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.

In the last years have seen an increasing usage of Electrical Vehicle (EV). To guarantee safe and reliable operation, it's necessary to possess the capability to monitor, in real time, the state of health (SOH) of the battery. This paper presents a deep learning method which utilizes a Deep Neural network (DNN) for cell-level capacity estimation based on the voltage, current, and State Of Charge. First, a multi-physical models of the battery is done to extract input and output data for the different learning and testing phases. Second, two machine learning algorithms, including DNN and Convolution Neural Network (CNN), are used to predict SOH. Mean Absolute Error (MAE) and Mean Square Error (MSE) are selected as the evaluation index. The results show that the proposed algorithm DNN has the weakest error, which makes it possible to accurately predict the SOH and to have a better stability.

In this article, a new control strategy for a speed dc motor that is thought of as a black box is presented. The strategy involves identification with two models, the first is a traditional integer model and the second, which takes up more and more space in the identification part, is a fractional model. Modern heuristic techniques, like PSO, ABC, ACO, and GA, will be used in this phase of dentification Then comes a control phase with a fractional order proportional-integrator-derivative (FOPID) regulator. The FOPID controller is the most often used fractional order controller. The proportional gain Kp, integral gain KI, derivative gain Kd, integer order A, and derivative order$u$are the five parameters that make up the FOPID controller. To illustrate our strategy and the performance-based control effectiveness of the dc motor with a fractional model, we will apply the fractional regulator optimized by these algorithms on the two recognized models. Ordinary performance requirements specified by the corrector (overshoot, settling time, etc.) are executed while taking into account the Integral of Time and Absolute Error (ITAE) evaluation criteria. To show the effectiveness of our method, a robustness test with a variation of the parameters is carried out at the end of our work. A real-world DC motor's parameters are experimentally estimated using Matlab/Simulink connected to an Arduino Uno board. The proposed method proves that fractional calculus gives good performance for speed control of a DC motor.

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 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.

In this paper, a new PSO algorithm with a new adaptive weight of inertia and time acceleration coefficients (TVAC) is proposed; this algorithm called IAPSO is introduced for global optimization. The objective of this proposition is to initialize the weight of inertia to a high value, giving priority to the global exploration of the research space and gradually decreasing the new inertia adaptable to the weight in order to obtain refined solutions. The test of our algorithm is performed on three standard reference functions (Schwefel’s (unimodal), Ackley (multimodal) and Griewank (Multimodal)). The proposed IAPSO algorithm combined with the Fuzzy Clustering NPCM algorithm for modeling and identifying a non-linear system. The new NPCM-IAPSO grouping algorithm also solves the problems of the classical clustering algorithm (FCM, GK, PCM, EPCM, FCM-PSO, EPCM-PSO …etc.), such as convergence towards local optimization and sensitivity to initialization. The effectiveness of the proposed NPCM-IAPSO algorithm was tested on the furnace gas Box and Jenkins, dryer system and two other nonlinear systems described by differential equations.

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.

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

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.

In this paper, is proposed a robust sensorless speed observer-controller scheme for a Permanent Magnet Synchronous Motor (PMSM) based on the use of an Extended Kalman Filter (EKF) to estimate both position and speed, without any mechanical sensor. Then, a state-feedback optimal control algorithm is developed for uncertain of PMSM, based on the use of an Algebraic Riccati Equation (ARE) and a convex optimization approach. The robustness of this method, with respect to the parameters uncertainties, is tested, with success, for fourth order model of the studied process.

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.

This paper presents a fast method for nonlinear filtering based on multichannel Chandrasekhar equations. By assuming that the adaptive second order Volterra filter may be transformed in a multichannel input linear filter, we present a new form of the second order Volterra filtering based a the fast multichannel Chandrasekhar algorithm. This method has a computational complexity of 3.N/sup 3/ multiplications per time instant, where N represents the memory span in number of samples of the nonlinear system model. This compares with 7.N/sup 3/ multiplications required for application of the fast Kalman filter with the same approach. A direct implementation of the RLS algorithm has a computational complexity of N/sup 6/. The adaptive filter is successfully used in a second order Volterra system identification in a stationary environment.