Graphite//LFP synthetic training diagnosis dataset

This training dataset was calculated using the mechanistic modeling approach. See the “Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis“ publication for mode details. More details will be added when published.
The diagnosis training dataset was compiled with a resolution of 0.01 for the triplets and C/25 charges. This accounts for more than 5,000 different paths. Each path was simulated with 0.85% increases for each degradation up to 85%. This accounts for 100 simulations per path. The training dataset, therefore, contains more than 500,000 voltage vs. capacity curves.
4 Variables are included:
Cell info: Contains information on the setup of the mechanistic model
1 Positive electrode
2 Negative electrode
3 Loading ration
4 Offset
5 Resistance adjustment

Qnorm: normalize capacity scale for all voltage curves

pathinfo: index for simulated conditions for all voltage curves
1 LLI
2 LAMPE
3 LAMNE
4 Corresponding capacity loss

volt: voltage data. Each column corresponds to the voltage simulated under the conditions of the corresponding line in pathinfo.

本训练数据集采用机制建模方法进行计算。详情请参阅《基于人工智能的锂离子电池诊断与预测的基准合成训练数据》一文的详细说明。待文章发表后，将补充更多详细信息。诊断训练数据集以0.01的分辨率对三元组和C/25充电倍率进行编制，涵盖了超过5,000条不同的路径。每条路径均通过0.85%的降解率增加进行模拟，直至85%，每条路径包含100次模拟。因此，训练数据集中包含超过500,000条电压与容量曲线。数据集包含以下4个变量：
细胞信息：包含机制模型设置的详细信息
1. 正极
2. 负极
3. 负载率
4. 偏移量
5. 电阻调整
Qnorm：对所有电压曲线进行容量归一化
路径信息：所有电压曲线模拟条件的索引
1. LLI
2. LAMPE
3. LAMNE
4. 对应的容量损失
电压：电压数据。每一列对应于路径信息中对应行下模拟的电压。