.-Y.
,
2014
, “
Big-Data Framework for Electric Vehicle Range Estimation
,”
IECON 2104 - 40th Annual Conference of the IEEE Industrial Electronics Society
, IEEE, Dallas, TX, pp.
5628
5634
.10.1109/IECON.2014.7049362
19.
Conradi
,
P.
,
Bouteiller
,
P.
, and
Hanßen
,
S.
,
2011
, “
Dynamic Cruising Range Prediction for Electric Vehicles
,”
Advanced Microsystems for Automotive Applications
,
G.
Meyer
and
J.
Valldorf
, eds.,
VDI-Buch, Springer
, Berlin, Heidelberg, pp.
269
277
.10.1007/978-3-642-21381-6_26
20.
Ferreira
,
J. C.
,
Monteiro
,
V. D. F.
, and
Afonso
,
J. L.
,
2012
, “
Data Mining Approach for Range Prediction of Electric Vehicle
,” from https://repositorium.sdum.uminho.pt
21.
De Nunzio
,
G.
, and
Thibault
,
L.
,
2017
, “
Energy-Optimal Driving Range Prediction for Electric Vehicles
,” IEEE Intelligent Vehicles Symposium (
IV
), IEEE, Los Angeles, CA, pp.
1608
1613
.10.1109/IVS.2017.7995939
22.
Vaz
,
W.
,
Nandi
,
A. K. R.
,
Landers
,
R. G.
, and
Koylu
,
U. O.
,
2015
, “
Electric Vehicle Range Prediction for Constant Speed Trip Using Multi-Objective Optimization
,”
Journal of Power Sources
, 275, pp.
435
446
.10.1016/j.jpowsour.2014.11.043
23.
Fukushima
,
A.
,
Yano
,
T.
,
Imahara
,
S.
,
Aisu
,
H.
,
Shimokawa
,
Y.
, and
Shibata
,
Y.
,
2018
, “
Prediction of Energy Consumption for New Electric Vehicle Models by Machine Learning
,”
IET Digital Libr.
,
12
(
9
), pp.
1174
1180
.10.1049/iet-its.2018.5169
24.
Gebhard
,
L.
,
Golab
,
L.
,
Keshav
,
S.
, and
de Meer
,
H.
,
2016
, “
Range Prediction for Electric Bicycles
,” Proceedings of the Seventh International Conference on Future Energy Systems, e-Energy '16, Association for Computing Machinery (
ACM
), Waterloo, ON, Canada, pp.
1
11
.10.1145/2934328.2934349
25.
Fechtner
,
H.
,
Teschner
,
T.
, and
Schmuelling
,
B.
,
2015
, “
Range Prediction for Electric Vehicles: Real-Time Payload Detection by Tire Pressure Monitoring
,”
IEEE Intelligent Vehicles Symposium (IV)
, IEEE, Seoul, South Korea, pp.
767
772
.10.1109/IVS.2015.7225777
26.
Zhao
,
L.
,
Yao
,
W.
,
Wang
,
Y.
, and
Hu
,
J.
,
2020
, “
Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles
,”
IEEE Access
, vol. 8, pp.
212423
212441
.10.1109/access.2020.3039815
27.
Wang
,
Z.
,
Wang
,
X.-H.
,
Wang
,
L.-Z.
,
Hu
,
X.-F.
, and
Fan
,
W.-H.
,
2017
, “
Research on Electric Vehicle (EV) Driving Range Prediction Method Based on PSO-LSSVM
,” IEEE International Conference on Prognostics and Health Management (
ICPHM
), IEEE, Dallas, TX, pp.
260
265
.10.1109/ICPHM.2017.7998338
28.
Barredo Arrieta
,
A.
,
Díaz-Rodríguez
,
N.
,
Del Ser
,
J.
,
Bennetot
,
A.
,
Tabik
,
S.
,
Barbado
,
A.
,
Garcia
,
S.
,
Gil-Lopez
,
S.
,
Molina
,
D.
,
Benjamins
,
R.
,
Chatila
,
R.
, and
Herrera
,
F.
,
2020
, “
Explainable Artificial Intelligence (Xai): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI
,”
Inf. Fusion
,
58
, pp.
82
115
.10.1016/j.inffus.2019.12.012
29.
Topić
,
J.
,
Škugor
,
B.
, and
Deur
,
J.
