Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles

Jihed Khiari; Cristina Olaverri-Monreal

Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles

Jihed Khiari
, Cristina Olaverri-Monreal

Department Intelligent Transport Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is \textit{range anxiety}, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

Original language	English
Title of host publication	IEEE Proceedings Intelligent Vehicles Symposium 2022
Number of pages	6
Publication status	Published - 2022

Fields of science

303 Health Sciences
303008 Ergonomics
201306 Traffic telematics
202031 Network engineering
202036 Sensor systems
202038 Telecommunications
202040 Transmission technology
203 Mechanical Engineering
211908 Energy research
211911 Sustainable technologies
102 Computer Sciences
102001 Artificial intelligence
102002 Augmented reality
102003 Image processing
102013 Human-computer interaction
102015 Information systems
102019 Machine learning
102021 Pervasive computing
102024 Usability research
102026 Virtual reality
102029 Practical computer science
102034 Cyber-physical systems
501026 Psychology of perception
501 Psychology
501025 Traffic psychology
201305 Traffic engineering
202 Electrical Engineering, Electronics, Information Engineering
202003 Automation
202030 Communication engineering
202034 Control engineering
202035 Robotics
202037 Signal processing
202041 Computer engineering
203004 Automotive technology
211902 Assistive technologies
211909 Energy technology
211917 Technology assessment
501030 Cognitive science

JKU Focus areas

Digital Transformation
Sustainable Development: Responsible Technologies and Management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{16217ece81f3447a8978647a9cb0ae63,

title = "Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles",

abstract = "The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is \textbackslash{}textit\{range anxiety\}, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.",

author = "Jihed Khiari and Cristina Olaverri-Monreal",

year = "2022",

language = "English",

booktitle = "IEEE Proceedings Intelligent Vehicles Symposium 2022",

}

TY - GEN

T1 - Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles

AU - Khiari, Jihed

AU - Olaverri-Monreal, Cristina

PY - 2022

Y1 - 2022

N2 - The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is \textit{range anxiety}, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

AB - The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is \textit{range anxiety}, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.

M3 - Conference proceedings

BT - IEEE Proceedings Intelligent Vehicles Symposium 2022

ER -