Hicham Chaoui, Ph.D., P.E., SMIEEE

Hicham Chaoui, Ph.D., P.E., SMIEEE

Carleton University

H-index: 33

North America-Canada

About Hicham Chaoui, Ph.D., P.E., SMIEEE

Hicham Chaoui, Ph.D., P.E., SMIEEE, With an exceptional h-index of 33 and a recent h-index of 27 (since 2020), a distinguished researcher at Carleton University, specializes in the field of Control Theory, Robotics, Mechatronics, Motor Drives, Energy Storage.

His recent articles reflect a diverse array of research interests and contributions to the field:

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Enhancing Battery Thermal Management with Virtual Temperature Sensor Using Hybrid CNN-LSTM

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

A Physics-Constrained TD3 Algorithm for Simultaneous Virtual Inertia and Damping Control of Grid-Connected Variable Speed DFIG Wind Turbines

MTPA Speed Control for IPMSM Drives Without Current Sensing

Comparing Hybrid Approaches of Deep Learning for Remaining Useful Life Prognostic of Lithium-ion Batteries

Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

Asynchronous deep reinforcement learning with gradient sharing for State of Charge balancing of multiple batteries in cyber–physical electric vehicles

Hicham Chaoui, Ph.D., P.E., SMIEEE Information

University

Carleton University

Position

Associate Professor

Citations(all)

4228

Citations(since 2020)

2960

Cited By

2248

hIndex(all)

33

hIndex(since 2020)

27

i10Index(all)

82

i10Index(since 2020)

61

Email

University Profile Page

Carleton University

Hicham Chaoui, Ph.D., P.E., SMIEEE Skills & Research Interests

Control Theory

Robotics

Mechatronics

Motor Drives

Energy Storage

Top articles of Hicham Chaoui, Ph.D., P.E., SMIEEE

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Authors

Joseph Omakor,Mohamad Alzayed,Hicham Chaoui

Journal

Energies

Published Date

2024/1

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery’s capacity was conserved at 25.5◦ C, which is the highest operating temperature attained under the proposed strategy.

Enhancing Battery Thermal Management with Virtual Temperature Sensor Using Hybrid CNN-LSTM

Authors

Safieh Bamati,Hicham Chaoui,Hamid Gualous

Journal

IEEE Transactions on Transportation Electrification

Published Date

2024/3/12

Temperature has a significant impact on lithium-ion batteries (LIBs) in terms of performance, safety, and longevity. Battery thermal management system is employed to ensure safe operation of the batteries, especially during fast charging, high power discharge, and extreme weather conditions, thus enhancing their performance and prolonging their lifespan. The thermal performance of batteries is typically monitored using temperature sensors, which directly measure their surface temperature (ST). But, as a battery pack’s number of cells increases, so does its number of temperature sensors, which raises its cost and reduces its reliability. To address this problem, this paper introduces an innovative hybrid method leveraging deep learning algorithm, to accurately estimate the ST of lithium-ion batteries. The methodology integrates convolutional neural network (CNN), long-short term memory (LSTM), and deep neural …

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Authors

Md Shahin Munsi,Hicham Chaoui

Published Date

2024/2/29

As the demand for electric vehicles (EVs) continues to surge, improvements to energy management systems (EMS) prove essential for improving their efficiency, performance, and sustainability. This paper covers the distinctive challenges in designing EMS for a range of electric vehicles, such as electrically powered automobiles, split drive cars, and P-HEVs. It also covers significant achievements and proposed solutions to these issues. The powertrain concept for series, parallel, series-parallel, and complex hybrid electric cars was also disclosed in this study. Much of this analysis is dedicated to investigating the various control strategies used in EMS for various electric vehicle types, which include global-optimization approaches, fuzzy rule based, and real-time optimization-oriented strategies. The study thoroughly evaluates the strengths and shortcomings of various electric vehicle strategies, offering valuable …

A Physics-Constrained TD3 Algorithm for Simultaneous Virtual Inertia and Damping Control of Grid-Connected Variable Speed DFIG Wind Turbines

