Sorin Mihai Radu

By Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Sorin-Mihai Radu and Nicolae Tudoroiuv

The battery state-of-charge estimation is essential in automotive industry for a successful marketing of both electric and hybrid electric vehicles. Furthermore, the state-of-charge of a battery is a critical condition parameter for battery management system. In this research work we share from the experience accumulated in control systems applications field some preliminary results, especially in modeling and state estimation techniques, very useful for state-of-charge estimation of the rechargeable batteries with different chemistries. We investigate the design and the effectiveness of three nonlinear state-of-charge estimators implemented in a real-time MATLAB environment for a particular Li-Ion battery, such as an Unscented Kalman Filter, Particle filter, and a nonlinear observer. Finally, the target to be accomplished is to find the most suitable estimator in terms of performance accuracy and robustness.

Part of the book: New Trends in Electrical Vehicle Powertrains

By Nicolae Tudoroiu, Mohammed Zaheeruddin, Roxana-Elena Tudoroiu and Sorin Mihai Radu

Nowadays, the wavelet transformation and the 1-D wavelet technique provide valuable tools for signal processing, design, and analysis, in a wide range of control systems industrial applications, audio image and video compression, signal denoising, interpolation, image zooming, texture analysis, time-scale features extraction, multimedia, electrocardiogram signals analysis, and financial prediction. Based on this awareness of the vast applicability of 1-D wavelet in signal processing applications as a feature extraction tool, this paper aims to take advantage of its ability to extract different patterns from signal data sets collected from healthy and faulty input-output signals. It is beneficial for developing various techniques, such as coding, signal processing (denoising, filtering, reconstruction), prediction, diagnosis, detection and isolation of defects. The proposed case study intends to extend the applicability of these techniques to detect the failures that occur in the battery management control system, such as sensor failures to measure the current, voltage and temperature inside an HEV rechargeable battery, as an alternative to Kalman filtering estimation techniques. The MATLAB simulation results conducted on a MATLAB R2020a software platform demonstrate the effectiveness of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.

Part of the book: Wavelet Theory

By Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Nicolae Tudoroiu and Sorin Mihai Radu

This research paper will propose an incentive topic to investigate the accuracy of an adaptive neuro-fuzzy modeling approach of lithium-ion (Li-ion) batteries used in hybrid electric vehicles and electric vehicles. Based on this adaptive neuro-fuzzy inference system (ANFIS) modeling approach, we will show its effectiveness and suitability for modeling the nonlinear dynamics of any process or control system. This new ANFIS modeling approach improves the original nonlinear battery model and an alternative linear autoregressive exogenous input (ARX) polynomial model. The alternative ARX is generated using the least square errors estimation method and is preferred for its simplicity and faster implementation since it uses typical functions from the MATLAB system identification toolbox. The ARX and ANFIS models’ effectiveness is proved by many simulations conducted on attractive MATLAB R2021b and Simulink environments. The simulation results reveal a high model accuracy in battery state of charge (SOC) and terminal voltage. An accurate battery model has a crucial impact on building a very precise adaptive extended Kalman filter (AEKF) SOC estimator. It is considered an appropriate case study of a third-order resistor-capacitor equivalent circuit model (3RC ECM) SAFT-type 6 Ah 11 V nominal voltage of Li-ion battery for simulation purposes.

Part of the book: Smart Mobility

By Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Nicolae Tudoroiu, Sorin Mihai Radu and Hana Chammas

This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for “proof concept” and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems.

Part of the book: Electric Vehicles