eFlx: Energy Flexibility Provisioning for E-taxi Fleets

An e-taxi fleet consumes a significant amount of energy daily, making it a substantial electricity consumer. Unlike traditional consumers, such as factories and buildings, a fleet coordinates charging activities across both times and locations, offering considerable flexibility in its energy demand. This allows a fleet to achieve substantial reductions in energy consumption in response to demand response requests while maintaining transportation service quality. To better understand and control this intrinsic energy flexibility, we propose the eFlx framework for managing e-taxi fleets for demand response. In the eFlx framework, we establish a model to characterize the energy flexibility upon receiving a real-time demand response request. We then investigate the energy flexibility provisioning problem, formulated as a bi-level optimal control problem, which aims to optimize and maintain the energy flexibility of the fleet for potential demand response requests that could arise at any time. To achieve real-time flexibility provisioning, we develop an efficient iterative algorithm to solve this problem. Data-driven evaluations with NYC datasets demonstrate that eFlx achieves a 19. 98% greater reduction in energy demand compared to existing solutions, without requiring extra charging or compromising the quality of taxi service.

Authors

Institutions

Publication Details

Published
2025-05-06
DOI
https://doi.org/10.1145/3716550.3722026
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

eFlx: Energy Flexibility Provisioning for E-taxi Fleets

Shan Lin, Cui-xia Xu, Liangkai Zhou, Yukun Yuan et al.
Transportation and Mobility Innovations
article

eFlx: Energy Flexibility Provisioning for E-taxi Fleets

Shan Lin, Cui-xia Xu, Liangkai Zhou, Yukun Yuan, Yue Zhao
article en

Abstract

An e-taxi fleet consumes a significant amount of energy daily, making it a substantial electricity consumer. Unlike traditional consumers, such as factories and buildings, a fleet coordinates charging activities across both times and locations, offering considerable flexibility in its energy demand. This allows a fleet to achieve substantial reductions in energy consumption in response to demand response requests while maintaining transportation service quality. To better understand and control this intrinsic energy flexibility, we propose the eFlx framework for managing e-taxi fleets for demand response. In the eFlx framework, we establish a model to characterize the energy flexibility upon receiving a real-time demand response request. We then investigate the energy flexibility provisioning problem, formulated as a bi-level optimal control problem, which aims to optimize and maintain the energy flexibility of the fleet for potential demand response requests that could arise at any time. To achieve real-time flexibility provisioning, we develop an efficient iterative algorithm to solve this problem. Data-driven evaluations with NYC datasets demonstrate that eFlx achieves a 19. 98% greater reduction in energy demand compared to existing solutions, without requiring extra charging or compromising the quality of taxi service.

University of Tennessee at Chattanooga (US), Stony Brook University (US)
National Science Foundation, University of Tennessee at Chattanooga, Division of Electrical, Communications and Cyber Systems
Affordable and clean energy
Openalex Percentile: Top 91%
Transportation and Mobility Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

eFlx: Energy Flexibility Provisioning for E-taxi Fleets — Shan Lin, Cui-xia Xu, et al. · (2025) | TGRS Research Map | TGRS