A new German Charité Jet Lag Scale for jet lag symptoms and application

Travelling across multiple time zones provokes adaptation of endogenous circadian rhythm to the new time zone. Within the context of previous studies, an English-language state-of-health questionnaire, the Columbia Jet Lag Scale, is the only sufficiently validated scale for jet lag and its symptoms. This study presents a new state-of-health questionnaire in German, one intended to achieve standardisation of surveys on jet lag. The questionnaire was applied to define the baseline for the prevalence of jet lag symptoms based on a reference group (n = 36). The jet lag score ascertained was subsequently applied to determine the frequency of jet lag in a group of 53 subjects. Systematic investigation of the frequency of jet lag symptoms had not been previously presented. Among the group of 53 travelling test subjects, 60% demonstrated moderate jet lag symptoms.

Practitioner Summary: This introduction of the Charité Jet Lag Scale, the first German jet lag questionnaire, calls attention to this topic for the first time since a 2000 publication in Ergonomics. Our systematic investigation of jet lag frequency, with the new scale, determined moderate jet lag symptoms among 60% of subjects.     

Online state-of-health prediction of lithium-ion batteries with limited labeled data

State-of-health (SOH) plays a vital role in battery health management and power system stability. This process can be achieved by capacity estimation. However, in practice, the capacity of a battery is difficult to obtain online given that it cannot be determined with general sensors. This means that the capacity is only known for the limited cycles of the batteries. To address this issue, we propose a novel semi-supervised learning framework to estimate the capacity of unlabeled data to achieve better SOH prediction. First, four indirect features are extracted from the charging profiles. Then an improved locally linear reconstruction method is used to determine the capacity distributions of the unlabeled data. Combined with the oversampling method applied to generate a series of data by the estimated distributions, a support vector regression model is utilized to predict the RUL of the batteries given the threshold values of the batteries. A case study with two types of cellular phone lithium-ion batteries is presented to illustrate the effectiveness of the proposed method for the prediction of the remaining useful life of different batteries and different starting points. The experimental results prove that the performance of the proposed method is better than the K-nearest neighbor method and locally linear reconstruction method in terms of accuracy and robustness.     

State-of-Health Map of Lithium-ion Batteries Established by ICE Map Approach

通过内燃机外特性曲线图的方法建立锂离子电池健康状况图

贺林¹,王铭伟¹,魏宇江²,芮鹏程¹,俞圣杰¹,石琴²*

（1. 合肥工业大学 汽车智能与电动实验室,合肥 230009;

2 合肥工业大学 汽车与交通工程学院,合肥 230009）

摘要：

锂离子电池的电池健康状态（SOH）反映了锂离子电池的寿命信息。因此,准确估计锂离子电池运行过程中的SOH是非常必要的。本文提出一个SOH图来描述锂离子电池的健康状况。直流内阻（DCR）和开路电压（OCV）是锂离子电池的两个关键参数,可以通过测量和计算得到。由于环境温度的严重影响,提出了温度换算法,将不同温度下的DCR/OCV换算成25℃时的标称值,并与内燃机外特性曲线图（ICE Map）进行比较,用标称DCR和OCV来表示SOH,可以查到标称SOH值。在SOH图中,一对DCR和OCV只能映射出一个唯一的SOH,有利于工程应用。

关键词：锂离子电池,健康状态,内燃机外特性曲线图,标称SOH,SOH图

     

一种基于粒子滤波的锂离子电池健康预测算法

安全性是制约锂离子电池发展的关键瓶颈,锂离子电池故障往往会导致系统性能下降并可能使系统瘫痪,军事应用时将直接导致各种无人作战系统无法遂行战略战术预期。根据锂离子电池电学特性建立一种融合非线性开路电压、电流,温度,循环次数以及随时间变化的剩余容量等多种电池动态特性的集总参数模型。以锂离子电池电化学工作过程为基础,提出一种依据序列重要性重采样来预测其单次放电循环放电终止时间的和循环寿命的粒子滤波算法。仿真结果表明以集总参数模型为基础的粒子滤波算法能对锂离子电池健康状态进行正确预测,为其健康管理提供了一种关键的理论支持。     

锂离子电池健康状态估计方法研究综述

电池管理系统是锂离子电池高效、安全运行的重要保障。电池的状态估计在电池管理系统中发挥着重要的作用。健康状态是锂离子电池状态估计的重要指标之一。通过对近几年国内外锂离子健康状态估计方法相关文献的整理,综述了锂离子电池健康状态的定义和估计方法,并对现有的估计方法进行了分类和阐述。最后针对现有估计方法的不足,提出未来需要改进的方向。