Electric Vehicle Usage and Charging Analysis Dataset Across Seven Major Cities in China
Creators
- 1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China
- 2. Department of Space, Earth and Environment, Division of Physical Resource Theory, Chalmers University of Technology, Gothenburg, Sweden
- 3. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
Description
Background
This dataset provides supporting data for the figures presented in our study on electric vehicle (EV) usage and charging behavior across major Chinese cities. The detailed analysis and raw data are thoroughly described in Zhan et al (2025). The study examines 1.69 million EVs, representing 42% of China's total EV fleet, from November 2020 to October 2021. The study provides insights into operational demands, infrastructure requirements, and energy consumption patterns by analyzing diverse vehicle types—including private cars, taxis, buses, and special purpose vehicles (SPVs).
The purpose of this dataset is to enable researchers who do not have access to the same raw data to replicate, calibrate, or extend our findings using the processed data that underpins each figure. This resource is valuable for further research on EV infrastructure planning, energy consumption, and vehicle performance. This dataset is made available to help the research community leverage our findings and facilitate advancements in electric vehicle research and infrastructure planning. Please refer to Zhan et al (2025) for full details on the methodology and analysis.
Data description
This dataset includes the processed data underlying each figure in Zhan et al (2025), covering various aspects of EV usage, battery capacity, and charging behavior across seven major Chinese cities: Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing. The dataset is organized to correspond directly with the figures in the paper, facilitating its use for further analysis and model calibration. Each dataset is aligned with specific figures, providing essential data to help researchers without access to the original raw data.
1. EV Type and Battery Energy Distribution Across Cities
Fig1a.Distribution of EV types across selected Chinese cities
File: Fig1a.Distribution of EV types across selected Chinese cities.csv
Description: Distribution of EV types across seven cities, detailing the share of different vehicle types.
|
Column |
Description |
Data type |
Unit |
|
Beijing |
Distribution of EV types in Beijing |
Float |
% |
|
Shenzhen |
Distribution of EV types in Shenzhen |
Float |
% |
|
Shanghai |
Distribution of EV types in Shanghai |
Float |
% |
|
Guangzhou |
Distribution of EV types in Guangzhou |
Float |
% |
|
Chengdu |
Distribution of EV types in Chengdu |
Float |
% |
|
Chongqing |
Distribution of EV types in Chongqing |
Float |
% |
|
Nanjing |
Distribution of EV types in Nanjing |
Float |
% |
Fig1b.Distribution of battery energy by vehicle types
File: Fig1b.Distribution of battery energy by vehicle types.csv
Description: Distribution of battery energy across different vehicle types, represented as box plot statistics.
|
Column |
Description |
Data type |
Unit |
|
type_2 |
vehicle types |
String |
- |
|
Lower Whisker |
The battery energy corresponding to the Lower Whisker of the box plot. |
Float |
kWh |
|
Q1 (25%) |
The 25th percentile value of battery energy. |
Float |
kWh |
|
Median (50%) |
The median value of battery energy. |
Float |
kWh |
|
Q3 (75%) |
The 75th percentile value of battery energy. |
Float |
kWh |
|
Upper Whisker |
The battery energy corresponding to the Upper Whisker of the box plot. |
Float |
kWh |
2. Variations in Battery Energy
Fig1c.Variations of battery energy of buses
File: Fig1c.Variations of battery energy of buses across studied cities.csv
Description: Battery energy variations for buses across the studied cities.
|
Column |
Description |
Data type |
Unit |
|
city_En |
English name of 7 Chinese city |
String |
- |
|
Lower Whisker |
The battery energy of buses corresponding to the Lower Whisker of the box plot. |
Float |
kWh |
|
Q1 (25%) |
The 25th percentile value of battery energy of buses. |
Float |
kWh |
|
Median (50%) |
The median value of battery energy of buses. |
Float |
kWh |
|
Q3 (75%) |
The 75th percentile value of battery energy of buses. |
Float |
kWh |
|
Upper Whisker |
The battery energy of buses corresponding to the Upper Whisker of the box plot. |
Float |
kWh |
Fig1d.Variations of battery energy of SPVs
File: Fig1c.Variations of battery energy of SPVs across studied cities.csv
Description: Battery energy variations for special purpose vehicles (SPVs) across cities.
