AI-Enabled Solutions to Challenges in Electric Vehicle Charging Infrastructure in Pakistan: A Systematic Review

Abstract

Pakistan has set a target of achieving 30% electric vehicle (EV) penetration by 2030; however, limited and inefficient Electric Vehicle Charging Infrastructure (EVCI) remains a critical barrier to this transition.  This study investigates the key technical, financial, regulatory, and social challenges hindering EVCI deployment in Pakistan through a Systematic Literature Review (SLR) of 55 peer-reviewed studies published between 2008 and 2024 from databases such as Google Scholar, IEEE Xplore, ScienceDirect, and Mendeley using a PDCP framework, complemented by a focus group study involving nine experts (n = 9) from academia, industry, and policy sectors. The SLR scope covers publications related to EV infrastructure planning, grid integration, policy frameworks, and AI-enabled optimization to identify key challenges and solution pathways.  Ten critical challenges were identified, including insufficient charging stations, high upfront costs, grid instability, range anxiety, battery supply constraints, policy uncertainty, and workforce limitations. In order to overcome these obstacles, the paper suggests AI-based solutions that combine machine learning, reinforcement learning, digital twins, predictive analytics, and dynamic pricing. Such tools assist in the optimal location of charging stations, load forecasting, subsidy targeting, grid stability, and battery life cycle management under Pakistan’s National Electric Vehicle Policy (NEVP) 2019 and New Energy Vehicles Policy 2025-30. The results suggest that combining policy reforms with AI-driven decision-making can speed up scalable, resilient, and sustainable EVCI development, rebranding Pakistan’s EV transition into a data-driven and adaptive national strategy towards its 2030 climate objectives.