A dataset of EV batery charging from Shenzhen Auto Electric Power Plant Co., Ltd (Autosun) and Hong Kong Polytechnic University.

A dataset of EV batery in Shenzehn, China.

This dataset covering the period between October 2020 and October 2023 was collected from 30 real public EV charging stations which are operated by Shenzhen Auto Electric Power Plant Co., Ltd (Autosun), a leading company in EV Mega-Watt charging technology solution with a 250 MW public EV charging network in China.

We at first do the preprocessing here including: (1) down-sampling of normal operating data; (2) dividing charging stations into 8 data owners; (3) dropping incomplete or abnormal data. After this, we provide 2 files. The first one (processed_data.xlsx) is the pre-processed data. The second (processed_data_longer_than_30.xlsx) is the data which only includes charging sequences longer than 30 minutes.

The second dataset, which will be used in our article, contains 21175 pieces of EV charging data with 1547432 sampling points, covering 10154 EVs that comprise various types of batteries. Each piece of charging data includes charging current, voltage and power at a 1 minute time interval. The battery fault data in this dataset can be categorized into two distinct types. The first type refers to faults that have been reported by engineering personnel, such as thermal runaway, unbalanced cell voltages, short-circuit and so on. The second type is characterized by excessively high temperatures that incur disconnection by the stations themselves.

The meaning of all columns is shown here:

id: unique id for every data point
transaction_id: unique id for every charging sequence
begin_time: begin time of a charging sequence
end_time: current sampling time
total_charging_kwh: charging energy for now
total_charging_min: charging time for now
current_soc: SOC for now
current_energy_meter_value: the energy of this battery at this time
chargingv: current charging voltage
charginga: current charging current
out_power: current charging power
charging_gun_temperature1: temperature of the charging gun
charging_gun_temperature2: temperature of the charging gun
types: Battery types, 03: LFP, 06: NMC, 04: LMO, 05: LCO, 07: LP
class_judge: data owner index
label: denote if it is a fault battery data, 1 means faulty, 0 means normal

中国深圳电动汽车电池数据集。

本数据集采集时段为2020年10月至2023年10月，数据来源于30座真实运营的公共电动汽车充电站，这些充电站由深圳奥特迅电力设备股份有限公司（Autosun）运营——该公司是国内电动汽车兆瓦级充电技术解决方案的领军企业，拥有规模达250兆瓦的公共电动汽车充电网络。

本数据集首先开展了如下预处理工作：(1) 对正常运行数据进行下采样；(2) 将所有充电站划分为8个数据持有者；(3) 剔除不完整或异常数据。预处理完成后，我们提供两份数据文件：第一份为processed_data.xlsx，即预处理后的完整数据集；第二份为processed_data_longer_than_30.xlsx，仅包含时长超过30分钟的充电序列数据。

本研究将使用的第二份数据集包含21175条电动汽车充电数据，共计1547432个采样点，覆盖10154辆搭载不同类型电池的电动汽车。每条充电数据以1分钟为采样间隔，记录了充电电流、电压与功率信息。

本数据集中的电池故障数据可分为两类截然不同的类型：第一类为工程人员上报的故障，例如热失控、电芯电压失衡、短路等；第二类则因电池温度过高导致充电站主动切断充电而产生。

各字段含义如下：
id：每个数据点的唯一标识符
transaction_id：每条充电序列的唯一标识符
begin_time：充电序列的开始时间
end_time：当前采样时刻
total_charging_kwh：累计充电电量（单位：千瓦时）
total_charging_min：累计充电时长（单位：分钟）
current_soc：当前荷电状态（SOC）
current_energy_meter_value：当前电池的能量计量值
chargingv：当前充电电压
charginga：当前充电电流
out_power：当前充电功率
charging_gun_temperature1：充电枪温度1
charging_gun_temperature2：充电枪温度2
types：电池类型，其中03代表磷酸铁锂电池（LFP）、06代表镍锰钴三元锂电池（NMC）、04代表锰酸锂电池（LMO）、05代表钴酸锂电池（LCO）、07代表LP
class_judge：数据持有者索引
label：故障电池数据标记，1表示故障电池，0表示正常电池

搜集汇总

背景与挑战

背景概述

该数据集来自深圳奥特迅和香港理工大学，覆盖2020年10月至2023年10月期间中国深圳30个公共电动汽车充电站的实时数据，包含21,175条充电序列，涉及10,154辆电动汽车的多种电池类型，以1分钟间隔记录电流、电压、功率等参数，并标注了工程报告和高温导致的故障类型，适用于电动汽车电池充电行为分析和故障检测研究。