Empowering the Future of Clean Energy, AI, and Smart Electrification

I’m Dr. Md. Tanjil Sarker, a research professor, engineer, and innovator working at the intersection of AI, renewable energy, second-life EV batteries, and smart grid systems.

With a strong background in electrical engineering, system identification, and intelligent energy management, my work focuses on building sustainable, data-driven solutions for modern power systems and next-generation EV infrastructure.
From AI-optimized EV charging networks to advanced battery energy storage systems (BESS), my research and projects aim to create a cleaner, more efficient, and resilient energy ecosystem for communities, industries, and emerging smart cities.

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Tanjil Sarker

Email

tanjilbu@gmail.com

Phone

+60 1116671830​

Time Zone

GMT+8

Education

PhD in Engineering

2018 – 2022

Multimedia University, Malaysia

MSc. in Computer Science and Engineering

2016 – 2017

Jagannath University, Bangladesh

MBA in Human Resource Management

2014-2015

Bangladesh University, Bangladesh

BSc. in Electrical and Electronic Engineering

2009-2013

Bangladesh University, Bangladesh

Experience

Research Professor, PV Energy Storage Lab, Faculty of Artificial Intelligence & Engineering (FAIE), Multimedia University (MMU)

August 2023 – Present

During my research career, I have developed advanced AI-based frameworks for smart EV charging systems, integrating solar PV, wind energy, and battery energy storage systems (BESS) to support high-density residential communities. I engineered secure, AI-driven load management architectures for multi-station EV charging networks with a strong emphasis on cyber-resilience and real-time energy optimization. My work also includes the design and testing of industrial energy-recycling systems, capturing rotational kinetic energy to improve overall energy efficiency. I have actively explored and addressed the key challenges of implementing second-life EV batteries in renewable-integrated micro-grids, including the design, integration, and characterization of battery packs built from diverse SLB cells and modules. In addition, I conducted extensive demand response and power system stability analyses under rapid EV charging scenarios and disruptive grid events, and performed comparative studies between new and second-life EV batteries to evaluate performance, stability, and resilience under dynamic load conditions. My research includes hands-on experimentation with lab-scale micro-grids, validating the feasibility and operational performance of second-life battery storage solutions. Beyond technical development, I also investigated the role of emerging technologies in shaping a sustainable EV battery value chain, while actively participating in academic and professional activities such as journal clubs, case conferences, teaching, institutional meetings, and scholarly programs.

Casual Lecturer, Multimedia University (MMU), Faculty of Engineering

January 2018 – December 2020

As a Casual Lecturer at the Faculty of Engineering, Multimedia University (MMU), I taught a range of core electrical engineering subjects, including Circuit Theory, Control Systems, Power Systems, Power System Protection, Introduction to Machines and Power Systems, and Multimedia Technology. In addition to teaching, I supervised laboratory sessions covering electrical circuits, control systems, and electrical machines, ensuring students gained strong practical and analytical skills. I provided academic and research support to more than 90 students, helping them enhance their understanding of engineering concepts and research methodology. I also supervised multiple final-year undergraduate projects, offering guidance on technical development, experimentation, and documentation to support high-quality research outcomes.

Senior Engineer, Engineering Design and Documentation Department, Reverie Power & Automation Engineering Ltd.

June 2022 – July 2023

As a Senior Engineer in the Engineering Design and Documentation Department at Reverie Power & Automation Engineering Ltd., I led the design and analysis of low-voltage (LV) electrical distribution systems, including single-line diagrams, distribution boards, and protective device coordination. I conducted comprehensive load flow, short-circuit, and protection coordination studies to ensure safe, efficient, and reliable LV system performance. My role required strict adherence to international and local standards such as IEC, NEC, and IEEE, and included reviewing and approving vendor documentation for LV equipment. I supervised and mentored junior engineers, providing guidance on best practices in LV design and quality assurance. Working closely with multidisciplinary teams, I supported the seamless integration of LV systems into broader project frameworks. I also contributed to on-site activities, including construction support, commissioning, and troubleshooting, while preparing detailed technical reports, design specifications, and documentation. Additionally, I played an active role in continuous improvement initiatives and value-engineering efforts to enhance project efficiency and reliability.