|
Column |
Description |
Data type |
Unit |
|
city_En |
English name of 7 Chinese city |
String |
- |
|
Lower Whisker |
The battery energy of SPVs corresponding to the Lower Whisker of the box plot. |
Float |
kWh |
|
Q1 (25%) |
The 25th percentile value of battery energy of SPVs. |
Float |
kWh |
|
Median (50%) |
The median value of battery energy of SPVs. |
Float |
kWh |
|
Q3 (75%) |
The 75th percentile value of battery energy of SPVs. |
Float |
kWh |
|
Upper Whisker |
The battery energy of SPVs corresponding to the Upper Whisker of the box plot. |
Float |
kWh |
3. Daily Driving Distance and Energy Consumption
Fig1e.Daily driving distance of different vehicle types
File: Fig1e.Daily driving distance of different vehicle types.csv
Description: Cumulative distribution functions (CDFs) of daily driving distances for various vehicle types.
|
Column |
Description |
Data type |
Unit |
|
CDF Percentile |
CDF Percentile
|
Integer |
% |
|
Private car |
The value of private car daily driving distance corresponding to CDF Percentile |
Float |
km |
|
Official car |
The value of official car daily driving distance corresponding to CDF Percentile |
Float |
km |
|
SPV |
The value of SPV daily driving distance corresponding to CDF Percentile |
Float |
km |
|
Rental car |
The value of rental car daily driving distance corresponding to CDF Percentile |
Float |
km |
|
Bus |
The value of bus daily driving distance corresponding to CDF Percentile |
Float |
km |
|
Taxi |
The value of taxi daily driving distance corresponding to CDF Percentile |
Float |
km |
Fig1f-1. Ratio of daily energy consumed over battery energy
File: Fig1f-1.The ratio of daily energy consumed over battery energy.csv
Description: Ratio of daily energy consumption relative to battery energy for each vehicle type.
|
Column |
Description |
Data type |
Unit |
|
type_2 |
vehicle types |
String |
- |
|
Lower Whisker |
The energy ratio corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of energy ratio. |
Float |
- |
|
Median (50%) |
The median value of energy ratio. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of energy ratio. |
Float |
- |
|
Upper Whisker |
The energy ratio corresponding to the Upper Whisker of the box plot. |
Float |
- |
Fig1f-2. Number of charging events per day
File: Fig1f-2.The number of charging events per day.csv
Description: Data on the number of daily charging events across vehicle types.
|
Column |
Description |
Data type |
Unit |
|
type_2 |
vehicle types |
String |
- |
|
Lower Whisker |
The charging events per day corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of charging events per day. |
Float |
- |
|
Median (50%) |
The median value of charging events per day. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of charging events per day. |
Float |
- |
|
Upper Whisker |
The charging events per day corresponding to the Upper Whisker of the box plot. |
Float |
- |
4. EV Usage Patterns and State of Charge (SOC)
Fig2a.Daily usage patterns of EVs
File: Fig2a.Daily usage patterns of EVs across different vehicle types and days.csv
Description: Usage patterns of EVs by type and day, segmented into 15-minute intervals.
|
Column |
Description |
Data type |
Unit |
|
Vehicle type_day type_state |
Take Private car_workday_driving as an example, it refers to the ratio of private cars parked to the total number of private cars on weekdays within a 15-minute period |
Float |
- |
Fig2b. SOC levels before and after charging
File: Fig2b. SOC levels before and after charging by charging level by vehicle type.csv
Description: SOC levels before and after charging events, classified by charging level and vehicle type.
|
Column |
Description |
Data type |
Unit |
|
vehicle_SOC_P |
Take Private car_Start SOC_P1 as an example, it refers to SOC of private cars charging with P1 at the start of charging |
String |
- |
|
Lower Whisker |
The SOC corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of SOC. |
Float |
- |
|
Median (50%) |
The median value of SOC. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of SOC. |
Float |
- |
|
Upper Whisker |
The SOC corresponding to the Upper Whisker of the box plot. |
Float |
- |
5. Energy Consumption Rate (ECR) of Passenger Cars
Fig2c-top. ECR of passenger cars by month of the year
File: Fig2c-top.Energy consumption rate (ECR) of passenger cars by month of the year.csv
Description: Monthly ECR of passenger cars in different cities.