,
2019
, “
Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range
,”
Energies
, 12(7), p.
1396
.10.3390/en12071396
30.
Modi
,
S.
,
Bhattacharya
,
J.
, and
Basak
,
P.
,
2021
, “
Convolutional Neural Network-Bagged Decision Tree: A Hybrid Approach to Reduce Electric Vehicle's Driver's Range Anxiety by Estimating Energy Consumption in Real-Time
,”
Soft Comput.
, 25(3), pp.
2399
2416
.10.1007/s00500-020-05310-y
31.
Oliva
,
J. A.
,
Weihrauch
,
C.
, and
Bertram
,
T.
,
2013
, “
Model-Based Remaining Driving Range Prediction in Electric Vehicles by Using Particle Filtering and Markov Chains
,” World Electric Vehicle Symposium and Exhibition (
EVS27
), IEEE, Barcelona, Spain, pp.
1
10
.10.1109/EVS.2013.6914989
32.
Nix
,
D.
, and
Weigend
,
A.
,
1994
, “
Estimating the Mean and Variance of the Target Probability Distribution
,” Proceedings of IEEE International Conference on Neural Networks (
ICNN'94
), vol. 1,
IEEE,
Orlando, FL, pp.
55
60
.10.1109/ICNN.1994.374138
33.
Galván
,
I. M.
,
Valls
,
J. M.
,
Cervantes
,
A.
, and
Aler
,
R.
,
2017
, “
Multi-Objective Evolutionary Optimization of Prediction Intervals for Solar Energy Forecasting With Neural Networks
,”
Inf. Sci.
,
418–419
, pp.
363
382
.10.1016/j.ins.2017.08.039
34.
Wan
,
C.
,
Xu
,
Z.
,
Pinson
,
P.
,
Dong
,
Z. Y.
, and
Wong
,
K. P.
,
2014
, “
Optimal Prediction Intervals of Wind Power Generation
,”
IEEE Trans. Power Syst.
,
29
(
3
), pp.
1166
1174
.10.1109/TPWRS.2013.2288100
35.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-Term Memory
,”
Neural Comput.
,
9
(
8
), pp.
1735
1780
.10.1162/neco.1997.9.8.1735
36.
Pascanu
,
R.
,
Mikolov
,
T.
, and
Bengio
,
Y.
,
2013
, “
On the Difficulty of Training Recurrent Neural Networks
,” arXiv:1211.5063 [cs].
37.
Pascanu
,
R.
,
Gulcehre
,
C.
,
Cho
,
K.
, and
Bengio
,
Y.
,
2014
, “
How to Construct Deep Recurrent Neural Networks
,” arXiv:1312.6026 [cs, stat].
38.
Graves
,
A.
,
2014
, “
Generating Sequences With Recurrent Neural Networks
,” arXiv:1308.0850 [cs].
39.
Graves
,
A.
,
Rahman Mohamed
,
A.
, and
Hinton
,
G.
,
2013
, “
Speech Recognition With Deep Recurrent Neural Networks
,”
IEEE International Conference on Acoustics, Speech and Signal Processing
,
IEEE
, Vancouver, BC, Canada, pp.
6645
6649
.10.1109/icassp.2013.6638947
40.
Saleh
,
K.
,
Hossny
,
M.
, and
Nahavandi
,
S.
,
2017
, “
Intent Prediction of Vulnerable Road Users From Motion Trajectories Using Stacked LSTM Network
,” IEEE 20th International Conference on Intelligent Transportation Systems (
ITSC
),
IEEE
, Yokohama, Japan, pp.
327
332
.10.1109/itsc.2017.8317941
41.
Yu
,
L.
,
Qu
,
J.
,
Gao
,
F.
, and
Tian
,
Y.
,
2019
, “
A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM
,”
Shock Vib.
,
2019
, pp.
1
10
.10.1109/ITSC.2017.8317941
42.
Wang
,
P.
,
2019
, “
Sample Delivery Trip Data of an Extended Range Electric Vehicle
,” Data Repository for the University of Minnesota (DRUM), accessed Jan. 5, 2022. 10.13020/V5P7-MZ54
43.
Wang
,
P.
,
Li
,
Y.
,
Shekhar
,
S.
, and
Northrop
,
W. F.
,
2020
, “
A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles
,” arXiv:2006.00795 [cs, eess].
You do not currently have access to this content.