Today, fossil fuels like coal, oil, and gas are vital to the world's energy production, especially in transportation systems, but they are not renewable and cannot be sustained in the long run (Perkins, 2017). Besides their limited supply, these fossil fuels also cause serious environmental problems like climate change and health issues (Perera, 2018). To address these issues and minimise the effect of fossil fuels in the transportation sector, which is the major source of greenhouse gas emissions—road transport alone accounts for 75% of these emissions (Dhankhar et al., 2024a). That's why governments should take necessary steps and propose initiatives like the Paris Agreement (Paris Agreement, 2018). According to Shahzad and Cheema (2024), road transportation is the main cause of excessive fossil fuel utilisation and CO2 emissions, with 90% of road transportation still relying on fossil fuels. Therefore, it is imperative to address the issue of greenhouse gas emissions and seek alternatives to petroleum. Electrification of the transportation sector is the most prominent solution for reducing dependency on fossil fuels and solving emissions-related problems (Angeline & Rajkumar, 2020).

According to the United States Department of Energy, electric vehicles (EVs) can be externally charged and propelled by an electric motor powered by a battery (U.S. Department of Energy, 2021). Since they can utilize energy from renewable sources, EVs have the potential to significantly reduce CO2 emissions (Mersky et al., 2016). Thus, EVs are considered a viable alternative to traditional vehicles, offering several advantages, such as reduced maintenance costs, environmental friendliness, zero tailpipe emissions, and tax benefits (Alanazi, 2023).

Despite these benefits, the overall demand for EVs remains low compared to traditional vehicles. In the last three years, approximately 63.583 million vehicles (including 4-wheelers, 3-wheelers, and 2-wheelers) have been sold in India. Of these, only 3.543 million vehicles are powered by electricity (Vahan Dashboard, 2024). According to Vahan Dashboard data, most of the 3.543 million electric vehicles sold are 3-wheelers and 2-wheelers. In contrast, electric automobiles, including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs), have reached only 0.80 million in sales over the last three years, accounting for approximately 22.6% of total electric vehicle sales. These statistics indicate that the growth of electric cars has not met government expectations because, due to some disadvantages (range anxiety, battery recycling and limited charging infrastructure) of electric cars, people still prefer traditional vehicles (Husain et al., 2021). Recognising this situation, the Indian government has adjusted its target from achieving 100% EV adoption to 30% by 2030 (Shankar et al., 2019).To addresses these issues, it is crucial to develop new technologies and strategies that encourage the adoption of EVs over traditional vehicles (Ahmed et al., 2021). A study by Dhankhar et al. (2014b) shows a positive demand for EVs in future. That indicates that EVs are the future of the Indian Transportation sector.

Artificial intelligence (AI) recently gained significant attention in automobile industries, and AI has the potential to play a significant role in adopting electric vehicle technology (Lee, 2021). Machines, particularly computer systems, replicate human intelligence procedures through AI. Such procedures consist of learning (the obtaining of data and regulations on how to use it), reasoning (applying rules to reach near or exact conclusions), and self-correction (Dobrev, 2012). AI covers various technologies, such as machine learning, natural language processing, neural networks, and computer vision (Five AI Technologies, n.d.). AI is an emerging technology that is helping to reshape various industries like healthcare (Shaheen, 2021), banking (Boobier, 2020), e-commerce (Bawack et al., 2022), and automobiles (Muradi et al.,2025;Rauf et al., 2024; Ahmed et al., 2021; Abduljabbar et al., 2019). The integration of artificial intelligence in various sectors, especially in the automotive industry, is enormous and varied, as it is a catalyst for significant innovations and breakthroughs.

Integrating AI into electric vehicles transforms vehicle design, manufacturing, and operation. Autonomous driving is one of the major inventions in the automobile sector that can be done through AI (Chai, 2021). Autonomous vehicles, with the help of AI systems, enable vehicles to understand the complex environment, recognise the surrounding objects like- signals, vehicles and other objects and act according to driving-related situations (Nascimento et al.,2019). Along with this, AI plays a significant role in the management of energy, maximum use of batteries and extending the range of EVs (Rauf et al., 2024). Addressing the challenges of limited charging stations and the fear of running out of power (range anxiety) is crucial for encouraging the adoption of electric vehicles (EVs). To encourage the large-scale adoption of EVs, challenges such as range anxiety and limited charging stations must be addressed (Husain et al., 2021). These challenges can be tackled by using AI in EVs. AI also plays an important role in the field of predictive maintenance. By analysing data from multiple sensors, AI can predict vehicle failures before they happen, significantly reducing downtime and associated costs (Arena et al., 2021).

