Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

• URL: http://arxiv.org/abs/2210.12693v1
• Date: Sun, 23 Oct 2022 10:52:51 GMT
• Title: Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
• Authors: Chengyin Li, Zheng Dong, Nathan Fisher, and Dongxiao Zhu
• Abstract summary: Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Here we propose a novel Regularized Actor-temporal (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference and the external reward. Experimental results on two real-world datasets demonstrate unique features and superior performance of our approach to the competing methods.
• Score: 9.009978844120514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.

Related papers

• Learning and Optimization for Price-based Demand Response of Electric Vehicle Charging [0.9124662097191375]
  We propose a new decision-focused end-to-end framework for PBDR modeling. We evaluate the effectiveness of our method on a simulation of charging station operation with synthetic PBDR patterns of EV customers.
  arXiv  Detail & Related papers  (2024-04-16T06:39:30Z)
• Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization [0.0]
  The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies. This paper proposes an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid.
  arXiv  Detail & Related papers  (2023-11-24T15:50:37Z)
• 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)
• Optimizing the Placement of Roadside LiDARs for Autonomous Driving [61.584278382844595]
  How to optimize the placement of roadside LiDARs is a crucial but often overlooked problem. This paper proposes an approach to optimize the placement of roadside LiDARs by selecting optimized positions within the scene. A dataset named Roadside-Opt is created using the CARLA simulator to facilitate research on the roadside LiDAR placement problem.
  arXiv  Detail & Related papers  (2023-10-11T07:24:27Z)
• Vague Preference Policy Learning for Conversational Recommendation [48.868921530958666]
  Conversational recommendation systems commonly assume users have clear preferences, leading to potential over-filtering. We introduce the Vague Preference Multi-round Conversational Recommendation (VPMCR) scenario, employing a soft estimation mechanism to accommodate users' vague and dynamic preferences. Our work advances CRS by accommodating users' inherent ambiguity and relative decision-making processes, improving real-world applicability.
  arXiv  Detail & Related papers  (2023-06-07T14:57:21Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control [42.17503767317918]
  This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments. A horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments. Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various factors.
  arXiv  Detail & Related papers  (2022-10-04T08:22:46Z)
• A Multi-Objective approach to the Electric Vehicle Routing Problem [0.0]
  The electric vehicle routing problem (EVRP) has garnered great interest from researchers and industrialists in an attempt to move from fuel-based vehicles to healthier and more efficient electric vehicles (EVs) Previous works target logistics and delivery-related solutions wherein a homogeneous fleet of commercial EVs have to return to the initial point after making multiple stops. We perform multi-objective optimization - minimizing the total trip time and the cumulative cost of charging.
  arXiv  Detail & Related papers  (2022-08-26T05:09:59Z)
• 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)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization [2.580765958706854]
  This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems. We present an optimal service system including the design of SAEV vehicles and charging stations.
  arXiv  Detail & Related papers  (2020-10-16T05:06:58Z)

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.