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Articles | Advanced Mechanical and Mechatronic Systems | Volume 2 - Issue 2 - 2026 | 106 −121 | https://doi.org/10.53623/amms.v2i2.1040
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Predictive Modeling of Lithium-Ion Battery During Discharging Stage Using CNN-DenseNet and LSTM

Author(s): Mukhidin Wartam 1 ORCID https://orcid.org/0009-0001-6854-443X , Yogi Reza Ramadhan 2 , Hayati Yassin 1 , Taufik Taufik 3 , Ryan Fitriawan 4
Author(s) information:
1 Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link Gadong, Brunei Darussalam
2 Redesma Technologies, Jalan Bulusan VI No 37, Tembalang, Semarang 50277, Indonesia
3 Department of Electrical Engineering, Cal Poly State University, San Luis Obispo, USA
4 PT. PLN Indonesia Power, Jl. Suralaya 21, Suralaya, Kota Cilegon, Banten 42439, Indonesia

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Advanced Mechanical and Mechatronic Systems

Volume 2 - Issue 2 - 2026

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Lithium-ion batteries (LIBs) have become essential to renewable energy technologies, enabling the storage of electricity generated from renewable sources. This study presents an application of Long Short-Term Memory (LSTM) method and CNN-DenseNet to predict the lithium-ion battery during the discharging stage to overcome the crucial problem of battery performance degradation in electric vehicles and energy storage systems. To examine the LSTM model, a specially developed battery monitoring system based on an STM32 microcontroller and a Raspberry Pi 4 microcomputer for data acquisition was used. Testing was done on different discharge patterns at different loads (100W, 130W, 180W, 200W, 220W) on 12V/70Ah lead-acid free-maintenance battery with data collected for 1-10 hours discharge cycles. The monitoring system was designed with voltage sensor, current sensor, and temperature sensor to anticipate disturbances during data capture. The proposed LSTM model achieved superior performance, with RMSE = 0.0847, MSE = 0.0505, and MAE = 0.03548, significantly outperforming the CNN-DenseNet approach (RMSE = 0.3333, MSE = 0.4037, MAE = 0.3051) across diverse testing conditions. Although DenseNet performed best under moderate load (100 W), the LSTM architecture excelled under high load (220 W), underscoring the adaptability of the proposed model across varying operating conditions. This study expands the maintenance framework for energy storage systems through an LSTM architecture offering greater accuracy and stability.

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SUBMITTED: 09 February 2026
ACCEPTED: 01 May 2026
PUBLISHED: 8 June 2026
SUBMITTED to ACCEPTED: 81 days
DOI: https://doi.org/10.53623/amms.v2i2.1040

Cite this article
Wartam, M. ., Reza Ramadhan, Y., Yassin, H. ., Taufik, T. ., & Fitriawan, R. . (2026). Predictive Modeling of Lithium-Ion Battery During Discharging Stage Using CNN-DenseNet and LSTM. Advanced Mechanical and Mechatronic Systems, 2(2), 106 −121. https://doi.org/10.53623/amms.v2i2.1040
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