Accurate real-time estimation of battery State of Charge (SOC) and State of Health (SOH) is essential for electric vehicle (EV) performance and safety. We propose a hybrid framework combining deep learning with physics insights using LFP cell data. Our core model is a series CNN-TCN-DNN network trained on raw voltage/current signals; replacing measured temperature with cumulative charge ($\int I d t$) as an input improves accuracy. The model is tested under input noise and bias to ensure robustness. For example, it consistently achieves SOC errors below 2% MAPE and more accurate SOH tracking, enabling more reliable range prediction.

This paper introduces a novel sliding mode controller design that guarantees robust closed-loop pole placement within a defined vertical strip and efficiently rejects disturbances in uncertain linear systems. The sliding mode controller (SMC) is developed using an optimal approach that employs Linear Quadratic Regulator (LQR) techniques. The system's sliding surface is designed using stable eigenvectors (EV) combined with a scalar sign function (SSF). The controller gain parameter is determined to ensure robust pole clustering. This paper discusses the control design and stability analysis of the new optimal sliding mode controller. The simulation results show that the proposed approach is able to grant robust trajectory following with high precision.

The interest of research and automotive industries is concentrating progressively on the Electric Vehicles (EV) which are a global transportation development currently in order to achieve considerable carbon emission reductions. Electric batteries are the essential component of the EV and precise remaining useful life prediction is the main to ensure its reliability. As a result, the inside workings of these battery systems must be fully included. There is presently no precise model for predicting an EV battery's aging. This paper presents an intelligent method for estimating the State Of Charge (SOC) of the battery.

This issue of New Methods and Approaches in Decision and Control of Intelligent and Cyber Systems contains a collection of extended versions of best presented papers in CoDIT 2019 conference and ad...

This article presents a new scheme of fuzzy sliding-mode control for a class of Takagi-Sugeno fuzzy models based on a parallel distributed compensator and using a scalar sign function method. First, a fuzzy model is constructed to represent the local dynamic behaviors of the given nonlinear system. A global controller is then constructed by combining all local state feedback controllers and a global supervisory sliding mode controller. The globally asymptotic stability of the closed-loop system is mathematically proved. Finally, the validity of the proposed design strategy is demonstrated through the simulation of an inverted pendulum on a cart.

The modelling of the chemical reactor behavior is always difficult task due to the absence of more detailed knowledge about the considered chemical transformation. We treat in this context the Nickel-Metal Hydride (Ni-MH) battery system. In this paper, an improved fuzzy c-regression model is proposed in order to develop a Ni-MH battery model on which a modified distance is introduced in the objective function of fuzzy c-regression model algorithm in the purpose of taking into account the outliers. After that the obtained model is employed to estimate the Ni-MH battery's State Of Heath (SOH). The experimental results indicate that the proposed method can be ensured an acceptable accuracy of the SOH estimation for Ni-MH battery system.

This paper studies the problem of the parameter identification based on fuzzy c-regression models for nonlinear systems. The novel procedure combines the possibilistic c-means procedure with fuzzy c-regression models (FCRM) in order to reduce the effects of noisy data. In comparison to the existing algorithms in the literature, the proposed method utilizes a generalized objective function that reduces the errors of partitioning data sets contaminated by noise and as a consequence an accurate model is obtained. The results of this study demonstrate the effectiveness of proposed method compared with other extended versions of FCRM algorithm.

This paper presents a new method for fuzzy c-regression models clustering algorithm. The main motivation for this work is to develop an identification procedure for nonlinear systems using weighted recursive least squares and particle swarm optimization. The fuzzy c-regression models algorithm is sensitive to initialization which leads to the convergence to a local minimum of the objective function. In order to overcome this problem, particle swarm optimization is employed to achieve global optimization of FCRM and to finally tune parameters of obtained fuzzy model. The weighted recursive least squares is used to identify the unknown parameters of the local linear model. Finally, validation results involving simulation of two examples have demonstrated the effectiveness and practicality of the proposed algorithm.

Predictive control of systems is very much related to the efficiency and cost of systems, as well as to the quality of systems outcomes. However, it is difficult to achieve optimal predictive control because most predictive controls for systems have characteristics of randomness, strong and complex constraints and nonlinearity. Conventional methods of solving constrained nonlinear optimization problems for predictive control are mainly based on quadratic programming, which is quite sensitive to initial values, easy to trap in local minimal points, and requires large computational effort. In order to overcome these problems, Discrete binary particle swarm optimization is used to perform model predictive controller for nonlinear process with constraints. The performances obtained are compared with those given by the MPC method. The simulation results show that the proposed algorithm outperforms MPC algorithm in terms of performance and robustness.