Strategic Charging Infrastructure Deployment for Electric Vehicles

Li, Meng; Jia, Yinghao; Shen, Zuojun; He, Fang · 2016 · ROSA P / California. Department of Transportation

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Summary

This study addresses the challenge of optimizing public charging infrastructure deployment to maximize the electrification rate of vehicle miles traveled (VMT) for electric vehicles. Motivated by the rapid adoption of electric vehicles in China and the need for data-driven policy guidance, the authors focus on a taxi fleet in Beijing. The research aims to determine optimal station locations, charger quantities, and types, while evaluating the impact of intelligent charging guidance systems and home charging availability. The methodology utilizes a large-scale dataset comprising real-time GPS trajectory data from 46,765 taxis in Beijing over a two-month period in 2014. The authors developed a time-series simulation model that tracks the state of charge (SOC) for each vehicle, assuming drivers maintain their existing travel patterns. The model defines a "charging opportunity" based on three criteria: a parking duration of at least 30 minutes, an SOC below a 0.2 threshold, and the availability of an unoccupied charger. To handle the computational complexity of simulating nearly 47,000 vehicles simultaneously, the authors employed an efficient "Tetris method" to identify charging demands. Station locations were determined using K-means clustering on the spatial distribution of charging time windows. The simulations varied parameters including the number of stations (50–1,000), charger types (fast vs. slow), and battery ranges (10–80 km). The results indicate that locating charging stations at clusters of charging time windows significantly outperforms uniform deployment, achieving a 54.3% electrification rate compared to 42.6% for uniform distribution. Deploying 500 public stations, each with 30 slow chargers, could electrify 170 million VMT in Beijing within two months, assuming an 80 km battery range and available home charging. Sensitivity analyses revealed that combining slow and fast chargers yields higher electrification rates than using only one type, with a mix of two fast and ten slow chargers performing optimally for larger battery ranges. Furthermore, distributing chargers across more, smaller stations increases the electrification rate up to a point of diminishing returns around 500 stations. The implementation of an intelligent charging guidance system, which directs drivers to stations with available chargers, increased the electrification rate by approximately 2.7%. Finally, the study found that promoting home charging is particularly effective when public infrastructure is limited or battery ranges are short. The significance of this work lies in its demonstration of a scalable, data-driven framework for infrastructure planning that accounts for charger availability and heterogeneous travel patterns. The findings provide actionable guidelines for policymakers, suggesting that strategic clustering of stations, a mixed charger portfolio, and intelligent guidance systems are critical for maximizing the environmental benefits of electric vehicle fleets. The approach is applicable to other cities and fleet types with similar trajectory data.

Key finding

Locating public charging stations at clustered charging time windows increased the electrification rate of vehicle miles traveled to 54.3%, compared to 42.6% for uniform deployment, while an intelligent charging guidance system provided an additional 2.7% increase.

Methodology

modeling

Sample size: 46765

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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