Accurate parameter dependent electro-chemical numerical models for lithium-ion batteries are essential in industrial application. The exact parameters of each battery cell are unknown and a process of estimation is necessary to infer them. The parameter estimation generates an accurate model able to reproduce real cell data. The field of optimal input/experimental design deals with creating the experimental settings facilitating the estimation problem. Here we apply two different input design algorithms that aim at maximizing the observability of the true, unknown parameters: in the first algorithm, we design the applied current and the starting voltage. This lets the algorithm collect information on different states of charge, but requires long experimental times (60 000 s). In the second algorithm, we generate a continuous current, composed of concatenated optimal intervals. In this case, the experimental time is shorter (7000 s) and numerical experiments with virtual data give an even better accuracy results, but experiments with real battery data reveal that the accuracy could decrease hundredfold. As the design algorithms are built independent of the model, the same results and motivation are applicable to more complex battery cell models and, moreover, to other applications.

翻译：摘要： 锂离子电池精确的参数依赖电化学数值模型在工业应用中至关重要。每个电池单元的确切参数未知，需要通过估计过程进行推断。参数估计能够生成可重现真实电池数据的精确模型。最优输入/实验设计领域旨在创建便于参数估计的实验设置。本文应用了两种不同的输入设计算法，其目标均是最小化真实未知参数的不可观测性：第一种算法中，我们设计了施加电流和初始电压，这使算法能够收集不同荷电状态的信息，但需要较长的实验时间（60000秒）。第二种算法中，我们生成了由多个最优区间串联构成的连续电流，此时实验时间缩短（7000秒），且基于虚拟数据的数值实验获得了更优的精度结果，但真实电池数据的实验表明精度可能下降百倍。由于设计算法独立于模型构建，相同的结论与动机可推广至更复杂的电池单元模型及其他应用领域。