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All Journal Emerging Science Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Christian Harito
Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics

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State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems Ryo G. Widjaja; Muhammad Asrol; Iwan Agustono; Endang Djuana; Christian Harito; G. N. Elwirehardja; Bens Pardamean; Fergyanto E. Gunawan; Tim Pasang; Derrick Speaks; Eklas Hossain; Arief S. Budiman
Emerging Science Journal Vol 7, No 3 (2023): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-03-02

Abstract

The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PDF
Influential Factors Affecting the Adoption Intention of Electric Vehicles in Indonesia: An Extension of the Theory of Planned Behavior Daffa Refor Multi Ray; Christian Harito
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10525

Abstract

The primary goal of the thesis was to examine the factors that affect the willingness of people in Indonesia to adopt Electric Vehicles (EVs). Given the pressing need in Indonesia to address energy shortages and reduce greenhouse gas emissions, this research aimed to investigate the elements that influence people's inclination to use EVs. In this study, questionnaires were used as a means of measurement. Respondents were provided with a brief explanation before completing the survey. Using an extended TPB (Theory of Planned Behavior) model, the research analyzed the adoption intentions of 310 respondents from Indonesia, following a minimum sample guideline of 200. The collected data was analyzed using smartPLS4 to extract insights. The empirical analysis of the research focused on five key factors: attitude, subjective norms, perceived behavioral control, environmental concern, and moral norms. Notably, the empirical results showed that while attitude had an insignificant impact on the adoption intention of EVs in Indonesia, the other factors subjective norms, perceived behavioral control, environmental concern, and moral norms had a significant and positive influence on the intention to embrace electric vehicles in the country. Based on these findings, it can be concluded that the extended TPB model is suitable for predicting the adoption intention of electric vehicles. Considering these results, the study explores the implications for EV adoption in Indonesia, offering valuable insights and recommendations for future research and for the Indonesian government's decision-making process regarding the factors that influence EV adoption.
Co-Authors Arief S. Budiman Bens Pardamean Daffa Refor Multi Ray Derrick Speaks Eklas Hossain Endang Djuana Fergyanto E. Gunawan G. N. Elwirehardja Iwan Agustono Muhammad Asrol Ryo G. Widjaja Tim Pasang