i-manager Publications

i-manager's Journal on Electrical Engineering (JEE)

Current Issue Vol. 18 Issue 2

EV Battery SOC Estimation Method for Adaptive Power Management
Integrated Microcontroller-Based Hybrid Electric Vehicle with Multi-Source Non-Conventional Energy Harvesting and Fuzzy Logic-Based Energy Storage System Control
Deep Learning-Based Image Processing Approach for Irradiance Estimation in MPPT Control of Photovoltaic System
Optimized Grid-Connected Solar Inverter Design with Advanced Maximum Power Point Tracking
A LIFI Technology Based Vehicle-To-Vehicle Communication Strategy for Reducing Vehicle Accidents

Most Cited

Volume 18 Issue 2 October - December 2024

Research Paper

EV Battery SOC Estimation Method for Adaptive Power Management

Niharika Purohit* , Shruti Tiwari**

* Department of Power System, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

** Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Purohit, N., and Tiwari, S. (2024). EV Battery SOC Estimation Method for Adaptive Power Management. i-manager’s Journal on Electrical Engineering, 18(2), 1-13. https://doi.org/10.26634/jee.18.2.21478

Abstract

Accurate State of Charge (SOC) estimation is essential for effective power management in Electric Vehicles (EVs), as it directly impacts battery performance, energy efficiency, and driving range. This paper presents an adaptive SOC estimation method using the Coulomb counting method, implemented in MATLAB, aimed at optimizing EV battery power management. The proposed method integrates real-time battery modeling and dynamic filter adaptation to account for varying operational conditions, such as load fluctuations, temperature changes, and battery degradation. A simplified battery model, incorporating current and voltage data, is used to simulate the SOC, with the Coulomb counting method employed to refine the estimation based on noisy measurements. The method adapts its parameters in response to environmental changes, enhancing the accuracy of SOC predictions. MATLAB simulations demonstrate the effectiveness of the adaptive Coulomb counting method, showing improved SOC estimation accuracy and more efficient power management compared to traditional techniques. This adaptive approach ensures better performance in real-world driving scenarios, contributing to extended battery life and optimized energy consumption in EVs.

References

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[8]. Chen, B. C., Guan, J. C., & Li, J. H. (2014). Adaptive power management control of range extended electric vehicle. Energy Procedia, 61, 67-70.

[9]. Deng, J., Bae, C., Denlinger, A., & Miller, T. (2020). Electric vehicles batteries: Requirements and challenges. Joule, 4(3), 511-515.

[10]. Duraisamy, T., & Kaliyaperumal, D. (2020). Active cell balancing for electric vehicle battery management system. International Journal of Power Electronics and Drive Systems, 11(2), 571.

[11]. Hossain, M. S., Kumar, L., Islam, M. M., & Selvaraj, J. (2022). A comprehensive review on the integration of electric vehicles for sustainable development. Journal of Advanced Transportation, 2022(1), 3868388.

[12]. Hwang, F. S., Confrey, T., Reidy, C., Picovici, D., Callaghan, D., Culliton, D., & Nolan, C. (2024). Review of battery thermal management systems in electric vehicles. Renewable and Sustainable Energy Reviews, 192, 114171.

[13]. Indlekofer, T., Faure-Beaulieu, A., Noiray, N., & Dawson, J. (2021). The effect of dynamic operating conditions on the thermoacoustic response of hydrogen rich flames in an annular combustor. Combustion and Flame, 223, 284-294.

[14]. Kang, L., & Shen, H. (2021, October). An electric vehicle battery state-of-health estimation system with aging propagation consideration. In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS) (pp. 473-481). IEEE.

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[17]. Lipu, M. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., & Mahlia, T. I. (2021). Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production, 292, 126044.

[18]. Moore, E. A., Russell, J. D., Babbitt, C. W., Tomaszewski, B., & Clark, S. S. (2020). Spatial modeling of a second-use strategy for electric vehicle batteries to improve disaster resilience and circular economy. Resources, Conservation and Recycling, 160, 104889.

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[20]. Paul, R. (2022, April). Electric vehicle cell balancing using single and multi tiered switched capacitor. In 2022 4th International Conference on Energy, Power and Environment (ICEPE) (pp. 1-6). IEEE.

