Vehicle-to-grid for car sharing -- A simulation study for 2030

• URL: http://arxiv.org/abs/2311.07349v2
• Date: Fri, 12 Jul 2024 19:55:01 GMT
• Title: Vehicle-to-grid for car sharing -- A simulation study for 2030
• Abstract summary: Car sharing systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing.
• Score: 4.142286700293885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies - a facet that has received limited attention in previous research. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. We propose an agent-based simulation pipeline that considers population changes as well as different business strategies of the car sharing service, and we demonstrate its successful application for simulating scenarios for 2030. To imitate car sharing user behavior, we develop a data-driven mode choice model. Our analysis reveals important differences in the examined scenarios, such as higher vehicle utilization rates for a reduced fleet size as well as in a scenario featuring new car sharing stations. These disparities translate into variations in the power flexibility of the fleet available for ancillary services, ranging from 12 to 50 MW, depending on the scenario and the time of the day. Furthermore, we conduct a case study involving a subset of the car sharing fleet, incorporating real-world electricity pricing data. The case study substantiates the existence of a sweet spot involving monetary gains for both power grid operators and fleet owners. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing.

Related papers

• VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
  VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
  arXiv  Detail & Related papers  (2026-09-02T11:32:52Z)
• Car Dependency in Urban Accessibility [0.0]
  Car-centric urban growth has transformed the automobile into a necessity for accessing essential services, creating significant "car dependency"<n>This study introduces a novel Car Dependency Index (CDI) that quantifies the accessibility gap between private and public transport across 18 cities in Europe and North America.
  arXiv  Detail & Related papers  (2026-04-01T15:19:44Z)
• CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine [73.74077186298523]
  CoReVLA is a continual learning framework for autonomous driving.<n>It improves the performance in long-tail scenarios through a dual-stage process of data Collection and behavior Refinement.<n>CoReVLA achieves a Driving Score (DS) of 72.18 and a Success Rate (SR) of 50%, outperforming state-of-the-art methods by 7.96 DS and 15% SR under long-tail, safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-09-19T13:25:56Z)
• Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantees via Constrained Mean-Field Reinforcement Learning [42.070187224580344]
  Rapid expansion of services such as Uber, Lyft and Didi Chuxing has reshaped urban transportation by offering flexible, on-demand mobility via mobile applications. Inadequate rebalancing results in prolonged rider waiting times, inefficient vehicle utilization, and inequitable distribution services. We introduce continuous-state mean-field control (MFC) and reinforcement learning (MFRL) models that explicitly represent each vehicle's precise location and employ continuous repositioning actions guided by the distribution of other vehicles.
  arXiv  Detail & Related papers  (2025-03-31T15:00:11Z)
• Federated Data-Driven Kalman Filtering for State Estimation [49.40531019551957]
  This paper proposes a novel localization framework based on collaborative training or federated learning paradigm. We build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering. FedKalmanNet is used by each vehicle to localize itself, by estimating the associated system uncertainty matrices.
  arXiv  Detail & Related papers  (2024-11-06T16:18:33Z)
• Shared learning of powertrain control policies for vehicle fleets [3.9325957466009203]
  Deep reinforcement learning (DRL) aims at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. We present a novel framework for shared learning among a fleet of vehicles through the use of a distilled group policy. We find a fleet average improvement of 8.5 percent in fuel economy compared to the baseline.
  arXiv  Detail & Related papers  (2024-04-27T13:01:05Z)
• Shared lightweight autonomous vehicles for urban food deliveries: A simulation study [1.5990479272083913]
  We develop an agent-based model and an environmental impact study assessing the fleet performance of lightweight autonomous food delivery vehicles. Results indicate that there could be environmental benefits in replacing traditional car-based deliveries with shared lightweight autonomous vehicle fleets.
  arXiv  Detail & Related papers  (2024-02-29T15:06:46Z)
• A V2X-based Privacy Preserving Federated Measuring and Learning System [0.0]
  We propose a federated measurement and learning system that provides real-time data to fellow vehicles over Vehicle-to-Vehicle (V2V) communication. We also operate a federated learning scheme over the Vehicle-to-Network (V2N) link to create a predictive model of the transportation network. Results indicate that the proposed FL scheme improves learning performance and prevents eavesdropping at the aggregator server side.
  arXiv  Detail & Related papers  (2024-01-24T23:11:11Z)
• Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
  Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
  arXiv  Detail & Related papers  (2023-04-23T16:01:36Z)
• Spatially-aware station based car-sharing demand prediction [2.0840675600355127]
  We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms.<n>We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance.<n>An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role.
  arXiv  Detail & Related papers  (2023-03-25T10:10:11Z)
• 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)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• 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)
• Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities [0.6875312133832077]
  We use web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss which socio-demographic and urban activity indicators are associated with variations in car sharing demand.
  arXiv  Detail & Related papers  (2021-09-20T20:37:25Z)
• Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network [1.599072005190786]
  We formulate the problem of passenger-vehicle matching in a sparsely connected graph. We propose an algorithm to derive an equilibrium policy in a multi-agent environment.
  arXiv  Detail & Related papers  (2021-02-13T03:18:44Z)
• Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning [101.64598586454571]
  A novel framework is proposed to allow reinforcement learning-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) A federated RL approach is introduced in order to speed up the training process across vehicles. Results show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.
  arXiv  Detail & Related papers  (2020-12-07T02:09:15Z)

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.