Research and Project Overview

Charging the future with intelligence and efficiency

01

AI-driven smart EV charging systems​

This project focuses on developing intelligent EV charging frameworks capable of managing high-density residential and commercial charging environments. The system integrates machine learning algorithms to predict charging demand, optimize power distribution, and minimize grid stress during peak periods. Through real-time data analytics, the framework dynamically schedules charging, balances loads across multiple stations, and enhances energy efficiency. The result is a scalable, cyber-secure EV charging solution designed to support the rapid growth of electric mobility.

02

Grid stability and predictive maintenance using machine learning​

This project applies machine learning to improve power system reliability, focusing on grid stability analysis and predictive maintenance of critical components. By analyzing large datasets from EV charging loads, renewable inputs, and grid disturbances, the system predicts potential failures and identifies anomalies before they escalate. The model enhances operational planning, prevents downtime, and supports a more resilient and intelligent energy grid—especially valuable for modern grids influenced by EV adoption and variable renewable generation.

03

Second-life EV battery integration for home and commercial BESS

This work explores the repurposing of second-life electric vehicle batteries as cost-effective energy storage solutions. The project includes characterization, testing, and system-level integration of diverse SLB modules for both residential and industrial applications. By designing optimized battery packs and evaluating their performance under dynamic load conditions, the project demonstrates how second-life batteries can extend energy access, support solar PV systems, and reduce storage costs while advancing circular economy and sustainability goals.

04

Renewable-based micro-grids with solar and hybrid energy

This project involves the development of renewable-powered micro-grids equipped with solar PV, wind systems, and hybrid energy sources. The focus is on intelligent energy management, where real-time forecasting and optimization algorithms ensure reliable power supply even in remote or off-grid settings. The micro-grids are designed to operate autonomously, maintain system stability, and integrate seamlessly with energy storage technologies, enabling communities and facilities to achieve higher energy independence and resilience.

05

Energy recycling and advanced power optimization

This research explores innovative approaches to capturing untapped industrial energy—specifically rotational kinetic energy—to enhance overall efficiency. The project includes the design and testing of energy-recycling systems capable of converting lost mechanical energy into usable electrical power. Together with advanced optimization techniques, this work offers practical solutions for industries seeking to reduce energy waste, enhance sustainability, and improve operational cost efficiency.

06

AI-Based Load Forecasting and Energy Demand Prediction

This project focuses on developing advanced machine learning models to forecast energy demand across residential, commercial, and remote communities. Using historical consumption patterns, weather data, renewable generation behavior, and seasonal variations, the system predicts short-term and long-term load profiles with high accuracy. These forecasts are used to enhance grid planning, reduce operational costs, and support smarter integration of renewable resources and EV charging infrastructure. The project strengthens energy reliability and helps utilities make data-driven decisions.

07

Battery Health Diagnostics and Lifecycle Evaluation Framework

This work centers on creating a comprehensive diagnostic framework for assessing the health, aging behavior, and performance degradation of new and second-life lithium-ion batteries. By combining AI-based State of Health (SOH) estimation, thermal behavior analysis, and cycle-life testing, the project provides a reliable method for predicting battery failure and optimizing reusability. The framework helps determine which EV batteries are suitable for second-life applications, improves safety, and supports the development of sustainable battery value chains for future energy storage systems

08

Cyber-Resilient Energy Management and Secure Grid Communication Systems

This project focuses on strengthening the cybersecurity and communication reliability of modern energy networks, especially those integrating EV charging, IoT devices, and distributed renewable systems. The work involves designing secure data pathways, intrusion-resistant control architectures, and anomaly-detection algorithms to protect critical energy infrastructures from cyber threats. By combining encryption protocols, real-time monitoring, and resilient communication frameworks, the project ensures uninterrupted operation, reduces system vulnerabilities, and enhances trust in next-generation smart grids.

Publications

Journals:   