[21]. Sandeep, R., & Hariprasad, A. (2020). Battery management system in electric vehicles. International Journal of Engineering Research & Technology (IJERT), 9(5).

[22]. Shah, N. L., & Kurchania, A. K. (2023). Comparative analysis of predictive models for SOC estimation in EV under different running conditions. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 5, 100207.

[23]. Sulaiman, M. H., & Mustaffa, Z. (2024). State of charge estimation for electric vehicles using random forest. Green Energy and Intelligent Transportation, 100177.

[24]. Vairavel, M., Girimurugan, R., Shilaja, C., Loganathan, G. B., & Kumaresan, J. (2022, November). Modeling, validation and simulation of electric vehicles using MATLAB. In AIP Conference Proceedings, 2452 (1). AIP Publishing.

[25]. Verasamy, M., Faisal, M., Ker, P. J., & Hannan, M. A. (2018). Charging and discharging control of Li-Ion battery energy management for electric vehicle application. International Journal of Engineering & Technology, 7(4), 482-486.

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Research Paper

Integrated Microcontroller-Based Hybrid Electric Vehicle with Multi-Source Non-Conventional Energy Harvesting and Fuzzy Logic-Based Energy Storage System Control

Krishna Sarker* , Brijit Roy, Jayanti Sarker*, Indranil Kushary****

* Department of Electrical and Electronics Engineering, St. Thomas' College of Engineering and Technology, West Bengal, India.

Department of Electrical and Electronics Engineering, JIS College of Engineering, Kalyani, West Bengal, India.

* Department of Electrical and Electronics Engineering, Techno Main Salt Lake, West Bengal, India.

**** Department of Electrical and Electronics Engineering, Technique Polytechnic Institute, West Bengal, India.

Sarker, K., Roy, B., Sarker, J., and Kushary, I. (2024). Integrated Microcontroller-Based Hybrid Electric Vehicle with Multi-Source Non-Conventional Energy Harvesting and Fuzzy Logic-Based Energy Storage System Control. i-manager’s Journal on Electrical Engineering, 18(2), 14-29. https://doi.org/10.26634/jee.18.2.21498

Abstract

This paper addresses the urgent need for environmentally sustainable transportation by introducing an advanced Microcontroller-Based Electric Vehicle (EV) model. The model aims to optimize energy consumption, improve reliability, and promote environmental sustainability. It incorporates self-driving technology, utilizing a Joystick, Bluetooth device, and Ultrasonic Sensor for obstacle detection, with the Microcontroller receiving feedback signals that influence motor operation based on proximity to obstacles. To enhance efficiency, the model integrates non-conventional energy sources, including Solar and Wind Energy. A Solar Panel, control circuit, and DC to DC Converter capture solar energy to power the vehicle through an Arduino UNO and Motor Driver L298. A Weight-Based Electric Energy Consumption module ensures optimal energy use. The vehicle's battery can be charged using a USB Cable or Solar Panel, with the TP4056 charge controller managing the transition between charging and discharging. In case of emergencies, Wind Energy serves as an alternative power source. To address power fluctuation, an Energy Storage System (ESS) within the wind turbine's DC link, supported by supercapacitors, smooths the wind-generated power, mitigates voltage variations, and improves fault tolerance. A fuzzy logic-based control scheme efficiently integrates the wind turbine and ESS conditions, with validation performed through computational simulations. The proposed model offers significant advancements in sustainable electric vehicle technology, providing flexibility, reliability, and adaptability to various energy scenarios.

References

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[3]. Bahmani, M. H., Shayan, M. E., & Fioriti, D. (2024). Assessing electric vehicles behavior in power networks: A non-stationary discrete Markov chain approach. Electric Power Systems Research, 229, 110106.

[4]. Chang, T. W. (2023). An indispensable role in promoting the electric vehicle Industry: An empirical test to explore the integration framework of electric vehicle charger and electric vehicle purchase behavior. Transportation Research Part A: Policy and Practice, 176, 103824.