Authors

Osarodion Emmanuel Egbomwan,Hicham Chaoui,Shichao Liu

Journal

IEEE Transactions on Automation Science and Engineering

Published Date

2024/2/2

This paper proposed a physics-constrained twin delayed deep deterministic policy gradient (TD3) algorithm for simultaneous virtual inertia and damping control of a grid-connected variable speed doubly-fed induction generator (DFIG) wind turbine using a combined deep reinforcement learning (DRL) and quadratic programming as a novel solution to suppress frequency fluctuations caused by the control mechanism which decouples the active power from the system frequency, thus hiding the rotating kinetic energy of the wind generator. The optimization stage modifies the action of the DRL agent, thus preventing the agent from taking certain unsafe actions. We tested the effectiveness of the proposed scheme under various scenarios through simulations on an IEEE 9-bus test system in MATLAB/Simulink. Compared with other virtual inertia controls, the results show that the proposed scheme achieved improved …

MTPA Speed Control for IPMSM Drives Without Current Sensing

Authors

Alaref Elhaj,Mohamad Alzayed,Hicham Chaoui

Journal

IEEE Access

Published Date

2024/1/19

This paper introduces a current sensorless speed control method for interior permanent magnet synchronous motors (IPMSMs) to track the maximum torque per ampere (MTPA) trajectory. Unlike traditional cascaded control structures, current measurements and inner regulation loops are eliminated. Instead, the MTPA is attained by directly adjusting the voltage vector amplitude and angle. An analytical formulation based on the motor voltage model is developed to extract the optimal voltage amplitude to run the motor within the MTPA operating points, disregarding any control law approximation or lookup tables-based numerical solutions. As a result of excluding current measurements and regulation loops, a one-speed controller is required. This leads to a significant reduction in control system complexity. Moreover, the simple structure of the control system highly qualifies it for cost-effective implementation of …

Comparing Hybrid Approaches of Deep Learning for Remaining Useful Life Prognostic of Lithium-ion Batteries

Authors

Anas Tiane,Chafik Okar,Mohamad Alzayed,Hicham Chaoui

Journal

IEEE Access

Published Date

2024/4/29

Many published journals used hybrid deep learning methods to predict batteries’ remaining useful life by adopting different rationales to select and combine deep learning methods aiming to propose the most accurate prediction model possible. The main contribution of this article consists of proposing, to the best of the authors’ knowledge, the most accurate hybrid deep learning prediction model, designed and configured by considering the theoretical strength of each of the selected deep learning models, combined with meticulous data preprocessing and feature engineering steps. A benchmark study is presented to confirm the theoretical design by comparing the prediction results of the selected hybrid model with other proposed hybrid deep learning algorithms. The selected prediction model is compared as well with previously published articles, specifically, the ones that have used hybrid deep learning …

Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

Authors

Yasser Falah Hassan,Mahmood Khalid Hadi Zarkani,Mohammed Jasim Alali,Haitham Daealhaq,Hicham Chaoui

Journal

Mathematics

Published Date

2024/1/11

In this paper, for the first time, a simultaneous design of a model predictive control plan and persistent dwell-time switching signal utilizing the conventional multiple Lyapunov–Krasovskii functional is proposed for linear delayed switched systems that are affected by physical constraints and exogenous disturbances. The conventional multiple Lyapunov–Krasovskii functional with a ‘jump high’ condition is used as a step forward to reduce the strictness of constraints on controller design compared with the switched Lyapunov–Krasovskii functional. However, a dwell-time constraint is inflicted on the switching signal by the ‘jump-high’ condition. Therefore, to decrease the dwell-time limit, the persistent dwell-time structure is used and compared with other structures. Also, a new online framework is proposed to reduce the number of constraints on controller design at each time step. Moreover, for the first time, exogenous disturbances are considered in the procedure of MPC design for delayed switched systems, and non-weighted H∞ performance is ensured. The simulation outcome demonstrates the great performance of the suggested plan and its ability to asymptotically stabilize the switched system.

Asynchronous deep reinforcement learning with gradient sharing for State of Charge balancing of multiple batteries in cyber–physical electric vehicles

Authors

Pengcheng Chen,Shichao Liu,Hicham Chaoui,Bo Chen,Li Yu

Journal

Journal of the Franklin Institute

Published Date

2024/4/1

This work targets the State of Charge (SoC) imbalance issue due to the mismatch across multiple batteries in cyber–physical electric vehicles (EVs) arisen from a variety of practical factors such as manufacturing tolerance, nonuniform aging process and uneven operation temperature. While most of existing SoC balancing approaches are model-based and require accurate prior knowledge, a data-driven asynchronous deep reinforcement learning (DRL) with gradient sharing is proposed to equilibrate the SoC of multiple battery packs in cyber–physical EVs under time-varying operating conditions, and the transmission of control signals in the EVs is formulated by ISO/IEC 15118 standards to ensure the security of the cyber–physical EVs. The gradient sharing (GS) based exploration scheme can expand the exploration space to diversify the actor policies in the training process and in turn reduce the entropy loss …