  • Sarker, M. T., Hossen, M. S., Ramasamy, G., Al Qwaid, M., Karim, H. A. (2026). SLB-Based Energy Storage for Solar-Integrated Charging Stations in Tropical Regions, Scientific Reports, Accepted for Publication.
  • Sarker, M. T., Sadeque, M.G, Al Qwaid, M., Siddiquee, K.N.A, Karim, H. A. (2026). Modeling and Performance Evaluation of Hybrid SLB-PV Systems for Sustainable Telecom Tower Power Supply in Bangladesh, Energy Report, Accepted for Publication.
  • Sarker, M. T., Ramasamy, G., Al Qwaid, M., Hossen, M. S., & Sadeque, M. G. (2025). AI-driven smart grid optimization for hospital energy systems integrating renewable generation, predictive maintenance, and resilient infrastructure. Scientific Reports, 15(1), 44787. https://doi.org/10.1038/s41598-025-28907-5
  • Sarker, M. T., Ramasamy, G., Al Qwaid, M., & Krishnan, S. (2025). Second-Life EV Batteries for PV–SLB Hybrid Petrol Stations: A Roadmap for Malaysia’s Urban Energy Transition. Urban Science, 9(10), 422. https://doi.org/10.3390/urbansci9100422
  • Abuajwa, O., Thiagarajah, S. P., Ambak, Z., Sarker, M. T., Ramasamy, G., & David, A. P. (2025). Comprehensive review of wireless power transfer systems for electric vehicle charging applications. Discover Applied Sciences, 7(10), 1-49. https://doi.org/10.1007/s42452-025-07738-z
  • Sarker, M. T., Al Qwaid, M., Ramasamy, G., & Haram, M. H. S. M. (2025). Performance Evaluation of Second-Life EV Batteries for Off-Grid Solar Energy Storage System. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3596991
  • Sarker, M. T., Al Qwaid, M., Hossen, M. S., & Ramasamy, G. (2025). Performance Optimization of Grounding System for Multi-Voltage Electrical Installation. Applied Sciences (2076-3417), 15(15). https://doi.org/10.3390/app15158600
  • Hossen, M. S., Sarker, M. T., Nabi, M. S., Bannah, H., Ramasamy, G., & Eng Eng, N. (2025). Federated AI-OCPP Framework for Secure and Scalable EV Charging in Smart Cities. Urban Science, 9(9), 363. https://doi.org/10.3390/urbansci9090363 
  • Sarker, M. T., Al Qwaid, M., Shern, S. J., & Ramasamy, G. (2025). AI-Driven Optimization Framework for Smart EV Charging Systems Integrated with Solar PV and BESS in High-Density Residential Environments. World Electric Vehicle Journal, 16(7), 385. https://doi.org/10.3390/wevj16070385
  • Hossen, M. S., Sarker, M. T., Al Qwaid, M., Ramasamy, G., & Eng Eng, N. (2025). AI-Driven Framework for Secure and Efficient Load Management in Multi-Station EV Charging Networks. World Electric Vehicle Journal, 16(7), 370. https://doi.org/10.3390/wevj16070370
  • Sarker, M. T., Jing, S. W., Ramasamy, G., Thiagarajah, S. P., & Sadeque, M. G. (2025). Energy Recycling System for Harnessing Industrial Rotational Kinetic Energy. Energy Engineering, 122(7). https://doi.org/10.32604/ee.2025.065331
  • Bhuiyan, M. R., Abdullah, J., Hashim, N., Farid, F. A., Isa, W. N. M., Uddin, J., … & Sarker, M. T. (2025). Optical flow and deep learning-based anomaly detection for hajj pilgrimage crowd monitoring. Signal, Image and Video Processing, 19(9), 1-10. https://doi.org/10.1007/s11760-025-04291-5
  • Jing, S. W., Sarker, M. T., Ramasamy, G., Thiagarajah, S. P., & Aman, F. (2025). Industrial Untapped Rotational Kinetic Energy Assessment for Sustainable Energy Recycling. Energy Engineering, 122(3).
  • Shern, S. J., Sarker, M. T., Haram, M. H. S. M., Ramasamy, G., Thiagarajah, S. P., & Al Farid, F. (2024). Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems. Energies, 17(22), 5772. https://doi.org/10.3390/en17225772
  • Shern, S. J., Sarker, M. T., Ramasamy, G., Thiagarajah, S. P., Al Farid, F., & Suganthi, S. T. (2024). Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia. World Electric Vehicle Journal, 15(10), 440. https://doi.org/10.3390/wevj15100440
