储能电池性能预测精度验证支撑数据

本数据集由北京理工大学构建，专用于“储能电池全寿命的大数据和人工智能分析系统”中各关键状态预测模型的精度验证与性能评估。数据集包含四个子集：1. DCR预测验证数据：280Ah磷酸铁锂电池的循环运行数据，用于验证直流内阻预测精度；2. 温度预测验证数据：280Ah电池在CC-CV工况下的热特性数据，用于验证温度预测模型；3. 端电压预测验证数据：280Ah电池的动态电压响应数据，用于验证端电压预测误差；4. 容量预测验证数据：40Ah电池在全寿命周期内的健康因子及容量标定数据，用于验证SOH/容量预测算法。数据内容涵盖高精度（1s）采集的电流、电压、温度、容量及提取的健康因子，为电池状态分析算法的鲁棒性与准确性提供标准测试基准。

This dataset was constructed by Beijing Institute of Technology, specifically for the accuracy verification and performance evaluation of key state prediction models in the "Big Data and Artificial Intelligence Analysis System for Full Lifecycle of Energy Storage Batteries". This dataset includes four subsets: 1. DCR Prediction Verification Data: Cyclic operation data of 280Ah lithium iron phosphate batteries, used to verify the prediction accuracy of direct current internal resistance; 2. Temperature Prediction Verification Data: Thermal characteristic data of 280Ah batteries under CC-CV operating conditions, used to verify temperature prediction models; 3. Terminal Voltage Prediction Verification Data: Dynamic voltage response data of 280Ah batteries, used to verify the prediction error of terminal voltage; 4. Capacity Prediction Verification Data: Health factors and capacity calibration data of 40Ah batteries throughout their full lifecycle, used to verify SOH/capacity prediction algorithms. The dataset covers current, voltage, temperature, and capacity collected at a high sampling frequency of 1 second, as well as extracted health factors, providing a standard test benchmark for the robustness and accuracy of battery state analysis 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，专用于储能电池全寿命周期的大数据和人工智能分析系统中关键状态预测模型的精度验证与性能评估。数据集包含四个子集，涵盖280Ah磷酸铁锂电池的直流内阻、温度、端电压预测验证数据，以及40Ah电池的容量预测验证数据，提供高精度采集的电流、电压、温度、容量和健康因子，为电池状态分析算法的鲁棒性与准确性提供标准测试基准。