Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data

• URL: http://arxiv.org/abs/2604.20764v1
• Date: Wed, 22 Apr 2026 16:49:52 GMT
• Title: Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data
• Abstract summary: This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework.<n>It integrates map-based contextual features with driver-specific velocity prediction and physics-based energy consumption modeling.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual features with driver-specific velocity prediction and physics-based energy consumption modeling. The system combines route selection, detailed road feature processing, a rule-based reference velocity generator, a PID controller-based vehicle dynamics simulator, and a Bidirectional LSTM model trained to reproduce individual driving behavior. The predicted individual-specific velocity profiles are coupled with a quasi-steady backward energy consumption model to compute tractive power, regenerative braking, and State-of-Charge (SOC) evolution. Evaluation across urban, freeway, and hilly routes demonstrates that the proposed approach captures key driver behavioral patterns such as deceleration at intersections, speed-limit tracking, and road grade-dependent responses, while producing accurate power and SOC trajectories. The results highlight the effectiveness of combining learned driver behavior with map-based context and physics-based energy consumption modeling to produce accurate, personalized BEV SOC depletion profiles.

Related papers

• ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving [56.96697737229895]
  Reactive capability is a key property of data-driven behavior world model simulators for autonomous driving simulation systems.<n>We introduce ReactSim-Bench for evaluating the reactive capability of behavior world model simulation in autonomous driving.
  arXiv  Detail & Related papers  (2026-06-12T03:11:06Z)
• TrajDiff: End-to-end Autonomous Driving without Perception Annotation [65.49718343700319]
  End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs.<n>TrajDiff is a Trajectory-oriented BEV Conditioned Diffusion framework that establishes a perception annotation-free generative method for end-to-end autonomous driving.<n> evaluated on the NAVSIM benchmark, TrajDiff achieves 87.5 PDMS, establishing state-of-the-art performance among all annotation-free methods.
  arXiv  Detail & Related papers  (2025-11-30T04:34:20Z)
• A phase-aware AI car-following model for electric vehicles with adaptive cruise control: Development and validation using real-world data [7.228308959516853]
  Internal combustion engine (ICE) vehicles and electric vehicles (EVs) exhibit distinct vehicle dynamics.<n>Existing microscopic models effectively capture the driving behavior of ICE vehicles.<n>This study develops and validates a Phase-Aware AI (PAAI) car-following model specifically for EVs.
  arXiv  Detail & Related papers  (2025-09-30T22:27:03Z)
• MetaFollower: Adaptable Personalized Autonomous Car Following [63.90050686330677]
  We propose an adaptable personalized car-following framework - MetaFollower. We first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability.
  arXiv  Detail & Related papers  (2024-06-23T15:30:40Z)
• Planning with Adaptive World Models for Autonomous Driving [50.4439896514353]
  We present nuPlan, a real-world motion planning benchmark that captures multi-agent interactions.<n>We learn to model such unique behaviors with BehaviorNet, a graph convolutional neural network (GCNN)<n>We also 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)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation [0.0]
  Vehicle Energy Consumption estimation aims to predict the total energy required for a given trip before it starts. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. This paper proposes a preference-aware metaoptimization framework Meta-Pec for personalized vehicle energy consumption estimation.
  arXiv  Detail & Related papers  (2023-06-26T05:03:24Z)
• Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios [0.5161531917413706]
  In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. Deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. The proposed speed prediction models are evaluated for long-term predictions (up to 10 s) of target vehicle future velocities.
  arXiv  Detail & Related papers  (2022-11-30T22:50:43Z)
• A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles [0.0]
  This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level. We show that although all internal dynamics that affect energy consumption are masked, it is possible to learn aggregate-level energy consumption values quite accurately. This model has been deployed and integrated within POLARIS Transportation System Simulation Tool to support real-time behavioral transportation models.
  arXiv  Detail & Related papers  (2021-11-25T01:20:32Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• Analyzing the Travel and Charging Behavior of Electric Vehicles -- A Data-driven Approach [1.7403133838762446]
  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.
  arXiv  Detail & Related papers  (2021-06-11T15:53:59Z)

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.