GEMS 2027 Thematic Cluster: AI-Driven EV Smart Mobility and Grid Integration for Sustainable Energy Management in Malaysia and Indonesia

Cities face urgent challenges transitioning to electrified, smart mobility ecosystems amid rising energy costs, geopolitical uncertainty, and rapidly growing urban transport demand. Critical gaps persist in understanding EV charging behaviour relative to mobility patterns, optimising electric microtransit for first-and-last-mile connectivity, and managing real-time Vehicle-to-Grid energy flows integrated with renewable generation forecasting.

Together, the three projects form a thematically unified cluster progressing from demand characterisation and behavioural insight, through fleet deployment optimisation, to smart grid scheduling. Their collective outputs directly inform Malaysia's National Energy Transition Roadmap (NETR, 2023) and Indonesia's clean energy agenda, with tangible impact on infrastructure planning, transport equity, and grid resilience.

The thematic cluster is anchored within the Monash Warwick Alliance Major Research Initiative Fund (MWA) and Monash University Indonesia's Reliable Affordable and Clean Energy (RACE) for 2030, co-supervised across MUM, MUA, and Monash Indonesia, with industry partners including EV Connection Sdn Bhd, AmSolar Sdn Bhd, PT PLN, and Klang Valley public transport operators (e.g., Mara Liner Sdn Bhd, Wawasan Sutera Sdn Bhd).

Impact: A cross-campus Training Academy for Urban Electrification, embedding doctoral networks, ECR capacity-building, and South-to-South knowledge exchange, will position Monash as a regional leader in sustainable energy transition, advancing Impact 2030's Thriving Communities and Climate Change across Southeast Asia.

Project 1 (School of Engineering)

EV charging behaviour driven by network mobility demand

The rapid adoption of electric vehicles (EVs) offers an opportunity to advance sustainable transportation, but its success depends on how charging infrastructure aligns with real-world mobility demand and user needs. This study proposes a data-driven investigation into EV charging behaviour from two perspectives: (1) the alignment between charging station distribution and spatial-temporal mobility demand, and (2) user perceptions of charging accessibility and reliability. Geospatial charging data will be integrated with mobility indicators, such as traffic flows and trip density, to assess how well existing infrastructure reflects demand and to develop measures of accessibility and equity. In addition, supervised learning and natural language processing techniques will be used to analyze user reviews and feedback to classify sentiments regarding reliability, availability, and ease of use. The findings will highlight the mismatches between infrastructure supply and demand and reveal how user experience influences charging behaviour.

For enquiries, please contact Dr Ding Ze Yang

For more information about this project, please visit our GEMS website.

How to Apply

When you apply for admission into your preferred degree program you will be able to select your scholarship type. No separate application is required.

By clicking on a course, you will be directed to further information, including details on ‘How to Apply’.

However, before applying for a GEMS, it is recommended that you first contact the main supervisor for this GEMS research topic. Please provide details of your academic background and achievements to the supervisor so that they can assess your suitability for the GEMS research topic you are interested in.

Main Supervisor (Malaysia):  Dr Ding Ze Yang

Associate Supervisor (Malaysia):  Prof. Tan Chee Pin

Associate Supervisor (Indonesia): Dr Alyas Widita

Project 2 (School of Engineering)

Demand-Driven Electric Microtransit: Multi-Objective Optimisation for Equitable First-Mile/Last-Mile Connectivity

The Malaysian government has recently begun deploying microtransit services to address persistent first-mile/last-mile (FMLM) gaps that continue to constrain public transport ridership. However, existing planning parameters, coverage area, fleet size, seating capacity, and headway, have largely not been derived from empirical demand patterns or traveller characteristics.

This deficiency is compounded by geopolitical tension and rising fuel prices, which are reshaping modal choice and intensifying reliance on public transport across ASEAN cities. This study contends that electric microtransit, integrated operationally and financially within broader public transport network planning, is essential to closing the FMLM gap, with observed shifts in traveller behaviour and spatial coverage deficiencies directly informing electrification sizing and investment decisions.

Building on this premise, the study develops a data-driven, AI-enabled framework for optimising electric microtransit deployment across three objectives: minimising fleet and charging infrastructure costs, maximising FMLM ridership and spatial coverage, and promoting equitable access across socioeconomic and geographic groups. Multi-objective reinforcement learning is employed as the core optimisation method, benchmarked against metaheuristic approaches for validation, to inform public transport electrification planning and microtransit investment strategy adaptable across ASEAN urban contexts.

For enquiries, please contact Dr Susilawati

For more information about this project, please visit our GEMS website.

How to Apply

When you apply for admission into your preferred degree program you will be able to select your scholarship type. No separate application is required.

By clicking on a course, you will be directed to further information, including details on ‘How to Apply’.

However, before applying for a GEMS, it is recommended that you first contact the main supervisor for this GEMS research topic. Please provide details of your academic background and achievements to the supervisor so that they can assess your suitability for the GEMS research topic you are interested in.

Main Supervisor (Malaysia):  Dr Susilawati

Associate Supervisor (Malaysia):Dr Sheena Sara

Associate Supervisor (Australia): Prof. Dong Ngoduy

Associate Supervisor (Indonesia):Prof. Taufiq Asyhari

Project 3 (School of Engineering)

AI-Driven G2V and V2G Coordination for Energy Management

The rapid growth of electric vehicles (EVs) and the integration of renewable energy pose major challenges for grid stability and energy management. Existing vehicle-to-grid (V2G) models struggle to accurately predict available capacity due to uncertainties in EV usage and computational delays in real-time decision-making. A key innovation is the integration of a multi-layered, data-driven forecasting model using Transformer-based algorithms to predict EV arrival/departure times, charging demand, and renewable energy generation. Furthermore, this project will develop an AI-assisted optimization framework for real-time management of EV charging (G2V) and discharging (V2G). Game-theory-based pricing mechanisms and AI-assisted Mixed-Integer Linear Programming (MILP) will be combined to balance user incentives, battery degradation costs, and computational efficiency. The framework will be validated using real-world EV charging, building load, and solar generation data from Malaysia’s commercial and industrial sectors, in collaboration with EV and Solar industry partners.

For enquiries, please contact  Dr Tan Wen Shan

For more information about this project, please visit our GEMS website.

How to Apply

When you apply for admission into your preferred degree program you will be able to select your scholarship type. No separate application is required.

By clicking on a course, you will be directed to further information, including details on ‘How to Apply’.

However, before applying for a GEMS, it is recommended that you first contact the main supervisor for this GEMS research topic. Please provide details of your academic background and achievements to the supervisor so that they can assess your suitability for the GEMS research topic you are interested in.

Main Supervisor (Malaysia): Dr Tan Wen Shan

Associate Supervisor (Malaysia): Assoc Prof Vishnu Monn

Associate Supervisor (Australia): Dr Hao Wang

Associate Supervisor (Indonesia): Assoc Prof Risqi Saputra