Additionally, AI-based onboard personal assistants provide personalised services for drivers and passengers, offering convenience and satisfaction like never before (Using AI and AR to Enhance Personalized Driving Assistance - Wipro, n.d.). AI is highly relevant in the automobile industry, addressing challenges such as road safety and greenhouse gas emissions while creating future innovations. The use of AI in electric vehicles can make driving smarter and safer. The purpose of this paper is to explore the role of AI in shaping the future of EVs. Its main aim is to examine how AI technologies are integrated into EVs to solve problems such as range anxiety, charging infrastructure, road safety, and greenhouse gas emissions.

Additionally, this paper highlights how AI-driven innovations, such as predictive maintenance and personal assistants, improve EVs' efficiency and user experience. Finally, the study underscores the impact of AI on the adoption of EVs to achieve a sustainable transportation future. Section 2 defines the "Current Applications of AI in EVs." This section discusses current applications of AI, such as autonomous driving, predictive maintenance, energy management, in-car assistants, and enhanced user experiences. Followed by "Future Potential of AI in EVs", are discussed under head section 3. In this head, future techniques of AI are discussed. Furthermore, section 4 discussed the "Challenges and Considerations" This section addresses the challenges and considerations related to the integration of AI in EVs and section 5 discusses strategies for the successful implementation of AI in EVs. In the last section 6 provide the conclusion of study.

AI is an emerging technology that is helping to reshape the automobile industry, especially for EVs. Implementing AI technologies into EVs can significantly boost their performance, improve driver experience, and ensure reliability. Figure 1 show the current applications of AI in EVs. This section discusses the Current Applications of AI in EVs and highlights how these technologies address the existing problem.

Autonomous Driving

Autonomous vehicles are also known as self-driven vehicles (Litman, 2017). Autonomous vehicles can operate without requiring direct driver input to regulate steering, acceleration, and braking. Autonomous vehicles utilize advanced technology to navigate between different destinations, effectively avoiding potential road hazards and promptly adapting to changing traffic conditions (Roberts, 2024). In simple terms, vehicles that do not need any human drivers and control their operation by themselves are called autonomous vehicles. AI acts as the brain of EVs, allowing them to drive autonomously. Tesla is a real-life example of an autonomous vehicle, which is also an electric vehicle. Key AI technologies are included in autonomous EVs. Machine learning helps EVs learn from big datasets and improve their performance over time (Rauf et al., 2024). This algorithm is important for understanding the patterns and forecasting based on the available data, which is important in managing complex road conditions (Ali et al., 2021). Additionally, computer vision helps autonomous EVs visualize the surrounding data through cameras and sensors, which help to identify objects and road signs. Computer vision techniques are vital in increasing EV driving conditions, finding obstacles, and analyzing road traffic conditions (Kanchana et al., 2021).

Furthermore, sensor fusion is a technology that combines data from different sensors, such as cameras, radar, and LiDAR, to give a more accurate and complete understanding of a vehicle's environment. The camera captures the images and identifies objects from the surroundings but may struggle in bed lighting situations. Radar works to detect the speed and distance of objects but doesn't provide images. Along with this, LiDAR provides 3D maps of the surroundings with the help of lasers. Neural Networks is an AI tool that structures human brains. To process the information, it consists of nodes, which are interconnected just like neurons in the brain (Asaii et al., 1996). The main use of these networks is to handle rigid and unstructured data such as images, sound, and raw information. In the context of autonomous EVs, neural networks perform tasks like image recognition and speech recognition.

