Hicham Chaoui’s research while affiliated with Texas Tech University and other places

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Publications (224)


Advances in hospital energy systems: Genetic algorithm optimization of a hybrid solar and hydrogen fuel cell combined heat and power
  • Article

October 2024

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25 Reads

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4 Citations

International Journal of Hydrogen Energy

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R. Elkhatib

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Enhancing Energy Management Strategy for Battery Electric Vehicles: Incorporating Cell Balancing and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient Architecture

July 2024

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31 Reads

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1 Citation

IEEE Transactions on Vehicular Technology

This paper introduces a real-time multi-objective adaptive Energy Management Strategy (EMS) based on a MultiAgent Reinforcement Learning (MARL) architecture. Leveraging Twin Delayed Deep Deterministic Policy Gradient (TD3) methods, this EMS continuously monitors the system, striking a balance between front and rear electric drive operations, cell balancing in batteries, and other crucial parameters affecting battery aging. It not only meets driver requirements but also determines the optimal power levels for Electric Motors (EMs), reducing battery depletion and aging. Validation employs a 2021 Motor Vehicle Challenge model with two electric motors. Results indicate the advantages of the proposed EMS, meeting driver power needs across diverse environmental conditions. Furthermore, it achieves a final state of charge (SOC) within a mere 0.3% deviation from the Dynamic Programming (DP) approach. The EMS excels by effectively balancing battery cells and optimizing temperature, mitigating long-term battery aging. Importantly, it outperforms the highest reported SOC value in the 2021 Motor Vehicle Challenge while satisfying all specified criteria.


Figure 1. Comparison between different methods of primary and secondary control strategies in recent years.
Figure 2. Grid-connected mode.
Figure 3. Islanded mode.
Figure 12. Schematic of LFC in two-area power system [114].
A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications
  • Article
  • Full-text available

June 2024

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130 Reads

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6 Citations

Energies

The escalating adoption of low-carbon energy technologies underscores the imperative to transition from conventional fossil fuel-dependent sources to sustainable alternatives. The expansion of Distributed Energy Resources (DERs) signifies an essential shift towards a more resilient and environmentally friendly energy landscape. However, integrating inverter-based DERs introduces challenges, particularly in system inertia and grid instability. This review delves into the critical area of inverter-based grid control strategies, focusing on the primary and secondary control mechanisms. Primary controls are investigated, including traditional droop control and low-voltage ride-through (LVRT) capability. The secondary control strategies, involving virtual impedance (VI) and load frequency control (LFC), are vital in maintaining grid stability and reliability are reviewed. The aim is to offer a comprehensive understanding of the principles, advancements, and challenges associated with inverter-based grid controls, contributing valuable insights for the seamless integration of DERs into modern power grids.

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

April 2024

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81 Reads

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7 Citations

Energies

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.



Figure 2. The input of the drinking water supply network. Figure 2. The input of the drinking water supply network.
Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

January 2024

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50 Reads

Mathematics

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.


FIGURE 5. Schematic of improved battery cell
FIGURE 6. Evolution of scientific literature based on electric vehicle powertrain control systems
FIGURE 10. Depicting the development of global optimization-based approaches over time
FIGURE 11. The percentage of three real-time optimization-based journals published since 2019
FIGURE 12. Block diagram representation of MPC controller
Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

January 2024

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1,286 Reads

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15 Citations

IEEE Access

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 insights into their practical implementation and effectiveness across different EV models, such as BEVs, HEVs, and PHEVs.


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

January 2024

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13 Reads

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1 Citation

IEEE Transactions on Automation Science and Engineering

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 dynamic performance with the lowest system frequency deviation and fastest frequency recovery under wind and load variations and severe grid faults. A further test on the IEEE 39-bus system shows that the grid size does not affect the performance of our proposed technique. Note to Practitioners —Integrating the wind turbine systems into the utility grid results in power quality problems such as frequency fluctuation, voltage dip, power loss, and severe power outages. The unpredictability and uncontrollability of the wind pose a serious problem in integrating wind energy conversion systems. This problem becomes worse with the increasing number of connected wind turbines. Therefore, new control strategies are required to mitigate this issue. The droop-based virtual and damping control method traditionally provides frequency support in grid-tied wind turbine systems. However, the fixed droop gain is a significant drawback of this method. In this paper, we proposed a novel physics-constrained deep-reinforcement learning-based virtual inertial and damping control. The proposed control agent is constrained from unsafe actions and rewarded for maintaining the grid frequency within operational limits. Simulation results with the IEEE 9 bus system validated the feasibility and effectiveness of our proposed approach. A comparison of our method with the conventional control scheme, adaptive droop-based virtual inertia control, etc., carried out under various operational scenarios verified the enhanced performance of our proposed strategy.


