动力电池主动安全研究数据

本数据集依托国家重点研发计划项目（2022YFE0207900），聚焦新能源汽车高比能动力电池主动安全核心技术研究，系统收录了2022年11月至2025年10月期间，针对高比能动力电池体系，在隐性缺陷显化机理研究、极片对齐度检测研究、基于双向充电的电池系统安全预警方法等三个核心指标下产生的242个数据文件，数据量达11GB，格式涵盖.png、.raw、.xlsx、.csv、.txt等多种类型。研究构建了“缺陷植入-精准检测-工况预警”全链条主动安全测试体系，整合缺陷检测技术，CT检测系统和基于双向充电的电池安全预警算法，实现“实验测试-仿真建模-实车验证”三维数据支撑体系，实现从微观极片缺陷检测到全生命周期安全预警的跨尺度数据采集。数据集包含3大核心模块：电池隐性缺陷显化机理与检测数据、极片原位对齐度CT检测数据、基于双向充电的电池系统安全预警数据，涵盖内短路电压下降率（K值）、极片对齐度检测分辨率、安全预警特征指标、衰减状态估计误差等指标。数据采集严格遵循国家/行业标准，建立“设备计量标定-多重复实验-跨场景交叉验证”全流程质控体系。本数据集填补了高比能电池主动安全“缺陷-检测-预警”全链条数据空白，为学术界解析电池隐性缺陷显化机理、构建跨尺度安全预警模型，以及工业界优化电池产线质检流程、开发车规级BMS安全算法提供直接的数据支撑，对推动新能源汽车V2G技术规模化应用及主动安全技术产业化升级具有重要意义。This dataset is supported by the National Key R&D Program of China (Grant No. 2022YFE0207900), focusing on core technical research on active safety of high-energy-density power batteries for new energy vehicles. It systematically collects 242 data files generated between November 2022 and October 2025, targeting high-energy-density power battery systems, under three core research directions: mechanism research on the manifestation of latent defects, research on electrode sheet alignment detection, and battery system safety early warning methods based on bidirectional charging. The total data volume reaches 11 GB, covering multiple file formats including .png, .raw, .xlsx, .csv, .txt and others. This research constructs a full-chain active safety test system of "defect implantation - precise detection - working condition early warning", integrates defect detection technologies, CT detection systems and bidirectional charging-based battery safety early warning algorithms, and establishes a three-dimensional data support system of "experimental test - simulation modeling - real vehicle verification", realizing cross-scale data collection from micro-scale electrode sheet defect detection to full-life-cycle safety early warning. The dataset includes three core modules: data on the manifestation mechanism and detection of battery latent defects, CT detection data for in-situ alignment of electrode sheets, and battery system safety early warning data based on bidirectional charging. It covers indicators such as internal short-circuit voltage drop rate (K value), electrode sheet alignment detection resolution, safety early warning feature indicators, and attenuation state estimation error. The data collection strictly complies with national and industrial standards, and a full-process quality control system of "equipment metrological calibration - multiple repeated experiments - cross-scenario cross-validation" has been established. This dataset fills the data gap in the full chain of "defect - detection - early warning" for active safety of high-energy-density batteries. It provides direct data support for the academic community to analyze the manifestation mechanism of battery latent defects and build cross-scale safety early warning models, as well as for the industrial sector to optimize battery production line quality inspection processes and develop vehicle-grade BMS safety algorithms. It is of great significance for promoting the large-scale application of V2G technology in new energy vehicles and the industrial upgrading of active safety technologies.

搜集汇总

背景与挑战

背景概述

该数据集依托国家重点研发计划项目，聚焦新能源汽车高比能动力电池主动安全研究，系统收录了2022年11月至2025年10月期间在隐性缺陷显化、极片对齐度检测和电池安全预警三个核心模块下产生的242个数据文件，总数据量达11GB。数据集构建了“缺陷植入-精准检测-工况预警”全链条测试体系，涵盖多种数据格式，旨在为电池安全机理解析和工业应用提供直接数据支撑。