  • Sarker, M. T., Haram, M. H. S. M., Shern, S. J., Ramasamy, G., & Al Farid, F. (2024). Readiness of Malaysian PV System to Utilize Energy Storage System with Second-Life Electric Vehicle Batteries. Energies, 17(16), 3953. https://doi.org/10.3390/en17163953
  • Sarker, M. T., Haram, M. H. S. M., Shern, S. J., Ramasamy, G., & Al Farid, F. (2024). Second-Life Electric Vehicle Batteries for Home Photovoltaic Systems: Transforming Energy Storage and Sustainability. Energies, 17(10), 2345. https://doi.org/10.3390/en17102345
  • Sarker, M. T., Al Farid, F., Alam, M. J., Ramasamy, G., Karim, H. A., Mansor, S., & Sadeque, M. G. (2024). Analysis of the power sector in Bangladesh: current trends, challenges, and future perspectives. Bulletin of Electrical Engineering and Informatics, 13(6), 3862-3879. https://doi.org/10.11591/eei.v13i6.7503
  • Barman, S., Farid, F. A., Raihan, J., Khan, N. A., Hafiz, M. F. B., Bhattacharya, A., Mahmud, Z., Ridita, S. A., Sarker, M. T., Karim, H. A., & Mansor, S. (2024). Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn. Journal of Imaging, 10(8), 183. https://doi.org/10.3390/jimaging10080183
  • Sarker, M. T., Ramasamy, G., Al Farid, F., Mansor, S., & Karim, H. A. (2024). Energy consumption forecasting: a case study on Bhashan Char island in Bangladesh. Bulletin of Electrical Engineering and Informatics, 13(5), 3021-3032. https://doi.org/10.11591/eei.v13i5.7561
  • Sarker, M. T., Alam, M. J., Ramasamy, G., & Uddin, M. N. (2024). Energy demand forecasting of remote areas using linear regression and inverse matrix analysis. International Journal of Electrical & Computer Engineering, 14(1), (129-139), DOI: http://doi.org/10.11591/ijece.v14i1.pp129-139
  • Al Farid, F., Hashim, N., Abdullah, J., Bhuiyan, M. R., Kairanbay, M., Yusoff, Z., … Sarker, M. T., & Ramasamy, G. (2024). Single Shot Detector CNN and Deep Dilated Masks for Vision-Based Hand Gesture Recognition from Video Sequences. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2024.3360857
  • Sarker, M. T., & Ramasamy, G. (2023). Optimal Signal Design in System Identification for Model Predictive Control (MPC). IEEE Access, 11, (140229 – 140237). DOI: 10.1109/ACCESS.2023.3342024
  • Sarker, M. T., Haram, M. H. S. M., Ramasamy, G., Farid, F. A., & Mansor, S. (2023). Solar Photovoltaic Home Systems in Malaysia: A Comprehensive Review and Analysis. Energies, 16(23), 1-23. DOI: https://doi.org/10.3390/en16237718
  • Haram, M. H. S. M., Sarker, M. T., Ramasamy, G., & Ngu, E. E. (2023). Second Life EV Batteries: Technical Evaluation, Design Framework, and Case Analysis. IEEE Access, 11, (138799 – 138812). DOI: 10.1109/ACCESS.2023.3340044
  • Sarker, M. T., Tan, A. H., & Yap, T. T. V. (2023). Design of Software-Based Optimal Signals for System Identification. IEEE Transactions on Instrumentation and Measurement, 72, (3001810). DOI: 10.1109/TIM.2023.3290297
  • Sarker, M. T., Tan, A. H., & Yap, T. T. V. (2023). Input Spectrum Design for Identification of a Thermostat System. IEEE Access, 11, (2920-2927). DOI: 10.1109/ACCESS.2023.3234255
  • Sarker, M. T., Alam, M. J., & Uddin, M. N. (2023). Auto Intensity Control Technology for Solar Street Lights and Feasibility Study with Traditional System in Bangladesh. International Journal of Engineering and Techniques, 9 (1), (36-44).
  • Cham, C. L., Tan, A. H., Tan, W. H., & Sarker, M. T. (2020). Model predictive control with direct feedthrough with application on a MIST reactor. IFAC-PapersOnLine, 53(1), 183-188.DOI: https://doi.org/10.1016/j.ifacol.2020.06.031
  • Sarker, M. T., Rahman, M. A., & Mahmud, Z. H. (2017). Electricity demand load forecasting for a remote area of Bangladesh. Int. J. Sci. Eng. Res, 8(1), (265-277).
  • Sarker, M. T., Rahman, M. A., Rahman, T., Sarker, A., Sarker, V. K., Zahid, P., & Mahmud, H. (2017). GSM & Microcontroller Based Three Phase Fault Analysis System. International Journal of Advancements in Research & Technology, 6(1), (1-8).