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[8]. Faisal, S., Soni, B. P., Goyal, G. R., Bakhsh, F. I., Husain, D., & Ahmad, A. (2024). Reducing the Ecological Footprint and charging cost of electric vehicle charging station using renewable energy based power system. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 7, 100398.

[9]. Han, R., He, H., Zhang, Z., Quan, S., & Chen, J. (2024). A multi-objective hierarchical energy management strategy for a distributed fuel-cell hybrid electric tracked vehicle. Journal of Energy Storage, 76, 109858.

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[11]. Longhitano, P. D., Bérenguer, C., & Echard, B. (2024). Joint electric vehicle routing and battery health management integrating an explicit state of charge model. Computers & Industrial Engineering, 188, 109892.

[12]. Ma, M., & Pinsky, E. (2024). Using machine learning to identify primary features in choosing electric vehicles based on income levels. Data Science and Management, 7(1), 1-6.

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[14]. Meng, W., Song, D., Huang, L., Chen, X., Yang, J., Dong, M., & Elkholy, M. H. (2024a). Distributed energy management of electric vehicle charging stations based on hierarchical pricing mechanism and aggregate feasible regions. Energy, 291, 130332.

[15]. Nasab, M. A., Al-Shibli, W. K., Zand, M., Ehsan-maleki, B., & Padmanaban, S. (2024). Charging management of electric vehicles with the presence of renewable resources. Renewable Energy Focus, 48, 100536.

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[18]. Pasha, J., Li, B., Elmi, Z., Fathollahi-Fard, A. M., Lau, Y. Y., Roshani, A., & Dulebenets, M. A. (2024). Electric vehicle scheduling: State of the art, critical challenges, and future research opportunities. Journal of Industrial Information Integration, 38, 100561.

[19]. Patil, L. N., Bhosale, D. G., Patil, Y. M., Bhagat, G. P., Patil, A. A., Satpute, S. S., & Agrawal, V. K. (2024). Investigation on different parameters associated with purchase of electric vehicle in India. Case Studies on Transport Policy, 15, 101152.

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[25]. Zhu, G., Tian, Y., Liu, M., Zhao, Y., Wang, W., Wang, M., & Xie, K. (2023). Comprehensive competitiveness assessment of ammonia-hydrogen fuel cell electric vehicles and their competitive routes. Energy, 285, 129471.

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Research Paper

Deep Learning-Based Image Processing Approach for Irradiance Estimation in MPPT Control of Photovoltaic System

Dammu Arun Kumar* , Rasabattula Koti Babu**

*-** Department of Electrical and Electronics Engineering, DVR and Dr. HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India.

Kumar, D. A., and Babu, R. K. (2024). Deep Learning-Based Image Processing Approach for Irradiance Estimation in MPPT Control of Photovoltaic System. i-manager’s Journal on Electrical Engineering, 18(2), 30-36. https://doi.org/10.26634/jee.18.2.21507

Abstract

Renewable energy significantly contributes to power generation to meet the growing energy demand. It is sourced from solar, wind, hydroelectric, and other sources. Among these, solar energy is considered the most suitable due to its cleanliness and its ability to directly convert sunlight into electrical power through solar photovoltaic (PV) modules. One of the biggest challenges with solar panels is the random fluctuation of their power output due to variations in irradiance. The concept of maximum power point tracking (MPPT) techniques has been introduced to address this non-linear behavior of PV systems and optimize their efficiency. Various MPPT techniques have been proposed, based on both conventional and intelligent methods. In this work, a novel image processing-based MPPT technique is introduced to enhance the efficiency of PV systems. The irradiance level is accurately classified using the self-learned EfficientNetB0 deep learning model. The parameters of the EfficientNetB0 model are adjusted using Tuna Swarm Optimization. The results show that the tracking efficiency is higher compared to other intelligent MPPT techniques. The classification accuracy of the proposed learning model surpasses that of conventional models.

References

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[2]. Al-Smadi, M. K., & Mahmoud, Y. (2020). Image-based differential power processing for photovoltaic microinverter. IEEE Transactions on Energy Conversion, 36(2), 619-628.

