Agent-Based Modeling Framework for Electric Vehicle Adoption Transition in Indonesia

Abstract

Currently, the adoption of electric vehicles (EV) draws much attention, as the environmental issue of reducing carbon emission is increasing worldwide. However, different countries face different challenges during this transition, particularly developing countries. This research aims to create a framework for the transition to EV in Indonesia through Agent-Based Modeling (ABM). The framework is used as the conceptual design for ABM to investigate the effect of agents’ decision-making processes at the microlevel into the number of adopted EV at the macrolevel. The cluster analysis is equipped to determine the agents’ characteristics based on the categories of the innovation adopters. There are 11 significant variables and four respondents’ clusters: innovators, early majority, late majority, and the uncategorized one. Moreover, Twitter data analytics are utilized to investigate the information engagement coefficient based on the agents’ location. The agents’ characteristics which emerged from this analysis framework will be used as the fundamental for investigating the effect of agents’ specific characteristics and their interaction through ABM for further research. It is expected that this framework will enable the discovery of which incentive scheme or critical technical features effectively increase the uptake of EV according to the agents’ specific characteristics.

Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm

“…Selection Method Consideration [6], [28] Agent-based model Patterns in residential EVs ownership and driving activities. [12], [29], [30] TOPSIS Environment, economy, society, electric power system, and transportation system.…”

“…The SC 𝑠(𝑖) and CSC scores 𝑠(𝑖, 𝑛) can vary between -1 and 1, as shown in ( 5) and (6). For each data point 𝑖, 𝑠(𝑖) can range between -1 and 1.…”

“…The results indicate that only financial risk factors and perceived operational break customer intentions to use EVs. Novizayanti et al [6] revealed that the innovation and social media effect allows a new perspective on assessing EV adoption in Indonesia. The cost-benefit analysis [7] indicates that generally, the longer and higher usage span of EVs ownership led to an additionally competitive total cost of ownership (TCO) than internal combustion engine (ICE) in Indonesia.…”

A Systematic Review of the Agent-Based Modelling/Simulation Paradigm in Mobility Transition

“…For example, it was assumed that battery prices drop 18 % yearly. Employing a cluster analysis on technology and incentive-related variables, Novizayanti et al (2021) categorised people into four agent types (innovators, early majority, late majority, and the uncategorised one). Based on preferences, agents competed to select one of the following transportation modes: battery EV, plug-in hybrid EV and fossil fuel vehicle.…”

Design and Optimization of Lightweight Lithium-Ion Battery Protector with 3D Auxetic Meta Structures

“…Recently, lithium-ion batteries have also begun to be used as energy storage in transportation fields, such as electric cars and planes, in the transportation industry's efforts to reduce CO 2 emissions in the environment [2]. According to Novizayanti et al [3], the three most prioritized factors in choosing electric cars in Indonesia are vehicle range, price, and speed. This new field of use and demand from the market poses new safety challenges due to the difference in external elements that the lithium-ion battery will experience during operation and can potentially cause accidents and danger to passengers.…”