AI-based Predictive Analytic Approaches for safeguarding the Future of Electric/Hybrid Vehicles
This work tackles cybersecurity and sustainability challenges for electric/hybrid vehicle manufacturers and users, but it appears incremental as it applies existing AI methods to a specific domain without introducing new paradigms.
The paper addresses cybersecurity vulnerabilities in electric/hybrid vehicles (EHVs), such as remote hijacking and unauthorized access, and proposes using AI-based predictive analytics to improve energy efficiency, reduce emissions, and enhance sustainability.
In response to the global need for sustainable energy, green technology may help fight climate change. Before green infrastructure to be easily integrated into the world's energy system, it needs upgrading. By improving energy infrastructure and decision-making, artificial intelligence (AI) may help solve this challenge. EHVs have grown in popularity because to concerns about global warming and the need for more ecologically friendly transportation. EHVs may work better with cutting-edge technologies like AI. Electric vehicles (EVs) reduce greenhouse gas emissions and promote sustainable mobility. Electric automobiles (EVs) are growing in popularity due to their benefits for climate change mitigation and sustainable mobility. Unfortunately, EV production consumes a lot of energy and materials, which may harm nature. EV production is being improved using green technologies like artificial intelligence and predictive analysis. Electric and hybrid vehicles (EHVs) may help meet the need for ecologically friendly transportation. However, the Battery Management System (BMS) controls EHV performance and longevity. AI may improve EHV energy efficiency, emissions reduction, and sustainability. Remote hijacking, security breaches, and unauthorized access are EHV cybersecurity vulnerabilities addressed in the article. AI research and development may help make transportation more sustainable, as may optimizing EHVs and charging infrastructure.