Driver Assistance Systems

Driver Assistance Systems refer to advanced technologies designed to assist drivers in operating their vehicles safely and more efficiently (Bengler et al., 2014). These systems encompass various features, such as automatic braking and hazard warnings, intended to enhance driving safety and reduce the cognitive load on the driver. AI technologies enable features such as adaptive cruise control (ACC), lane-keeping assist (LKA), and automatic emergency braking (AEB). ACC uses radar and camera data to automatically adjust vehicle speed and maintain a safe distance from the car ahead(Aleksa et al., 2024), while LKA employs computer vision to keep the vehicle centred within its lane, reducing lane departure and driver fatigue (Lai & Yang,2023). AEB uses AI to detect potential collisions and automatically apply the brakes to prevent or mitigate impact (Lindbom & Petersson,2024). These systems rely on AI-driven sensor fusion and machine learning to process data from multiple sensors, creating a comprehensive understanding of the driving environment and continuously improving their predictive capabilities. By integrating these advanced technologies, EVs not only improve safety and reduce driver workload but also make electric vehicles more appealing, contributing to their broader adoption and a more sustainable future in transportation.

Predictive maintenance,

Predictive maintenance is an approach to preventative maintenance that incorporates condition monitoring and machine learning to forecast when machinery could fail (Nunes et al., 2023). It’s powered by artificial intelligence (AI), which plays a crucial role in enhancing reliability and reducing the maintenance costs of EVs. Artificial intelligence (AI) can analyse data collected in real-time from electric vehicle (EV) components, including the battery, motor, and electrical systems, to spot irregularities and predict system problems (Theissler et al., 2021). This pre-emptive approach minimises unexpected breakdowns and extends the lifespan of key components, such as batteries, which are costly to replace (Aravind et al., 2022). As a result, AI-driven predictive maintenance not only lowers maintenance costs but also boosts consumer confidence in the reliability of EVs, addressing one of the main barriers to their adoption (Pamidimukkala et al., 2023). By improving overall vehicle efficiency and reducing the total cost of ownership, predictive maintenance contributes to the wider acceptance of EVs and helps facilitate the transition to sustainable transportation (Pateer et al.,2024a;Smarttech, 2023).

Energy management

Energy management is a crucial application of AI in EVs, enhancing efficiency and addressing key challenges such as range anxiety and battery optimization (Vadera, 2024). AI algorithms can predict energy consumption based on driving patterns, road conditions, and weather data, allowing real-time adjustments to maximize range and performance (Sunaiana  et al.,2025;Zhang et al.,2020). By optimizing battery usage and charging schedules, AI helps reduce overall energy consumption and extend the vehicle’s operational range (Rauf al., 2024;Pateer et al.,2024b). This technology is essential for boosting consumer confidence in EVs by minimizing range limitations and promoting more efficient energy usage, which is vital for sustainable transportation. Additionally, AI-driven energy management systems improve grid integration by adjusting charging based on electricity demand, further enhancing the viability of electric vehicles as a cleaner alternative to traditional combustion engines.

Safety and Security

AI plays a crucial role in improving the safety and security of EVs by enabling advanced surveillance, threat detection, and cyber security measures (Resmi, 2024). AI-powered systems monitor the vehicle’s environment and internal systems, identifying potential threats in real-time, such as suspicious activities or system anomalies, enhancing the vehicle’s overall security. Additionally, AI enhances cyber-security by detecting and responding to cyber-attacks on vehicle software and communication networks, which are increasingly important as EVs become more, connected (Salem et al., 2024). AI also supports accident prevention systems through features like automatic emergency braking, collision avoidance, and adaptive cruise control, reducing the risk of accidents and enhancing road safety. These innovations make EVs safer and more secure, encouraging wider adoption by addressing physical and digital security concerns.

FUTURE POTENTIAL OF AI IN EVS

In India, the adoption of EVs and AI is still in its nascent stage. Both technologies are dynamic and hold immense potential to revolutionize the Indian transportation sector. The future potential of AI in EVs promises significant advancements that could transform the electric vehicle industry. This section explains the future potential of AI in EVs and highlights the anticipated outcomes of AI integration into EV technology.

Advanced Autonomous Driving

In India, autonomous driving is gaining momentum rapidly. With the help of AI algorithms, vehicles can easily manage complex environments with minimum human intervention (Bathla et al., 2022). In future, the adoption of fully 5G technologies can support this evolution by providing the necessary resources for real-time data processing and communication (Attaran, 2023). To enhance the capacity of autonomous EVs and make them more efficient and reliable, integration of AI and 5G technology is necessary.

