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Development of Real-Time Estimation of Thermal and Internal Resistance for Reused Lithium-Ion Batteries Targeted at Carbon-Neutral Greenhouse Conditions

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  • Muhammad Bilhaq Ashlah

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

  • Chiao-Yin Tu

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

  • Chia-Hao Wu

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

  • Yulian Fatkur Rohman

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

  • Akhmad Azhar Firdaus

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

  • Won-Jung Choi

    (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea)

  • Wu-Yang Sean

    (Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung City 40227, Taiwan)

Abstract

The transition toward renewable-powered greenhouse agriculture offers opportunities for reducing operational costs and environmental impacts, yet challenges remain in managing fluctuating energy loads and optimizing agricultural inputs. While second-life lithium-ion batteries provide a cost-effective energy storage option, their thermal and electrical characteristics under real-world greenhouse conditions are poorly documented. Similarly, although plasma-activated water (PAW) shows potential to reduce chemical fertilizer usage, its integration with renewable-powered systems requires further investigation. This study develops an adaptive monitoring and modeling framework to estimate the thermal resistances ( R u , R c ) and internal resistance (R int ) of second-life lithium-ion batteries using operational data from greenhouse applications, alongside a field trial assessing PAW effects on beefsteak tomato cultivation. The adaptive control algorithm accurately estimated surface temperature ( T s ) and core temperature ( T c ), achieving a root mean square error (RMSE) of 0.31 °C, a mean absolute error (MAE) of 0.25 °C, and a percentage error of 0.31%. Thermal resistance values stabilized at R u ≈ 3.00 °C/W (surface to ambient) and R c ≈ 2.00 °C/W (core to surface), indicating stable thermal regulation under load variations. Internal resistance (R int ) maintained a baseline of ~1.0–1.2 Ω, with peaks up to 12 Ω during load transitions, confirming the importance of continuous monitoring for performance and degradation prevention in second-life applications. The PAW treatment reduced chemical nitrogen fertilizer use by 31.2% without decreasing total nitrogen availability (69.5 mg/L). The NO 3 − -N concentration in PAW reached 134 mg/L, with an initial pH of 3.04 neutralized before application, ensuring no adverse effects on germination or growth. Leaf nutrient analysis showed lower nitrogen (1.83% vs. 2.28%) and potassium (1.66% vs. 2.17%) compared to the control, but higher magnesium content (0.59% vs. 0.37%), meeting Japanese adequacy standards. The total yield was 7.8 kg/m 2 , with fruit quality comparable between the PAW and control groups. The integration of adaptive battery monitoring with PAW irrigation demonstrates a practical pathway toward energy efficient and sustainable greenhouse operations.

Suggested Citation

  • Muhammad Bilhaq Ashlah & Chiao-Yin Tu & Chia-Hao Wu & Yulian Fatkur Rohman & Akhmad Azhar Firdaus & Won-Jung Choi & Wu-Yang Sean, 2025. "Development of Real-Time Estimation of Thermal and Internal Resistance for Reused Lithium-Ion Batteries Targeted at Carbon-Neutral Greenhouse Conditions," Energies, MDPI, vol. 18(17), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4755-:d:1743878
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    References listed on IDEAS

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    1. Allen H. Hu & Chia-Hsiang Chen & Lance Hongwei Huang & Ming-Hsiu Chung & Yi-Chen Lan & Zhonghua Chen, 2019. "Environmental Impact and Carbon Footprint Assessment of Taiwanese Agricultural Products: A Case Study on Taiwanese Dongshan Tea," Energies, MDPI, vol. 12(1), pages 1-13, January.
    2. Yuxin Zhou & Zhengkun Wang & Zongfa Xie & Yanan Wang, 2022. "Parametric Investigation on the Performance of a Battery Thermal Management System with Immersion Cooling," Energies, MDPI, vol. 15(7), pages 1-21, March.
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