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[5]. Bouakkaz, M. S., Boukadoum, A., Boudebbouz, O., Attoui, I., Boutasseta, N., & Bouraiou, A. (2020, September). Fuzzy logic based adaptive step hill climbing MPPT algorithm for PV energy generation systems. In 2020 International Conference on Computing and Information Technology (ICCIT-1441) (pp. 1-5). IEEE.

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[11]. Huynh, D. C., & Dunnigan, M. W. (2016). Development and comparison of an improved incremental conductance algorithm for tracking the MPP of a solar PV panel. IEEE Transactions on Sustainable Energy, 7(4), 1421-1429.

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[17]. Padmanaban, S., Priyadarshi, N., Bhaskar, M. S., Holm-Nielsen, J. B., Ramachandaramurthy, V. K., & Hossain, E. (2019). A hybrid ANFIS-ABC based MPPT controller for PV system with anti-islanding grid protection: experimental realization. IEEE Access, 7, 103377-103389.

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[19]. Ye, B., Qi, J., Li, Y., Xie, L., & Yang, F. (2017, November). Research on PV array output characteristics based on shadow image recognition. In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) (pp. 1-6). IEEE.

[20]. Zakzouk, N. E., Elsaharty, M. A., Abdelsalam, A. K., Helal, A. A., & Williams, B. W. (2016). Improved performance low cost incremental conductance PV MPPT technique. IET Renewable Power Generation, 10(4), 561-574.

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Research Paper

Optimized Grid-Connected Solar Inverter Design with Advanced Maximum Power Point Tracking

Nimain Charan Nayak*

Department of Electrical and Electronics Engineering, Misrimal Navajee Munoth Jain Engineering College, Thoraipakkam, Chennai, India.

Nayak, N. C. (2024). Optimized Grid-Connected Solar Inverter Design with Advanced Maximum Power Point Tracking. i-manager’s Journal on Electrical Engineering, 18(2), 37-49. https://doi.org/10.26634/jee.18.2.21546

Abstract

This paper introduces a high-performance, single-stage inverter design tailored for grid-connected photovoltaic (PV) systems. The proposed configuration not only amplifies the typically low voltage of PV arrays but also efficiently converts solar-generated DC power into high-quality AC power for grid integration, while maximizing power extraction from the PV array. The system ensures that the total harmonic distortion of the grid-fed current remains within acceptable limits. Key advantages of the proposed topology include enhanced PV array utilization, improved efficiency, reduced cost, and a compact design. Furthermore, the inherent design ensures that the PV array operates as a floating source relative to the grid, significantly improving system safety. This study also provides a comprehensive review of existing single-stage topologies for grid-connected PV systems, offering a detailed comparison with the proposed design. A thorough steadystate analysis is included, along with a step-by-step design methodology and formulas for calculating peak device stresses. Additionally, the required condition for the modulation index in sinusoidal pulse-width modulation control under discontinuous conduction mode is derived. Analytical, simulation, and experimental results are provided to validate the performance of the proposed system.

References

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[3]. Asiminoaei, L., Teodorescu, R., Blaabjerg, F., & Borup, U. (2005). Implementation and test of an online embedded grid impedance estimation technique for PV inverters. IEEE Transactions on Industrial Electronics, 52(4), 1136-1144.

[4]. Barbosa, P. G., Braga, H. A. C., Rodrigues, M. D. C. B., & Teixeira, E. C. (2006). Boost current multilevel inverter and its application on single-phase grid-connected photovoltaic systems. IEEE Transactions on Power Electronics, 21(4), 1116-1124.

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Research Paper

A LIFI Technology Based Vehicle-To-Vehicle Communication Strategy for Reducing Vehicle Accidents

Rasabattula Koti Babu* , Nallani Venu Babu**

*-** Department of Electrical and Electronics Engineering, DVR and Dr HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India.

