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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Anoune, Kamal
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Reduce state of charge estimation errors with an extended Kalman filter algorithm El Maliki, Anas; Benlafkih, Abdessamad; Anoune, Kamal; Hadjoudja, Abdelkader
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp57-65

Abstract

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.
Estimating the state of charge of lithium-ion batteries using different noise inputs El Maliki, Anas; Anoune, Kamal; Benlafkih, Abdessamad; Hadjoudja, Abdelkader
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp8-18

Abstract

State of charge estimation (SOC) is the most significant functionality of a vehicle's battery management system (BMS). The methods for this estimation are conventionally oriented towards model-based methods. As part of this paper, we introduce a first order equivalent circuit estimation approach known as the Thevenin model, along with an extended Kalman filter (EKF) approach to accurately estimate the SOC. We then deploy and simulate it in MATLAB by using a reference load profile from the new European driving cycle (NEDC). Afterwards, the simulation results are reviewed based on various initial noise values, and the results are compared to those of other EKF algorithms. According to the results, SOC estimation accuracy has significantly increased as a result of the improvements made. Specifically, the root-mean-square error decreased from 0.0068 to 0.0020.
Maximizing energy efficiency in drones through accurate state of charge estimation using extended Kalman filter Anoune, Kamal; Maliki, Anas El; Belkasmi, Merouan
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i3.pp755-767

Abstract

This paper delves into the critical aspect of managing energy consumption in drone operations to achieve the utmost range and ensure accurate state of charge (SoC) estimation. Effective energy management is pivotal in determining the operational range of drones, allowing for longer distances and heavier payloads. The integration of precise energy estimation algorithms into operational planning extends the range of drones, facilitating swift, environmentally-conscious missions for sustainable and efficient logistics solutions. The paper introduces a mathematical model to understand energy consumption and battery behavior in drones, utilizing the hybrid pulse power characterization test and recursive least square with forgetting factor for parameter identification. To overcome the limitations of linear filters, the paper employs the accurate extended Kalman filter (EKF) in the nonlinear filter section. The EKF significantly enhances the battery management system by furnishing precise SoC data. The study evaluates two SoC estimation techniques: SoC-AH (ampere-hours) and SoC_EKF, using root mean square error for comparison. The SoC_EKF technique demonstrates higher accuracy, boasting a lower errors value of 0.78%, thus making it superior for precise drone battery SoC estimation. These findings contribute to the improved performance, reliability, and overall safety of drones.
Empowering industry through energy auditing: a case study of savings and sustainability Anoune, Kamal; Ghazi, Mohamed; Ghazouani, Mokhtar; Nasiri, Badr; Zebraoui, Otmane
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i4.pp952-962

Abstract

Conducting energy audits is pivotal in assessing industrial plant efficiency and formulating effective energy management plans. It identifies opportunities for efficient energy use, reducing costs and environmental impact. This study employs a techno-economic approach to analyze electricity cost reduction in an industrial facility. Through energy auditing, it explores economic benefits and improved energy quality, yielding favorable outcomes. Focused on a plastic derivative manufacturing plant, the study reveals critical audit findings. The main aim is to identify avenues for electric energy savings, cutting production costs, and enhancing product competitiveness. The audit involves a detailed analysis of consumption patterns, signal quality, and potential energy management strategies, culminating in a cost-cutting plan. The results of an economic assessment of the suggested energy-saving strategies, provide a comprehensive evaluation of their financial implications. It reveals significant cost reduction opportunities, estimating annual energy savings of $45,824.56, which represents a 23.68% decrease in expenses. These initiatives not only boost the plant's financial performance but also strengthen its competitive edge.
Co-Authors Belkasmi, Merouan Benlafkih, Abdessamad El Maliki, Anas Ghazi, Mohamed Ghazouani, Mokhtar Hadjoudja, Abdelkader Maliki, Anas El Nasiri, Badr Zebraoui, Otmane