储能电池组MATLAB分析数据

本数据集由北京理工大学机械与车辆学院储能电池组构建，专用于储能电池安全性能与寿命预测的人工智能算法模型测试与验证。数据来源于“储能电池全寿命的大数据和人工智能分析系统”，包含五类算法的专用分析数据：1. LSTM寿命预测数据：40Ah磷酸铁锂电池的循环容量、电压电流及温度数据；2. 析锂诊断数据（密度峰值聚类/DBSCAN）：包含130块正常电池与70块析锂电池（低温大倍率诱导）的特征参数（如IC曲线特征dq/dv、EIS参数、等效电路模型参数）；3. 容量预测数据（MLP/级联非线性映射）：CATL 280Ah电池在全寿命周期内的健康因子（Health Factors）及容量标定数据。该数据集特征维度丰富，涵盖瞬态物理量与提取特征量，适用于开发和验证各类电池状态估计与故障诊断算法。

This dataset was constructed by the School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, using energy storage battery packs, and is specifically designed for testing and validating artificial intelligence (AI) algorithm models for energy storage battery safety performance and lifespan prediction. The data is sourced from the "Big Data and Artificial Intelligence Analysis System for the Full Lifecycle of Energy Storage Batteries", and contains specialized analysis data for five types of algorithms:
1. LSTM lifespan prediction data: Cyclic capacity, voltage, current and temperature data of 40Ah lithium iron phosphate (LFP) batteries;
2. Lithium plating diagnosis data (Density Peak Clustering / DBSCAN): Characteristic parameters of 130 normal batteries and 70 lithium-plated batteries induced under low-temperature high-rate conditions, including Incremental Capacity (IC) curve features dq/dv, Electrochemical Impedance Spectroscopy (EIS) parameters, and equivalent circuit model parameters;
3. Capacity prediction data (Multi-Layer Perceptron (MLP) / cascaded nonlinear mapping): Health factors and capacity calibration data of CATL 280Ah batteries throughout their full lifecycle.
This dataset has rich feature dimensions, covering transient physical quantities and extracted feature quantities, and is suitable for developing and validating various battery state estimation and fault diagnosis algorithms.

搜集汇总

背景与挑战

背景概述

该数据集由北京理工大学构建，专注于储能电池安全性能与寿命预测的人工智能算法模型测试与验证。数据包括LSTM寿命预测、析锂诊断和容量预测三类算法专用数据，涵盖磷酸铁锂电池和CATL电池的循环容量、电压电流、温度及特征参数，适用于开发和验证电池状态估计与故障诊断算法。