Citations (56)


... In particular, as the number of synchronous generators decreases, concerns regarding frequency stability increase owing to the reduction in power system inertia. When a fault occurs in a low-inertia system, the sudden frequency drop and low-frequency nadir can trigger unexpected protection operations and increase the risk of blackouts [6][7][8][9]. Accordingly, transmission system operators (TSOs) worldwide have focused on reorganizing new electricity markets and supplying additional facilities to secure grid reliability owing to the high penetration of IBRs [10][11][12][13][14][15]. ...

Reference:

Estimation of Quantitative Inertia Requirement Based on Effective Inertia Using Historical Operation Data of South Korea Power System
A Review of Control Techniques for Inverter-Based Distributed Energy Resources Applications

Energies

... Among them, Li-ion is widely used in EVs because of its high power density and low cost. However, Li-ion suffers from low overcharge tolerance, and therefore Li-ion requires higher safety protection installed in EVs [6,7]. ...

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

IEEE Access

... This algorithm makes use of an ANN to optimize the parameters of a fuzzy controller. ANNs are also implemented for improving the droop control performance in [50] by means of a feedforward neural network (FFNN) architecture for islanded and grid-connected modes. In this study, the FFNN algorithm substitutes the conventional virtual impedance control loop by learning the transient inverter's non-linear model characteristic. ...

A Novel Bi-Directional Grid Inverter Control Based on Virtual Impedance Using Neural Network for Dynamics Improvement in Microgrids
  • Citing Article
  • January 2024

Power Systems, IEEE Transactions on

... A semi-active HESS involves one storage device connected to the DC Bus via a bi-directional DC-DC converter. An active HESS is defined by the connection of two storage devices through bi-directional DC-DC converters to the DC Bus, as discussed in the literature [21][22][23][24]. ...

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

Energies

... LSTM [33] is a specialized type of Recurrent Neural Network (RNN) that excels at capturing long-term dependencies in sequential data. Traditional RNNs often face challenges such as gradient vanishing or exploding when processing long time series. ...

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

IEEE Access

... [30][31][32][33][34] 2 | LITERATURE SURVEY In this literature survey, we delve into a comprehensive analysis of recent research studies focusing on PCM-based TMS techniques. By synthesizing findings from a range of studies references, [35][36][37][38][39][40][41][42][43][44] we aim to provide insights into the diverse approaches and methodologies employed in this field. This examination seeks to elucidate the current state-of-the-art in PCM-based TMS, identify key trends, and highlight areas for further exploration and development. ...

Enhancing Battery Thermal Management with Virtual Temperature Sensor Using Hybrid CNN-LSTM
  • Citing Article
  • December 2024

IEEE Transactions on Transportation Electrification

... However, as the number of optimization goals increases, so do the constraints, which significantly increases the computational burden on the controller. 116 Additionally, these methods require precise mathematical modeling of the system, as inaccuracies in the model can reduce the accuracy of the optimization results. ...

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

IEEE Access

... Further, the training process aims to minimize this loss function by adjusting the model's weights using optimization algorithms, such as Stochastic gradient descent (SGD) or Adam. These optimization techniques help in minimizing the difference between the predicted and actual stability states, improving the model's ability to accurately classify V/F stability [62][63][64][65][66]. ...

A Physics-Constrained TD3 Algorithm for Simultaneous Virtual Inertia and Damping Control of Grid-Connected Variable Speed DFIG Wind Turbines
  • Citing Article
  • January 2024

IEEE Transactions on Automation Science and Engineering

... While comprehensive, these models often face limitations in predictive accuracy due to issues such as numerical formulations, input data, and parameterizations errors. In contrast, datadriven approaches have gained popularity for their simplicity, speed, and cost-effectiveness (Khaneghah et al. 2023). By analyzing historical data, these models uncover patterns and relationships between predictors and PM 2.5 concentrations, enabling predictions based on temporal sequences of key variables. ...

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

... Due to its high energy conversion efficiency, low operating temperature, and environmental friendly by products, PEMFCs are considered potential power generation system for various applications [33]. PEMFCs have garnered significant attention for portable, stationary power generation and transportation applications [28]. However, performance degradation in PEMFCs presents substantial challenges for effective implementation, particularly in applications requiring high reliability, extended lifespans, and low maintenance costs. ...

Forecasting the remaining useful life of proton exchange membrane fuel cells by utilizing nonlinear autoregressive exogenous networks enhanced by genetic algorithms
  • Citing Article
  • October 2023

Journal of Power Sources Advances