Data for "Electric Vehicle Battery Parameter Identification and SOC Observability Analysis: NiMH and Li-S Case Studies"

In this study, battery
model identification is performed to be applied in electric vehicle battery
management systems. Two case studies
are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulfur (Li-S),
a promising next-generation technology. Equivalent circuit battery model
parameterization is performed in both cases using the Prediction-Error
Minimization (PEM) algorithm applied to experimental data. Performance of a
Li-S cell is also tested based on urban dynamometer driving schedule (UDDS) and
the proposed parameter identification framework is applied in this case as
well. The identification results are then validated against the exact values of
the battery parameters. The use of identified parameters for battery state-of-charge (SOC)
estimation is also discussed. It is shown
that the set of parameters needed can change with a different battery
chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is
adequate for SOC estimation whereas
Li-S battery SOC estimation is more challenging due to its unique
features such as flat OCV-SOC curve. An observability analysis shows that Li-S
battery SOC is not fully observable and the existing methods in the literature might
not be applicable for a Li-S cell. Finally, the effect of temperature on the
identification results and the observability are discussed by repeating the
UDDS test at 5, 10, 20, 30, 40 and 50
degree Celsius. <br><br>File created in MATLAB 2015a.

本研究围绕电动汽车电池管理系统的应用需求，开展电池模型辨识相关工作。本次研究共设置两组案例：其一为成熟的镍氢电池（nickel-metal hydride, NiMH）技术，其二为极具应用前景的下一代锂硫电池（lithium-sulfur, Li-S）技术。

两组案例均采用预测误差最小化（Prediction-Error Minimization, PEM）算法，结合实验数据完成等效电路电池模型的参数化建模。此外，本研究还基于城市驾驶循环（urban dynamometer driving schedule, UDDS）对锂硫单体电池进行性能测试，并将所提出的参数辨识框架应用于该测试场景中。

随后，通过与电池参数的真实值进行对比，对辨识结果开展验证。本文还探讨了将辨识得到的参数应用于电池荷电状态（state-of-charge, SOC）估计的相关内容。研究表明，所需的参数集合会因电池化学体系的不同而发生变化：对于镍氢电池而言，其开路电压（open circuit voltage, OCV）足以满足SOC估计的需求；而锂硫电池由于其独特的特性，例如平缓的OCV-SOC曲线，SOC估计难度更高。

可观性分析结果显示，锂硫电池的SOC并非完全可观，现有文献中的相关方法或许无法直接应用于锂硫单体电池。最后，本研究通过在5℃、10℃、20℃、30℃、40℃及50℃下重复开展UDDS测试，探讨了温度对辨识结果及可观性分析的影响。本数据集相关文件由MATLAB 2015a生成。