JMASM 55: MATLAB Algorithms and Source Codes of \u27cbnet\u27 Function for Univariate Time Series Modeling with Neural Networks (MATLAB)

Artificial Neural Networks (ANN) can be designed as a nonparametric tool for time series modeling. MATLAB serves as a powerful environment for ANN modeling. Although Neural Network Time Series Tool (ntstool) is useful for modeling time series, more detailed functions could be more useful in order to get more detailed and comprehensive analysis results. For these purposes, cbnet function with properties such as input lag generator, step-ahead forecaster, trial-error based network selection strategy, alternative network selection with various performance measure and global repetition feature to obtain more alternative network has been developed, and MATLAB algorithms and source codes has been introduced. A detailed comparison with the ntstool is carried out, showing that the cbnet function covers the shortcomings of ntstool

Optimal charging and state-of-charge estimation of a Lithium-ion cell using a simplified full homogenised macro-scale model

Advanced battery management systems (BMS) need accurate and computationally efficient Li-ion cell model for optimum operation as the performance of charging and estimation algorithms of BMS are dependent upon the accuracy of the mathematical model of a cell. This research work presents a novel, accurate and computationally efficient electrochemical model and develops charging and estimation algorithm based on the model. The simplified model is based on the novel full homogenised macroscale model (FHM). The simplified FHM model is compared with a simplified model based on the pseudo-two-dimensional (P2D) model. The FHM model is based on the homogenisation theory, while the volume averaging technique is the basis of the P2D model. Diffusion partial differential equations (PDEs) are approximated by ordinary differential equations with time-varying coefficients. The intercalation current and conduction equation are also approximated to develop variants of the simplified model. The diffusion and reaction rate parameters of the FHM model are more accurate at high temperatures than the parameters based on the empirical Bruggeman method, as the FHM model parameters are based on the numerical model of the electrode structure. The simulations results verify that, compared with a similar simplified model based on the P2D model, the proposed simplified FHM model is more accurate at 318K and higher temperature. The output voltage predicted by the proposed simplified model and the simplified P2D model has a root mean square (RMS) tracking error of 0.6% and 2%, respectively, at 1C input current and 318K temperature. The computational time of the proposed simplified model is reduced by 35% compared with that of the FHM model. Subsequently we present optimal charging of Li-ion cell based on the simplified full homogenised macro-scale (FHM) model. A solid electrolyte interface (SEI) layer model is included in the simplified FHM model to quantify health degradation. With these models, a multi-objective optimal control problem subject to constraints from safety concerns is formulated to achieve the health-conscious optimal charging. This constrained optimal control problem is converted to a nonlinear programming problem (NLP). A nonlinear model predictive control (NMPC) strategy is adopted by solving the NLP at each sampling time using the pseudo-spectral optimisation method. The effect of the input current upper bound on the cell film resistance Rfilm and state of health (SoH) reveals that Rfilm and SoH are more sensitive to input current upper bound at lower values of input current upper bound. Simulation results show that the simplified model and pseudo-spectral method are crucial for reducing the computational load to achieve feasible real-time implementation. The proposed algorithm is more efficient in reducing the health degradation than the conventional constant current constant voltage (CCCV ) charging algorithm since it can explicitly handle the film resistance and capacity as health parameters. Multiple cycle charging simulation reveals that the health-conscious algorithm decrease health degradation and increase battery life. Three observers are used and compared for output feedback charging of a Li-ion cell, i.e. extended Kalman filter (EKF), sliding mode observer (SMO) and moving horizon estimator (MHE). The observers are used in a closed-loop with an NMPC for optimal, health-conscious charging of a Li-ion cell. Simulation results show that EKF and SMO have a low computational burden, whereas MHE exhibits superior performance