eBaaS: AIoT-enabled eBike battery-swap as a service for last-mile delivery

Publication Type

Conference Proceeding Article

Publication Date

5-2025

Abstract

In China, the number of riders in the on-demand delivery industry has surpassed ten million. Ensuring that these riders earn a decent income can enhance their financial security, reduce poverty, and promote social equity and stability. Due to ease of use, lower-cost maintenance and environmental friendliness, electric bicycles (e-bikes) are the primary mode of transportation for delivery riders. However, these riders frequently encounter depleted batteries due to limited capacity and prolonged charging times, necessitating inconvenient swaps or recharges during deliveries. To address this issue, we propose the e-bike Battery Swap-as-a-Service (eBaaS), an innovative battery-swapping system that leverages an intelligent AIoT network for seamless battery swapping at distributed locations across urban areas. eBaaS integrates edge-cloud collaboration, battery resource allocation, battery anomaly detection, and battery range prediction to minimize downtime and reduce unnecessary mileage. While eBaaS's potential benefits are evident, there has been a lack of robust methods to quantify its impact. Thus, we further developed the eBaaS Impact Evaluation Method (EIEM), the first comprehensive model to address this gap. EIEM analyzes data from approximately 260,000 delivery riders and 5 million riding trajectories. Findings indicate that eBaaS reduces average invalid mileage by 6 km and increases the order volume by an average of over 20% daily per e-bike rider. Meanwhile, the annual electricity savings result in a reduction of 2.74 million kilograms of carbon emissions for 260,000 riders. The eBaaS system is therefore significantly beneficial for environmental conservation and sustainable urban development.

Keywords

E-bike Battery-swapping System, Edge-cloud Collaboration, Battery Anomaly Detection, Battery Range Prediction

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

WWW '25: Proceedings of the ACM on Web Conference 2025, Sydney, Australia, April 28 - May 2

First Page

5045

Last Page

5053

Identifier

10.1145/3696410.3714503

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3696410.3714503

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