Direct voltage MTPA speed control of IPMSM-based electric vehicles

Authors

Mohamad Alzayed,Hicham Chaoui

Journal

IEEE Access

Published Date

2023/4/3

A simple maximum torque per ampere (MTPA) method is designed for interior permanent magnet synchronous motors (IPMSMs) with no current control. The proposed method tracks the rotor speed by finding the best pair of voltage angle and amplitude for each motor’s speed and torque condition. This is achieved without stand for any current control loop and using a single controller, which makes the technology simple to a great extent, contrary to the majority of methodologies in the literature. Moreover, a thorough insight analysis is provided to determine analytically the control gains, which simplifies control and tuning and makes it a suitable contender for the development of low-cost PMSM drives. To illustrate the capability of the suggested control method, a comparative study is conducted using the popular MTPA vector control strategy. Experimental results for various situations reveal the ability of the suggested …

Multiparameter Estimation-Based Sensorless Adaptive Direct Voltage MTPA Control for IPMSM Using Fuzzy Logic MRAS

Authors

Alaref Elhaj,Mohamad Alzayed,Hicham Chaoui

Journal

Machines

Published Date

2023/8/28

This paper introduces a parameter-estimation-based sensorless adaptive direct voltage maximum torque per ampere (MTPA) control strategy for interior permanent magnet synchronous machines (IPMSMs). In direct voltage control, the motor’s electrical parameters, speed, and rotor position are of great significance. Thus, any mismatch in these parameters or failure to acquire accurate speed or position information leads to a significant deviation in the MTPA trajectory, causing high current consumption and hence affecting the performance of the entire control system. In view of this problem, a fuzzy logic control-based cascaded model reference adaptive system (FLC-MRAS) is introduced to mitigate the effect of parameter variation on the tracking of the MTPA trajectory and to provide precise information about the rotor speed and position. The cascaded scheme consists of two parallel FLC-MRAS for speed and multiparameter estimation. The first MRAS is utilized to estimate motor speed and rotor position to achieve robust sensorless control. However, the speed estimator is highly dependent on time-varying motor parameters. Therefore, the second MRAS is designed to identify the quadratic inductance and permanent magnet flux and continuously update both the speed estimator and control scheme with the identified values to ensure accurate speed estimation and real-time MTPA trajectory tracking. Unlike conventional MRAS, which uses linear proportional-integral controllers (PI-MRAS), an FLC is adopted to replace the PI controllers, ensuring high estimation accuracy and enhancing the robustness of the control system against sudden …

Minimizing the Operating Cost of a Hybrid Multi-Stack Fuel Cell Vehicle Based on a Predictive Hierarchical Strategy

Authors

Mohammadreza Moghadari,Mohsen Kandidayeni,Loïc Boulon,Hicham Chaoui

Published Date

2023/10/24

The operating cost of multi-stack fuel cell (FC) hybrid electric vehicles (HEVs) is notably affected by the energy management strategy (EMS). For this purpose, this study proposes a predictive hierarchical (PH)-EMS to decrease the multi-stack FC-HEV operating cost. The PH-EMS consists of two levels. The first level is a Sugeno-type fuzzy logic (FL)-EMS that determines the number of active FCs for participation in an optimization level based on the vehicle's future velocity. The second level is the model predictive control (MPC) approach, which distributes the optimal power among FCs and the battery based on the number of active FCs and predicted velocity in the prediction horizon. To evaluate the effectiveness of the proposed EMS, the results are compared to a rule-based (RB) EMS. The results indicate that the total operating cost of the PH-EMS is 55.504% lower compared to RB-EMS.