Babu, R. K., and Babu, N. V. (2024). A LIFI Technology Based Vehicle-to-Vehicle Communication Strategy for Reducing Vehicle Accidents. i-manager’s Journal on Electrical Engineering, 18(2), 50-55. https://doi.org/10.26634/jee.18.2.21572

Abstract

This paper presents an innovative approach to Vehicle-to-Vehicle (V2V) communication utilizing Light Fidelity (Li-Fi) technology. Effective communication between vehicles is essential for reducing auto accidents and serves as a crucial component of intelligent transportation systems. Traffic accident detection has emerged as a critical area of focus due to its significant role in improving road safety. This study explores advanced accident detection mechanisms using Li-Fi technology, providing a sophisticated solution to enhance human safety by preventing accidents. The proposed system integrates multiple sensors, including ultrasonic and alcohol detectors, to monitor and respond to various scenarios. When vehicles are in close proximity, the system alerts the driver of the vehicle ahead, ensuring increased driver awareness and promoting safer driving conditions.

References

[1]. Haas, H., Yin, L., Chen, C., Videv, S., Parol, D., Poves, E., & Islim, M. S. (2020). Introduction to indoor networking concepts and challenges in LiFi. Journal of Optical Communications and Networking, 12(2), A190-A203.

[2]. Haas, H., Yin, L., Wang, Y., & Chen, C. (2016). What is LiFi?. Journal of Lightwave Technology, 34 (6), 1533–1544.

[3]. Javale, D., Sashittal, C. A., Wakchaure, S., Phadnis, A. M., Patil, S. S., & Shahane, R. S. (2018, August). A new approach to wireless data transmission using visible light. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-4). IEEE.

[4]. Mosaif, A., & Rakrak, S. (2017, November). An indoor wireless visual sensor network basing on light-fidelity communication. In 2017 International Conference on Wireless Networks and Mobile Communications (WINCOM) (pp. 1-4). IEEE.

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i-manager's Journal on Electrical Engineering (JEE)

Current Issue Vol. 18 Issue 2

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Volume 18 Issue 2 October - December 2024

EV Battery SOC Estimation Method for Adaptive Power Management

Niharika Purohit* , Shruti Tiwari**

* Department of Power System, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India. ** Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Purohit, N., and Tiwari, S. (2024). EV Battery SOC Estimation Method for Adaptive Power Management. i-manager’s Journal on Electrical Engineering, 18(2), 1-13. https://doi.org/10.26634/jee.18.2.21478

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Integrated Microcontroller-Based Hybrid Electric Vehicle with Multi-Source Non-Conventional Energy Harvesting and Fuzzy Logic-Based Energy Storage System Control

Krishna Sarker* , Brijit Roy**, Jayanti Sarker***, Indranil Kushary****

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Deep Learning-Based Image Processing Approach for Irradiance Estimation in MPPT Control of Photovoltaic System

Dammu Arun Kumar* , Rasabattula Koti Babu**

*-** Department of Electrical and Electronics Engineering, DVR and Dr. HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India.

Kumar, D. A., and Babu, R. K. (2024). Deep Learning-Based Image Processing Approach for Irradiance Estimation in MPPT Control of Photovoltaic System. i-manager’s Journal on Electrical Engineering, 18(2), 30-36. https://doi.org/10.26634/jee.18.2.21507

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Optimized Grid-Connected Solar Inverter Design with Advanced Maximum Power Point Tracking

Nimain Charan Nayak*

Department of Electrical and Electronics Engineering, Misrimal Navajee Munoth Jain Engineering College, Thoraipakkam, Chennai, India.

Nayak, N. C. (2024). Optimized Grid-Connected Solar Inverter Design with Advanced Maximum Power Point Tracking. i-manager’s Journal on Electrical Engineering, 18(2), 37-49. https://doi.org/10.26634/jee.18.2.21546

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A LIFI Technology Based Vehicle-To-Vehicle Communication Strategy for Reducing Vehicle Accidents

Rasabattula Koti Babu* , Nallani Venu Babu**

*-** Department of Electrical and Electronics Engineering, DVR and Dr HS MIC College of Technology, Kanchikacherla, Andhra Pradesh, India.

Babu, R. K., and Babu, N. V. (2024). A LIFI Technology Based Vehicle-to-Vehicle Communication Strategy for Reducing Vehicle Accidents. i-manager’s Journal on Electrical Engineering, 18(2), 50-55. https://doi.org/10.26634/jee.18.2.21572

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* Department of Power System, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

** Department of Electrical and Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Krishna Sarker* , Brijit Roy, Jayanti Sarker*, Indranil Kushary****