State of Health Aware Adaptive Scheduling of Battery Energy Storage System Charging and Discharging in Rural Microgrids Using Long Short-Term Memory and Convolutional Neural Networks

Chi Nghiep Le, Arangarajan Vinayagam, Phat Thuan Tran, Stefan Stojcevski, Tan Ngoc Dinh (), Alex Stojcevski and Jaideep Chandran
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Chi Nghiep Le: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Arangarajan Vinayagam: Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru 560103, India
Phat Thuan Tran: Department of Computer Science, University of Science-VNUHCM, Ho Chi Minh City 700000, Vietnam
Stefan Stojcevski: Department of Computer Science and Information Technology, School of Computing, Engineering and Mathematical Science, La Trobe University, Bundoora, VIC 3083, Australia
Tan Ngoc Dinh: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
Alex Stojcevski: Singapore Campus, Curtin University, Singapore 117684, Singapore
Jaideep Chandran: School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

Energies, 2025, vol. 18, issue 21, 1-25

Abstract: This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R 2 ; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications.

Keywords: state of charge; state of health; battery energy storage system; adaptive scheduling; smart grid; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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