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Residual-based scheme for detection and characterization of faults in lithium-ion batteries

机译：用于锂离子电池故障的残留基于方案

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This work proposes a real-time scheme to monitor the occurrence of faults and perform fault characterization. Faults, in this context, correspond to changes in the parameters of the system being monitored. The method relies on the concept of Analytical Redundancy Relation (ARR), which can be defined as the evaluation of the mathematical constraints of the physical model of the system given the real, noisy measurements. The algorithm consists of two modules: a detection strategy that relies on the regular application of an ARR-based hypothesis test in discrete time-steps; and an optimization procedure to estimate the changes undergone after a fault. By selecting a set of feasible solutions from the output of the optimization algorithm, the method also sheds some light on the uncertainty associated to the estimated quantities. The methodology is tested on simulated data of lithium-ion batteries in unmanned aerial vehicles.
机译：这项工作提出了一种实时方案来监视故障的发生并执行故障表征。在此上下文中，故障对应于正在监视系统的参数的变化。该方法依赖于分析冗余关系（ARR）的概念，可以定义为赋予真实，嘈杂测量的系统物理模型的数学约束的评估。该算法由两个模块组成：一种检测策略，依赖于在离散时间步骤中依赖于基于ARR的假设试验的检测策略;和一个优化过程，以估计故障后发生的变化。通过从优化算法的输出中选择一组可行的解决方案，该方法还在与估计数量相关联的不确定性上缩小一些光。在无人航空车辆中的锂离子电池的模拟数据上测试了方法。

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