Battery Aging Dataset for HEV Drive Cycles applied to 2.5Ah A123 LFP Cells

Three 2.5Ah LFP battery cells are cycled until they reached a very low SOH (70% for the 300k km case, 40% for the 206k km case, and 15% for the 95k km case). The tests begin with an HPPC test and two repeated 1C discharge tests, which are followed by 300 repetitions of a power profile calculated for an HEV following the WLTP drive cycle. Each aging case has a different power profile, which is designed based off of an empirical aging model to acheive approximately 95,000 km, 206,000 km, or 300,000 km of driving before the cell ages to 80% SOH. One possible application of the dataset could be parameterizing/training and testing SOC or SOH estimation models for hybrid electric vehicles. Since there are three cases which age at a different rate, two cases could be used to train a machine learning algorithm and the third case could be used to test it, for example. For full details regarding the design of the tests see the Applied Energy paper \"Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation\".

本数据集包含3节2.5Ah磷酸铁锂电池（LFP battery cells），将其实施循环老化测试直至健康状态（State of Health，SOH）降至极低水平：30万公里工况对应70% SOH、20.6万公里工况对应40% SOH、9.5万公里工况对应15% SOH。测试流程首先开展混合脉冲功率特性测试（Hybrid Pulse Power Characterization，HPPC）与两次重复的1C倍率放电测试，随后执行300次重复的功率轮廓测试——该功率轮廓基于遵循全球轻型车测试循环（Worldwide Harmonized Light Vehicles Test Procedure，WLTP）的混合动力汽车（Hybrid Electric Vehicle，HEV）工况计算得到。每种老化工况对应一套差异化的功率轮廓，其设计依托经验老化模型，旨在使电芯在衰减至80% SOH前，实现约9.5万、20.6万或30万公里的行驶里程。该数据集的潜在应用场景包括：用于混合动力汽车荷电状态（State of Charge，SOC）或健康状态（SOH）估算模型的参数化、训练与测试。例如，由于本次测试包含三种老化速率各异的工况，可选取其中两组用于机器学习算法的训练，剩余一组用于算法性能测试。关于测试设计的完整细节，请参阅发表于《应用能源》（Applied Energy）的学术论文《混合动力汽车电池健康状态敏感型能量管理：寿命预测与老化实验验证》（Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation）。