Conferences:

  • Sadeque, M. G., Al Fayshal, M. K., Shaun, M. S. I., Ferdous, A. I., Hasan, M. G., & Sarker, M. T. (2025, January). Design of a High-Efficiency Class-AB RF Power Amplifier for 3.7–4.2 GHz Band. In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) (pp. 1-6). IEEE.
  • Khatun, M. T., Sadeque, M. G., Akter, M. N., Sarker, M. T., Kundu, D., & Ferdous, A. I. (2025, February). Doctor House: A Telemedicine Health Service for People in Remote Areas of Bangladesh. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
  • Sadeque, M. G., Sarker, M. T., Haque, M. L., Bhuiyan, T., Al Farid, F., & Ramasamy, G. (2024, July). Design and Development of a High-Efficiency Boost Converter. In 2024 Multimedia University Engineering Conference (MECON) (pp. 1-5). IEEE.
  • Bari, A., Al Farid, F., Sarker, M. T., Mansor, S., Karim, H. A., Bhuiyan, M. R., & Bannah, H. (2024, July). Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living. In 2024 Multimedia University Engineering Conference (MECON) (pp. 1-6). IEEE.
  • Bari, A., Al Farid, F., Sarker, M. T., Mansor, S., Karim, H. A., Bhuiyan, M. R., & Bannah, H. (2024, July). Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living. In 2024 Multimedia University Engineering Conference (MECON) (pp. 1-6). IEEE.
  • Uddin, M. N., Sarker, T. C., Sefat, S. I., Akter, S., Al Farid, F., Sarker, M. T., … & Mansor, S. (2024, July). A Novel Approach for Tomato Leaf Disease Detection and Classification Using Deep Learning. In 2024 Multimedia University Engineering Conference (MECON) (pp. 1-6). IEEE.
  • Barman, S., Al Farid, F., Sarker, M. T., Hafiz, M. F. B., Khan, N. A., Gope, H. L., … & Bari, A. (2024, July). BNIMI: Boolean Gene Regulatory Network Inference Based on Mutual Information. In 2024 Multimedia University Engineering Conference (MECON) (pp. 1-6). IEEE.
  • Sarker, M. T., Mansor, S., Al Farid, F., Karim, H. A., & Ramasamy, G. (2023, December). Investigation of Optimal Perturbation Signals for Multivariable System under Model Predictive Control. In 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) (pp. 304-309). IEEE. DOI: 10.1109/ICSPC59664.2023.10420073
  • Sarker, M. T., Al Farid, F., Ramasamy, G., Mansor, S., & Karim, H. A. (2023, December). An Overview of System Identification Procedures and Perturbation Signal. In 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC) (pp. 282-287). IEEE. DOI: 10.1109/ICSPC59664.2023.10420174
  • Sarker, M. T., Tan, A. H., & Yap, T. T. V. (2022, June). Performance evaluation of iterative signal design for system identification. In 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 203-208). IEEE. DOI: 10.1109/I2CACIS54679.2022.9815488
  • Sarker, M. T., Tan, A. H., & Yap, T. T. V. (2022, June). Amplitude spectrum design for multivariable system identification in open loop. In 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) (pp. 107-112). IEEE. DOI: 10.1109/I2CACIS54679.2022.9815487

Dr. Tanjil’s Innovation

Award and Recognition

Endorsements​

I’ve had the privilege of collaborating with Dr. Md Tanjil Sarker, PhD, on several impactful research publications, and I’ve always been impressed by his depth of knowledge, strong analytical mindset, and commitment to excellence. Dr. Tanjil brings both technical expertise and creative problem-solving to every project, making our collaborations productive and intellectually rewarding. His professionalism, teamwork, and passion for research truly stand out. I look forward to continuing our joint work and seeing his contributions make an even greater impact in the academic and scientific community.
Has excellent command in AI, Electric Vehicle systems, and sustainable energy. He has published numerous research papers in well-reputed international journals and is truly an expert in these fields. Highly recommended for any collaborative or research engagement.
I had the privilege of working with Dr. Md Tanjil Sarker, an exceptional Research Professor whose expertise in AI, EV systems, and sustainable energy is truly inspiring. His innovative research in smart charging systems and renewable energy integration demonstrates remarkable technical depth and academic leadership. Dr. Tanjil’s vision and dedication make him a driving force in advancing intelligent and sustainable power systems.
Big Names I Worked With​

Contact Me​

tanjilbu@gmail.com​
+601116671830​
Cyberjaya, Selangor, Malaysia​
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