An investigation of temperature-sensing textiles for temperature monitoring during sub-maximal cycling trials

Temperature-sensing textiles have been proposed for a variety of applications, including health monitoring and sports. Skin temperature ( Tsk) measurements are an important parameter in performance sports and can be used to better understand thermoregulation during exercise. Currently, most Tsk measurements are taken using skin-mounted thermistors, which can be uncomfortable to the wearer, or thermal imaging, which can be difficult to implement and analyze. This work investigates the feasibility of using textile temperature-sensing electronic yarns (E-yarns) to measure human skin temperature during sub-maximal cycling trials. E-yarns were attached to commercially available cycling suits and measurements were recorded using both the E-yarns and the skin-mounted thermistors at rest and during sub-maximal cycling. Temperature readings were compared between the two temperature-sensing methodologies to determine the viability of using the temperature-sensing E-yarns for this application. Differences in the Tsk measurements as high as 5.9℃ between the E-yarns and skin-mounted thermistors for participants at rest have been shown. This work has also identified that a build-up of sweat significantly altered the Tsk recorded by the E-yarns in some cases. Further experiments explored the effect of saline solutions (simulating sweat) on the response of the temperature-sensing E-yarns. This work has highlighted boundary conditions for taking point Tsk measurement using electronic textiles.

Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range

A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycled under each combination of temperatures (-10oC, 25oC, 60oC) and SOC ranges (0-10%, 25-75%, 90-100%, 0-100%) up to 60 equivalent cycles. The discharge data is transformed into dQ/dV-V curve and its features of the peaks and valleys are further taken for machine learning. Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range.

Impact of the Temperature in the Evaluation of Battery Performances During Long-Term Cycling—Characterisation and Modelling

This paper presents the results regarding the thermal characterisation and modelling of high energy lithium-ion battery cells at both room (25 °C) and cycling (35 °C) temperatures. In this work two types of Nickel Manganese Cobalt (NMC) batteries are studied: a fresh (or uncycled) and an aged (or cycled) battery cells. The ageing of the studied NMC battery cells is achieved by means of accelerated ageing tests (i.e., repetition of numerous charge and discharge cycles) at 35 °C cycling temperature. Temperature at the surface of the battery cells is characterised, with a set of three discharge current rates 0.3C (i.e., 6 A), 1C (i.e., 20 A) and 2C (i.e., 40 A), and the evolutions at three different locations on the surface of the battery cells namely, at the top, in the center and at the bottom regions are measured. In addition, temperature and ageing dependent electrochemical-thermal modelling of the uncycled and cycled battery cells is also successfully accomplished in case of both room and cycling temperatures. Numerical simulations were carried out in case of high 2C constant current rate, and the assessment of the modelling accuracy by comparison of the predicted battery cells voltage and temperature with respect to the experimental data is further presented. With this paper, thermal performances of battery cells prior and after long-term cycling are evaluated at the cycling temperature, next to the ambient temperature. Hence, thermal characterisation and modelling results are more closely reflecting that encountered by the battery cells in real cycling conditions, so that their performances are believed in this way to be more objectively evaluated.