Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement

This study presents a method of estimating battery cell core and surface temperature using a thermal model coupled with electrical impedance measurement, rather than using direct surface temperature measurements. This is advantageous over previous methods of estimating temperature from impedance, which only estimate the average internal temperature. The performance of the method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate cell fitted with surface and core thermocouples for validation. An extended Kalman filter, consisting of a reduced order thermal model coupled with current, voltage and impedance measurements, is shown to accurately predict core and surface temperatures for a current excitation profile based on a vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same thermal model and impedance measurement input is capable of estimating the convection coefficient at the cell surface when the latter is unknown. The performance of the DEKF using impedance as the measurement input is comparable to an equivalent dual Kalman filter using a conventional surface temperature sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on Sustainable Energy, 201

Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201

On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part II. State estimation via impedance-based temperature sensing

Impedance-based temperature detection (ITD) is a promising approach for rapid estimation of internal cell temperature based on the correlation between temperature and electrochemical impedance. Previously, ITD was used as part of an Extended Kalman Filter (EKF) state-estimator in conjunction with a thermal model to enable estimation of the 1-D temperature distribution of a cylindrical lithium-ion battery. Here, we extend this method to enable estimation of the 2-D temperature field of a battery with temperature gradients in both the radial and axial directions. An EKF using a parameterised 2-D spectral-Galerkin model with ITD measurement input (the imaginary part of the impedance at 215 Hz) is shown to accurately predict the core temperature and multiple surface temperatures of a 32113 LiFePO$_4$ cell, using current excitation profiles based on an Artemis HEV drive cycle. The method is validated experimentally on a cell fitted with a heat sink and asymmetrically cooled via forced air convection. A novel approach to impedance-temperature calibration is also presented, which uses data from a single drive cycle, rather than measurements at multiple uniform cell temperatures as in previous studies. This greatly reduces the time required for calibration, since it overcomes the need for repeated cell thermal equalization.Comment: 11 pages, 8 figures, submitted to the Journal of Power Source

Joy and calm: how an evolutionary functional model of affect regulation informs positive emotions in nature

Key theories of the human need for nature take an evolutionary perspective, and many of the mental well-being benefits of nature relate to positive affect. As affect has a physiological basis, it is important to consider these benefits alongside regulatory processes. However, research into nature and positive affect tends not to consider affect regulation and the neurophysiology of emotion. This brief systematic review and meta-analysis presents evidence to support the use of an existing evolutionary functional model of affect regulation (the three circle model of emotion) that provides a tripartite framework in which to consider the mental well-being benefits of nature and to guide nature-based well-being interventions. The model outlines drive, contentment and threat dimensions of affect regulation based on a review of the emotion regulation literature. The model has been used previously for understanding mental well-being, delivering successful mental health-care interventions and providing directions for future research. Finally, the three circle model is easily understood in the context of our everyday lives, providing an accessible physiological-based narrative to help explain the benefits of nature

A Ground-Based Search for Thermal Emission from the Exoplanet TrES-1

Eclipsing planetary systems give us an important window on extrasolar planet atmospheres. By measuring the depth of the secondary eclipse, when the planet moves behind the star, we can estimate the strength of the thermal emission from the day side of the planet. Attaining a ground-based detection of one of these eclipses has proven to be a significant challenge, as time-dependent variations in instrument throughput and atmospheric seeing and absorption overwhelm the small signal of the eclipse at infrared wavelengths. We gathered a series of simultaneous L grism spectra of the transiting planet system TrES-1 and a nearby comparison star of comparable brightness, allowing us to correct for these effects in principle. Combining the data from two eclipses, we demonstrate a detection sensitivity of 0.15% in the eclipse depth relative to the stellar flux. This approaches the sensitivity required to detect the planetary emission, which theoretical models predict should lie between 0.05-0.1% of the stellar flux in our 2.9-4.3 micron bandpass. We explore the factors that ultimately limit the precision of this technique, and discuss potential avenues for future improvements.Comment: 10 pages, 1 table, four figures, accepted for publication in PAS

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic

ECONOMETRIC MODEL OF THE U.S. SHEEP INDUSTRY FOR POLICY ANALYSIS

The U.S. sheep inventory has been declining for many years. To further investigate this trend, an econometric sector model using single demand equations was developed to analyze the impacts of two alternative levels of wool marketing loan rates.Marketing,