Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach

• URL: http://arxiv.org/abs/2106.06475v1
• Date: Fri, 11 Jun 2021 15:53:59 GMT
• Title: Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach
• Authors: Sina Baghali, Samiul Hasan, Zhaomiao Guo
• Abstract summary: Electric vehicles (EVs) may pose significant electricity demand on power systems. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips. We develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance.
• Score: 1.7403133838762446
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.

Related papers

• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  Motion planners (MPs) are crucial for safe navigation in complex urban environments. nuPlan, a recently released MP benchmark, addresses this limitation by augmenting real-world driving logs with closed-loop simulation logic. We present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on BehaviorNet's predictions.
  arXiv  Detail & Related papers  (2024-06-15T18:53:45Z)
• Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations [4.239428835958199]
  The rapid expansion of electric vehicles (EVs) has rendered the load forecasting of electric vehicle charging stations (EVCS) increasingly critical. The primary challenge in achieving precise load forecasting for EVCS lies in accounting for the nonlinear of charging behaviors, the spatial interactions among different stations, and the intricate temporal variations in usage patterns. We propose a Multiscale Spatio-Temporal Enhanced Model (MSTEM) for effective load forecasting at EVCS.
  arXiv  Detail & Related papers  (2024-05-29T12:54:22Z)
• Guiding Attention in End-to-End Driving Models [49.762868784033785]
  Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. We study how to guide the attention of these models to improve their driving quality by adding a loss term during training. In contrast to previous work, our method does not require these salient semantic maps to be available during testing time.
  arXiv  Detail & Related papers  (2024-04-30T23:18:51Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• 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)
• A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction [7.441576351434805]
  This paper proposes a novel approach that enables the integration of graph and temporal attention mechanisms for feature extraction. Evaluation results on a dataset of 18,013 EV charging piles in Shenzhen, China show that the proposed approach, named PAG, can achieve state-of-the-art forecasting performance.
  arXiv  Detail & Related papers  (2023-09-11T06:31:45Z)
• 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)
• Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge [0.0]
  The Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy. This challenge involved analysing a dataset of 91 charging stations across four geographical areas over seven months in 2020- 2021. The results highlight the potential of hierarchical forecasting approaches to accurately predict EV charging station occupancy.
  arXiv  Detail & Related papers  (2023-06-09T07:22:18Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach [1.160208922584163]
  Electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers.
  arXiv  Detail & Related papers  (2022-02-28T23:11:30Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)

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.