|
Column |
Description |
Data type |
Unit |
|
Beijing |
ECR of passenger cars by month in Beijing |
Float |
kWh/100km |
|
Shenzhen |
ECR of passenger cars by month in Shenzhen |
Float |
kWh/100km |
|
Shanghai |
ECR of passenger cars by month in Shanghai |
Float |
kWh/100km |
|
Guangzhou |
ECR of passenger cars by month in Guangzhou |
Float |
kWh/100km |
|
Chengdu |
ECR of passenger cars by month in Chengdu |
Float |
kWh/100km |
|
Chongqing |
ECR of passenger cars by month in Chongqing |
Float |
kWh/100km |
|
Nanjing |
ECR of passenger cars by month in Nanjing |
Float |
kWh/100km |
Fig2c-bottom.ECR of passenger cars as a function of temperature
File: Fig2c-bottom.ECR of passenger cars as a function of temperature.csv
Description: Passenger vehicle ECR in relation to temperature across different cities.
|
Column |
Description |
Data type |
Unit |
|
Temperature |
Temperature of a city in a certain month |
Float |
℃ |
|
ECR |
Average energy consumption rate of passenger cars of a city in a certain month |
Float |
kWh/100km |
6. Charging Events and Load Distribution
Fig3-1.Number of vehicles being charged by level by time of day
File: Fig3-1.Number of vehicles being charged by level by time of day.csv
Description: Number of vehicles charging at different power levels throughout the day.
|
Column |
Description |
Data type |
Unit |
|
Vehicle type_P_day type |
Take Private car_P1_workday as an example, it refers to number of private cars being charged with P1 on weekdays within a 5-minute period |
Integer |
- |
Fig3-2.Daily charging load from electric vehicles
File: Fig3-2.Daily charging load from electric vehicles across different vehicle types and power level.csv
Description: Charging load data across vehicle types and power levels, aggregated by time of day.
|
Column |
Description |
Data type |
Unit |
|
Vehicle type_P_day type |
Take Private car_P1_workday as an example, it refers to charging load of private cars being charged with P1 on weekdays within a 5-minute period |
Float |
- |
7. Spatial Distribution of Max Charging Power
Fig4a. Annual maximum charging power within each hexagonal grid across Beijing, 4c Distributions of the three clusters of temporal charging profiles in Beijing, and 4d Share of clusters by city.
FigS7-FigS12. Spatial distributions of charging power (kW): Max charging power and cluster distributions (City name).
File: max_power_cluster_cities.shp
Description: This dataset covers the maximum charging power distribution across seven Chinese cities, using H3 grids with Resolution 8 (~0.74 km²).
Cluster 0, 1, and 2 are defined based on the temporal profiles of charging power in the grids.
|
Column |
Description |
Data type |
Unit |
|
city |
Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Chengdu, and Chongqing |
String |
- |
|
hex_id |
Hexagon ID of H3 system with Resolution 8. |
String |
- |
|
cluster_id |
This indicates the cluster index of each hexagon. |
Integer |
- |
|
max_power |
Maximum charging power. |
Float |
kW |
|
geometry |
Hexagons in EPSG: 4326 – WGS 84. |
Polygon |
- |
8. Temporal Patterns of Charging Power
Fig 4b Three unique clusters of daily temporal patterns of charging power (all cities)
File: clusters_tempo.csv
Description: Temporal variations of charging power aggregated from all hexagons in each cluster.
|
Column |
Description |
Data type |
Unit |
|
cluster_id |
This indicates the cluster index of each hexagon. |
Integer |
- |
|
t |
Hourly index (0-23) |
Integer |
- |
|
q25 |
The 25th percentile value of charging power. |
Float |
kW |
|
q50 |
The median value of charging power. |
Float |
kW |
|
q75 |
The 75th percentile value of charging power. |
Float |
kW |
|
Type |
Weekday/Weekend. |
String |
- |
Supplementary Information:
S1. Accuracy and Quality of Data Collection: GPS Measurement Accuracy
FigSI1.Histogram of spatial errors in GPS Measurements
File: FigSI1.Histogram of spatial errors in GPS Measurements.csv
Description: Analysis of the accuracy of GPS data used in the study.
|
Column |
Description |
Data type |
Unit |
|
Interval |
The interval of spatial error |
Float |
m |
|
Height |
The height of each column in the histogram |
Float |
- |
S2. Charging Behavior Analysis
Empirical Distributions of Charger Power Delivered:
FigSI2-1.Distributions of charger power delivered to cars
File: FigSI2-1.Empirical distributions of charger power delivered to cars.csv
Description: Analysis of the distribution of charger power for passenger cars.
|
Column |
Description |
Data type |
Unit |
|
Interval |
The interval of charging power |
Float |
kW |
|
Height |
The height of each column in the histogram |
Float |
- |
FigSI2-2.Empirical distributions of charger power delivered to buses
File: FigSI2-2.Empirical distributions of charger power delivered to buses.csv
Description: Analysis of the distribution of charger power for buses.
