Data selection framework for battery state of health related parameter estimation under system uncertainties

IF 15 1区 工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2023-10-01 DOI:10.1016/j.etran.2023.100283
Jackson Fogelquist, Xinfan Lin
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Abstract

Data selection is a practical technique for improving parameter estimation accuracy through the strategic selection of information-rich data for use in the estimation algorithm. Traditional selection criteria have been either heuristic or sensitivity-based, without consideration of uncertainties in measurement, model, or parameter. In this paper, we propose an uncertainty-aware data selection framework that selects data segments based on the potential of the ingrained data structures to mitigate the influence of system uncertainties on the estimation result. The framework comprises two components: the data quality rating and data selection algorithm. The data quality rating is a metric for evaluating the uncertainty-propagating data structures of a data segment, and the data selection algorithm automatically integrates the data selection into the estimation procedure. Furthermore, a novel adaptive approximation of model/measurement uncertainty is derived and implemented in the data quality rating formula to enhance performance in the presence of time-varying sensor bias/noise and unmodeled system dynamics. The framework is validated through an advanced battery management system application, where two lithium-ion battery health-related electrochemical parameters are separately estimated under random drive-cycle input data to emulate battery state of health monitoring for an electric vehicle. We show that the drive-cycle data, which are frequently used for battery state of health estimation as the only available data during battery operation, may not provide accurate estimation results due to the existence of large portions of low-quality data (low sensitivity and high uncertainty). By extracting the high-quality data segments, the data selection framework reduced experimental estimation errors by one order of magnitude when compared with the conventional approach of estimating without data selection.

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系统不确定性下电池健康状态相关参数估计的数据选择框架
数据选择是一种提高参数估计精度的实用技术,通过策略性地选择信息丰富的数据用于估计算法。传统的选择标准要么是启发式的,要么是基于敏感性的,而没有考虑测量、模型或参数中的不确定性。在本文中,我们提出了一个不确定性感知的数据选择框架,该框架基于根深蒂固的数据结构的潜力来选择数据段,以减轻系统不确定性对估计结果的影响。该框架包括两个部分:数据质量评级和数据选择算法。数据质量评级是评估数据段的不确定性传播数据结构的度量,数据选择算法将数据选择自动集成到估计过程中。此外,在数据质量评定公式中推导并实现了一种新的自适应模型/测量不确定性近似,以提高存在时变传感器偏差/噪声和未建模系统动力学时的性能。该框架通过先进的电池管理系统应用程序进行验证,在随机驱动循环输入数据下,分别估计两个锂离子电池健康相关的电化学参数,以模拟电动汽车的电池健康状态监测。我们表明,由于存在大量低质量数据(低灵敏度和高不确定性),驱动循环数据可能无法提供准确的估计结果,而驱动循环数据是电池运行过程中经常用于电池健康状态估计的唯一可用数据。通过提取高质量的数据片段,与传统的无数据选择估计方法相比,该数据选择框架将实验估计误差降低了一个数量级。
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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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