Abstract
To develop a full battery model in view to accurate battery management, Li-ion cell dynamics is modelled by a capacitor in series with a simplified Randles circuit. The open circuit voltage is the voltage at the capacitor terminals, allowing, in this way, for the dependence of the open circuit voltage on the state-of-charge to be embedded in its capacitance. The Randles circuit is recognised as a trusty description of a cell dynamics. It contains a semi-integrator of the current, known as the Warburg impedance, that is a special case of a fractional integrator. To enable the formulation of a time-domain system identification algorithm, the Warburg impedance impulse response was calculated and normalised, in order to derive a finite order state-space approximation, using the Ho-Kalman algorithm. Thus, this Warburg impedance LTI model, with known parameters (normalised impedance) in series with a gain block, is suitable for system identification, since it has only one unknown parameter. A LTI System identification Algorithm was formulated to estimate the model parameters and the initial values of both the open circuit voltage and the states of the normalised Warburg impedance. The performance of the algorithm was very satisfactory on the whole state-of-charge region and when compared with low order Thevenin models. Once it is understood the parameters variability on the state-of-charge, temperature and ageing, we envisage to continue the work using parameter-varying algorithms.

Abstract
Knowledge of the Kalman filter is very important in machine learning since is the basis for understanding more advanced concepts. Towards this end, control and estimation courses should assure the understanding of the concept and its correct application. A tutorial on the design, implementation and test of the KF to denoise the discharge current of a Li-ion cell is presented in this article. The students are also meant to acquire the discharge data used in the case study - Discharge of a Li-ion cell. The Battery Discharger Board is a low cost device to discharge Li-ion cells with a user programmable current discharge profile. The discharge is controlled and monitored by an external microcontroller connected to a host computer that stores and processes the discharge data. This board has been constructed to help students to gain insight into batteries. The current is measured by ACS712 Hall sensors, which are low cost but also very noisy. To de-noise the current measurements two different KF are used with the current being modelled as the state of a first order integrator. In the first approach, the KF assumes that the system is disturbed by process and measurement noises while in the second it only assumes measurement noise, The operation of the discharge board is illustrated in two experiments: (i) one with a constant discharge current and (ii) the other with a pulsed current. In both experiments, the filters performance was very good. Copyright (c) 2023 The Authors.

Abstract
In this work, the prediction of a time series is formulated as a gaussian process regression, for different levels of noise. The gaussian regressor is translated into lower rank Dynamic Mode Decomposition methods that use kernels (K-DMD) - Kernel regression and Least Squares Support Vector Machines. The presented unified approach delivers an algorithm where the optimisation of the marginal likelihood function can be used to find the parameters of the kernel regression. The viability of the procedure is demonstrated on a chaotic series, with quite good adjustment results being obtained. Copyright (c) 2023 The Authors.

Abstract

Abstract
Associated with the exploitation of metallic minerals in Europe during the 20th century, several mining areas were abandoned without adequate environmental intervention. Furthermore, these areas lack studies to characterize the impact of pollution on the hydrogeological system. The area surrounding the tungsten mine of Regoufe, in northern Portugal, is one such area exploited during the Second World War. The accumulation of sulfide-rich tailings may have caused an acid mine drainage (AMD), where the leaching processes caused by seepage water led to soil contamination, evidenced by its acid character and anomalous concentrations of some Potentially Toxic Elements (PTE) reported in previous studies. The present research proposes an innovative approach that seeks the integration of different geophysical techniques to characterize the impact of mining activity on the subsurface. Electrical resistivity (ER) and electromagnetic (EM) were used to measure subsurface electrical properties. In addition, seismic refraction and Multichannel Analysis of Surface Waves (MASW) were performed to characterize the geometry, depth, and geomechanical behavior of the soil and rock bodies. The integration of these techniques allowed the interpretation of hydrogeological sections and a 3D resistivity volume to gain insight into the distribution of potentially contaminating fluids and tailings material present in the mining valley.

Abstract