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

• URL: http://arxiv.org/abs/2204.12825v2
• Date: Mon, 17 Apr 2023 10:20:18 GMT
• Title: Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles
• Authors: Jihed Khiari, Cristina Olaverri-Monreal
• Abstract summary: 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. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.
• Score: 2.147325264113341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• QoS prediction in radio vehicular environments via prior user information [54.853542701389074]
  We evaluate ML tree-ensemble methods to predict in the range of minutes with data collected from a cellular test network. Specifically, we use the correlations of the measurements coming from the radio environment by including information of prior vehicles to enhance the prediction of the target vehicles.
  arXiv  Detail & Related papers  (2024-02-27T17:05:41Z)
• Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning [4.68055125124498]
  Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Our work investigates the potential of using Federated Learning (FL) methods to improve BEV energy consumption prediction while maintaining user privacy.
  arXiv  Detail & Related papers  (2023-12-12T15:40:38Z)
• Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
  "Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
  arXiv  Detail & Related papers  (2023-10-18T18:38:59Z)
• Data-Driven Probabilistic Energy Consumption Estimation for Battery Electric Vehicles with Model Uncertainty [1.0787390511207684]
  We propose a new driver behaviour-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo. Our approach achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy.
  arXiv  Detail & Related papers  (2023-07-02T04:30:20Z)
• Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks [2.147325264113341]
  Transportation sector accounts for about 25% of global greenhouse gas emissions. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. We propose an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty.
  arXiv  Detail & Related papers  (2023-04-14T11:51:26Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Overcoming limited battery data challenges: A coupled neural network approach [0.0]
  We propose a novel method of time-series battery data augmentation using deep neural networks. One model produces battery charging profiles, and another produces battery discharging profiles. Results show the efficacy of this approach to solve the challenges of limited battery data.
  arXiv  Detail & Related papers  (2021-10-05T16:17:19Z)
• Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs [3.448121798373834]
  We propose a methodology to predict an estimation of the parking duration in shared parking premises. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand.
  arXiv  Detail & Related papers  (2021-09-20T16:33:17Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.