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Sensors-models trade-offs in battery state estimation

机译:传感器 - 电池状态估计中的折磨权

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In this paper, we explore the trade-offs between sensors' and models' accuracy for state estimation in battery management systems. If, in a battery pack, high quality sensors were used, then state estimation (or monitoring) would be improved at the expenses of hardware costs. On the other hand, if accurate models were used within the estimation algorithms, better estimates could be produced at the expenses of engineering time required for modeling the battery and parameterizing the model, as well as the inevitable increase in microprocessors' power due to the increase in the algorithm's computational complexity. Hence, the research question we ask is: for a given budget or a given estimation error tolerance, what is the minimum sensor accuracy and model accuracy needed in order to achieve that target? In its simple form, this is a two-dimensional multi-objective optimization problem.
机译:在本文中,我们探讨了电池管理系统中传感器和模型精度之间的权衡。 如果在电池组中,使用高质量传感器,则状态估计(或监控)将以硬件成本的费用提高。 另一方面,如果在估计算法中使用了准确的模型,则可以在建模电池所需的工程时间和参数化模型所需的工程时间的费用中产生更好的估计,以及由于增加导致的微处理器功率的不可避免地增加 在算法的计算复杂性中。 因此,我们问的研究问题是:对于给定的预算或给定的估计误差容差,以实现该目标的最小传感器精度和模型精度是多少? 在其简单的形式中,这是一个二维多目标优化问题。

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