|
Column |
Description |
Data type |
Unit |
|
Interval |
The interval of charging power |
Float |
kW |
|
Height |
The height of each column in the histogram |
Float |
- |
FigSI2-3.Empirical distributions of charger power delivered to SPVs
File: FigSI2-3.Empirical distributions of charger power delivered to SPVs.csv
Description: Analysis of the distribution of charger power for special purpose vehicles (SPVs).
|
Column |
Description |
Data type |
Unit |
|
Interval |
The interval of charging power |
Float |
kW |
|
Height |
The height of each column in the histogram |
Float |
- |
Charging Power Preferences:
FigSI3.Distribution of charging power level preferences among different EV types
File: FigSI3.Distribution of charging power level preferences among different EV types.csv
Description: Analysis of charging power level preferences for different EV types.
|
Column |
Description |
Data type |
Unit |
|
P1 & P2 & P3 |
The ratio of each EV type's number of P1 & P2 & P3 chargers to the total number of that EV type |
Float |
% |
|
P2 & P3 |
The ratio of each EV type's number of P2 & P3 chargers to the total number of that EV type |
Float |
% |
|
P1 & P3 |
The ratio of each EV type's number of P1 & P3 chargers to the total number of that EV type |
Float |
% |
|
P1 & P2 |
The ratio of each EV type's number of P1 & P2 chargers to the total number of that EV type |
Float |
% |
|
P3 |
The ratio of each EV type's number of P3 chargers to the total number of that EV type |
Float |
% |
|
P2 |
The ratio of each EV type's number of P2 chargers to the total number of that EV type |
Float |
% |
|
P1 |
The ratio of each EV type's number of P1 chargers to the total number of that EV type |
Float |
% |
Charging Event Durations
FigSI4.Average duration (hr) of charging events by type of charging energy for different vehicle types
File: Average duration (hr) of charging events by type of charging energy for different vehicle types.csv
Description: Analysis of the average duration of charging events categorized by energy type.
|
Column |
Description |
Data type |
Unit |
|
vehicle type_charging duration_P |
Take Private car_charging duration_P1 as an example, it refers to charging duration of private cars charging with P1 |
String |
- |
|
Lower Whisker |
The charging duration corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of charging duration. |
Float |
- |
|
Median (50%) |
The median value of charging duration. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of charging duration. |
Float |
- |
|
Upper Whisker |
The charging duration corresponding to the Upper Whisker of the box plot. |
Float |
- |
Vehicle Usage Patterns and Energy Metrics
FigSI5.Distributions of average daily driving distance by vehicle type
File: FigSI5.Distributions of average daily driving distance by vehicle type.csv
Description: Distribution analysis of daily driving distances across different vehicle types and cities.
|
Column |
Description |
Data type |
Unit |
|
city_vehicle type |
Take Beijing_Private car as an example, it refers to average daily driving distance of private cars in Beijing |
String |
- |
|
Lower Whisker |
The average daily driving distance corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of average daily driving distance. |
Float |
- |
|
Median (50%) |
The median value of average daily driving distance. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of average daily driving distance. |
Float |
- |
|
Upper Whisker |
The average daily driving distance corresponding to the Upper Whisker of the box plot. |
Float |
- |
Battery Energy Distribution:
FigSI6.Distributions of nominal battery energy by vehicle type
File: FigSI6.Distributions of nominal battery energy by vehicle type.csv
Description: Analysis of nominal battery energy distributions across vehicle types and cities.
|
Column |
Description |
Data type |
Unit |
|
city_vehicle type |
Take Beijing_Private car as an example, it refers to nominal battery energy of private cars in Beijing |
String |
- |
|
Lower Whisker |
The nominal battery energy corresponding to the Lower Whisker of the box plot. |
Float |
- |
|
Q1 (25%) |
The 25th percentile value of nominal battery energy. |
Float |
- |
|
Median (50%) |
The median value of nominal battery energy. |
Float |
- |
|
Q3 (75%) |
The 75th percentile value of nominal battery energy. |
Float |
- |
|
Upper Whisker |
The nominal battery energy corresponding to the Upper Whisker of the box plot. |
Float |
- |
Files
clusters_tempo.csv
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Fig3-2.Daily charging load from electric vehicles across different vehicle types and power level.csv
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Additional details
Related works
- Is documented by
- Preprint: 10.21203/rs.3.rs-4693997/v1 (DOI)
Funding
- China Scholarship Council
- China Scholarship Council 202306030157