Introduction to the special section on emerging technologies in navigation, control and sensing for agricultural robots:: Computational intelligence and artificial intelligence …

Authors

Hai Wang,Liqing Chen,Hicham Chaoui,Yue Wang

Published Date

2023/12

Introduction to the special section on emerging technologies in navigation, control and sensing for agricultural robots: : Computational intelligence and artificial intelligence solutions: Computers and Electrical Engineering: Vol 112, No C skip to main content ACM Digital Library home ACM home Google, Inc. (search) Advanced Search Browse About Sign in Register Advanced Search Journals Magazines Proceedings Books SIGs Conferences People More Search ACM Digital Library SearchSearch Advanced Search Computers and Electrical Engineering Periodical Home Latest Issue Archive Authors Affiliations Award Winners More Home Browse by Title Periodicals Computers and Electrical Engineering Vol. 112, No. C Introduction to the special section on emerging technologies in navigation, control and sensing for agricultural robots: Computational intelligence and artificial intelligence solutions editorial Share on …

Energy Efficiency Improvement Using Simplified Dynamic Direct Voltage Maximum Torque Per Ampere Control for Interior PMSMs

Authors

Mohamad Alzayed,Hicham Chaoui

Journal

IEEE/ASME Transactions on Mechatronics

Published Date

2023/3/30

An energy efficiency strategy is developed in this article using a simplified dynamic direct voltage MTPA speed control method for interior permanent magnet synchronous motors (IPMSMs). Finding an exclusive pair of the motor voltage angle and amplitude attains the MTPA trajectory tracking for each reference speed and/or torque condition, maintaining minimum current/power consumption and higher energy efficiency. This improvement is realized by taking into account the dynamic model of the motor, which makes the technology more precise in various operating conditions as contrary to existing literature. Further, the simplified dynamic strategy needs tuning of only couple of parameters compared to three times as much with the classical MTPA FOC technique. Moreover, a comparative study of the simplified dynamic direct voltage MTPA control methodology and the FOC strategy is carried out. Experimental …

Power Quality Improvement of the Grid System with Advanced Hysteresis Controller Design

Authors

Arun Raja Palpandian,Hicham Chaoui

Published Date

2023/8/7

In the power system the use of the nonlinear loads has increased the harmonics leading to serious problems such as low power factor, over heating of the distribution side transformers and disturbance to the load connected to the same point of common coupling. Active Power Line Conditioner with a power circuit of voltage source inverter connected to a DC link capacitor is used to reduce the harmonics. The Performance of the Active Power Line Conditioner depends on the controller used for the switching pulse generation. In this paper Synchronous Reference Frame method is used for the extraction of the reference current and hysteresis current controllers of two types have been used separately for switching pulse generation .The performance of the two types of hysteresis current controller such as fixed Hysteresis current controller and Adaptive Hysteresis current controller for the Active Power Line Conditioner …

Phase Identification in Power Distribution Systems via Feature Engineering

Authors

Nicholas Zaragoza,Hicham Chaoui,Brian Nutter,Vittal Rao

Journal

IEEE Access

Published Date

2023/10/23

Phase identification is the problem of determining the phase connection of loads in a power distribution system. In modern times, utility operators will generally accomplish this using smart meter data that requires some form of feature engineering to achieve practical phase identification using data-driven methods. Feature engineering is essential for voltage magnitude data containing noise, seasonality, and trend. We present crucial components of a feature engineering pipeline to perform linear denoising with Singular Value Decomposition, filtering of the denoised data to remove the seasonality and trend, and fuse multiple meter channels. We use the results of the feature engineering to perform phase label correction, a subproblem of phase identification. To evaluate techniques, the authors generate a synthetic dataset from the meshed IEEE 342-Node test feeder circuit with the 2021 Electric Reliability Council of …

FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems

Authors

Jonathan Plangger,Mohamed Atia,Hicham Chaoui

Journal

Machine Learning and Knowledge Extraction

Published Date

2023/11/23

In this paper, we present a comparative study of modern semantic segmentation loss functions and their resultant impact when applied with state-of-the-art off-road datasets. Class imbalance, inherent in these datasets, presents a significant challenge to off-road terrain semantic segmentation systems. With numerous environment classes being extremely sparse and underrepresented, model training becomes inefficient and struggles to comprehend the infrequent minority classes. As a solution to this problem, loss functions have been configured to take class imbalance into account and counteract this issue. To this end, we present a novel loss function, Focal Class-based Intersection over Union (FCIoU), which directly targets performance imbalance through the optimization of class-based Intersection over Union (IoU). The new loss function results in a general increase in class-based performance when compared to state-of-the-art targeted loss functions.

