Thermal management is a critical task of battery control to ensure the safe, efficient, and enduring performance of the battery system, which can be considered as an interconnected thermal network of cells. The basis of thermal management is the estimation of temperature and its gradient across the battery system, which has received extensive attention in the literature. However, existing works neglect two important constraints in practical battery systems: 1) limited number of available sensors and 2) presence of system uncertainty such as parameter error. This paper is the first to investigate robust battery system temperature estimation under sparse sensing and system uncertainty. We first propose a framework consisting of optimization problems at three different levels: 1) evaluation of the worst case estimation performance (error) under uncertainty; 2) robust observer design to minimize the worst case error; and 3) optimization of sensor locations. Two robust estimation methods are then used to solve the problem. The system uncertainty considered in this paper is the unknown resistance variability among battery cells, but the methodology can be applied to address other types of uncertainty. It is shown that the designed observers could guarantee and improve the robustness and reliability of estimation by significantly reducing the worst case estimation errors induced by uncertainty.

中文翻译：

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稀疏感和不确定性下电池系统温度分布的鲁棒估计

热管理是电池控制的关键任务，以确保电池系统的安全，有效和持久的性能，可以将其视为相互连接的电池热网络。热管理的基础是整个电池系统中温度及其梯度的估算，这在文献中已引起广泛关注。但是，现有的工作忽略了实际电池系统中的两个重要限制：1）可用传感器的数量有限，以及2）系统不确定性（例如参数误差）的存在。本文是第一个研究稀疏传感和系统不确定性条件下鲁棒电池系统温度估算的方法。我们首先提出一个框架，其中包含三个不同级别的优化问题：1）在不确定性下评估最坏情况下的估计性能（错误）；2）健壮的观察者设计，以最小化最坏情况的误差；3）优化传感器位置。然后使用两种鲁棒的估计方法来解决该问题。本文考虑的系统不确定性是电池单元之间未知的电阻可变性，但该方法可用于解决其他类型的不确定性。结果表明，设计的观测器可以通过显着减少不确定性引起的最坏情况估计误差，来保证并提高估计的鲁棒性和可靠性。本文考虑的系统不确定性是电池单元之间未知的电阻可变性，但该方法可用于解决其他类型的不确定性。结果表明，设计的观测器通过显着减少不确定性引起的最坏情况估计误差，可以保证并提高估计的鲁棒性和可靠性。本文考虑的系统不确定性是电池单元之间未知的电阻可变性，但该方法可用于解决其他类型的不确定性。结果表明，设计的观测器可以通过显着减少不确定性引起的最坏情况估计误差，来保证并提高估计的鲁棒性和可靠性。