Fault detection and diagnosis of the electric motor drive and battery system of electric vehicles

Authors

Mohammad Zamani Khaneghah,Mohamad Alzayed,Hicham Chaoui

Published Date

2023/7/5

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

Forecasting the remaining useful life of proton exchange membrane fuel cells by utilizing nonlinear autoregressive exogenous networks enhanced by genetic algorithms

Authors

Yitong Shen,Mohamad Alzayed,Hicham Chaoui

Journal

Journal of Power Sources Advances

Published Date

2023/10/1

The Proton Exchange Membrane Fuel Cell (PEMFC), known for its efficient energy conversion, minimal electrolyte leakage, and low operating temperature, shows great potential as a clean energy source. However, its lifespan is limited due to degradation during normal operation, which, if uncontrolled, can result in dangerous failures such as explosions. Hence, accurately estimating the remaining useful life (RUL) is vital. In this research, a combined prediction method using genetic algorithms (GA) and nonlinear autoregressive neural networks (NARX) with external inputs is proposed. The method's performance was trained and validated using the 2014 IEEE PHM Data Challenge dataset, and it was compared to two commonly used artificial neural network algorithms: GA-based backpropagation neural network (GA-BPNN) and GA-based time delay neural network (GA-TDNN). The findings demonstrate that the …

Prognostic and Health Management of an Aircraft Turbofan Engine Using Machine Learning

Authors

Unnati Thakkar,Hicham Chaoui

Published Date

2023/10/24

An aircraft's turbofan engine is essential to its operation. As with any component of a machine, engine components are subject to degradation and wear over time. This degradation can affect the performance of the engine and may lead to unexpected failures or decreased efficiency. To ensure proper maintenance, predicting its remaining useful life (RUL) is crucial. This paper presents a prediction framework for the RUL of aircraft engines using machine learning (ML) techniques, specifically a Deep Layer Recurrent Neural Network (DL-RNN) model. The proposed method is compared to other ML algorithms, such as Multilayer-Perceptron (MLP), Cascade Forward Neural Network (CFNN), and Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), using two datasets provided by NASA. The results show that the DL-RNN model has better predictive precision compared to the other methodologies.

Adversarial Defensive Framework for State of Health Prediction of Lithium Batteries

Authors

Anas Tiane,Chafik Okar,Hicham Chaoui

Journal

IEEE Transactions on Power Electronics

Published Date

2023/6/23

Neural networks are subject to malicious data poisoning attacks affecting the ability of the model to make accurate predictions. The attacks are generated using adversarial techniques imperceptible to the human eye since they use minimal noise to alter features, which end up affecting boundary decisions of the prediction model. Predicting the state of health (SOH) of lithium-ion batteries in an adversarial context becomes a challenging task, especially if the model is expected to always predict at a very high accuracy level. Our article presents three novel contributions. The first contribution is an SOH prediction model that shows one of the best accuracy rates in the literature ( $R2 = {99.82\%}$ ) and yet uses the simplest long short-term memory model configuration compared to literature. The second contribution is the implementation of three state-of-the-art adversarial data poisoning attacks at decision time, namely …

See List of Professors in Hicham Chaoui, Ph.D., P.E., SMIEEE University(Carleton University)

Hicham Chaoui, Ph.D., P.E., SMIEEE FAQs

What is Hicham Chaoui, Ph.D., P.E., SMIEEE's h-index at Carleton University?

The h-index of Hicham Chaoui, Ph.D., P.E., SMIEEE has been 27 since 2020 and 33 in total.

What are Hicham Chaoui, Ph.D., P.E., SMIEEE's top articles?

The articles with the titles of

Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles

Enhancing Battery Thermal Management with Virtual Temperature Sensor Using Hybrid CNN-LSTM

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

A Physics-Constrained TD3 Algorithm for Simultaneous Virtual Inertia and Damping Control of Grid-Connected Variable Speed DFIG Wind Turbines

MTPA Speed Control for IPMSM Drives Without Current Sensing

Comparing Hybrid Approaches of Deep Learning for Remaining Useful Life Prognostic of Lithium-ion Batteries

Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

Asynchronous deep reinforcement learning with gradient sharing for State of Charge balancing of multiple batteries in cyber–physical electric vehicles

...

are the top articles of Hicham Chaoui, Ph.D., P.E., SMIEEE at Carleton University.

What are Hicham Chaoui, Ph.D., P.E., SMIEEE's research interests?

The research interests of Hicham Chaoui, Ph.D., P.E., SMIEEE are: Control Theory, Robotics, Mechatronics, Motor Drives, Energy Storage

What is Hicham Chaoui, Ph.D., P.E., SMIEEE's total number of citations?

Hicham Chaoui, Ph.D., P.E., SMIEEE has 4,228 citations in total.

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