Smart Grid Integration

AI enables the smooth integration of EVs with smart grids by optimizing energy use and load balancing (Singh et al.,2024). AI ensures efficient energy distribution, reducing strain on the grid by predicting energy demand and managing charging schedules (Shern et al.,2024). This integration supports using renewable energy sources, contributing to a more sustainable energy ecosystem. For example, AI can analyses data to optimize EV charging stations, enhancing the overall efficiency of the smart grid (Rigas et al., 2014).

V2X Communication

AI enhances V2X communication by enabling EVs to interact with each other (Marshell & Jeyaraj,2024). This connectivity improves safety and traffic management by facilitating real-time data exchange on road conditions, traffic signals, and potential hazards (Singh et al.,2021). For example, with the help of V2X technology, drivers can alert about an approaching ambulance, allowing them to decongest the lane for the high-priority vehicle. Such applications are particularly beneficial in India's densely populated urban areas, where traffic congestion is a significant challenge.

Real-Time Traffic Management

Drivers can receive real-time updates through digital signage and smartphone apps because of AI and machine learning's ability to dynamically predict and control traffic flow (Alsrehin et al., 2019). In India's crowded cities, this ability can assist ease traffic, resulting in more effective transport and lower emissions.

Smart Charging Infrastructure

AI ensures effective resource utilization and reduces the burden on the power grid by managing energy distribution and optimizing charging schedules (Ali & Choi,2020). This is especially important for India, where the expansion of intelligent charging infrastructure is essential to the country's broad EV adoption.

AI integration into EVs in India poses several challenges that must be addressed for successful implementation and broad acceptance. These challenges can be categorized into five components: technical, infrastructure, economic, ethical, legal, and social as shown in figure 2.

Technical Challenges

Data Privacy and Security: AI systems in EVs collect and analyses huge amounts of data sets, including personal details and location-based information. Protecting this data from breaches and maintaining user privacy are crucial for maintaining trust and adhering to regulatory requirements. Additionally, robust security measures and transparent data-handling practices are essential to prevent misuse. Ensuring compliance with growing privacy laws further reinforces consumer confidence in AI-powered EV systems.

Algorithm Reliability: One major challenge is creating strong AI algorithms to manage various uncertain driving situations. Ensuring the reliability and safety of these systems under various conditions is critical for user acceptance. Simulation, real-world validation, and continuous testing are crucial to increase the algorithm's performance. Additionally, incorporating fail-safe mechanisms and redundancy can enhance trust in the technology.

Integration with Existing Systems: Integrating AI technology into existing EV designs and infrastructure requires overcoming compatibility and standardization issues. For seamless operation, interoperability between AI-driven systems and traditional components must be achieved. It includes addressing software integration, communication protocols, and hardware compatibility challenges. Furthermore, creating industry-wide standards and encouraging collaboration among manufacturers, technology providers, and legislators helps speed the development of integrated solutions. Effective integration also demands continuous updates and scalability to accommodate future advancements in both AI and EV technologies.

Infrastructure Challenges

Charging Infrastructure: India's existing charging infrastructure is insufficient to sustain the fast-expanding number of EVs. Significant upgrades are necessary to expand coverage, enhance charging speeds, and improve accessibility. Smart charging solutions and vehicle-to-grid (V2G) technologies can improve energy efficiency and reduce grid pressure. Additionally, encouraging public-private partnerships, offering incentives for charging station installations, and integrating renewable energy sources into the infrastructure will help to speed its development and sustainability.

Connectivity Requirements: AI-powered EVs require consistent high-speed internet access for real-time data processing, over-the-air updates, and effective communication with external systems. Inconsistent network connectivity, especially in rural and remote areas, creates a substantial barrier to seamless AI integration. To address this issue, investment in growing network infrastructure, such as 5G deployment, is required to ensure reliable connectivity. Additionally, incorporating edge computing technologies can help reduce reliance on constant internet access by enabling local data processing within the vehicle.

Grid Capacity: The rising demand for EV charging requires substantial upgrades to the electrical grid to prevent overloads and maintain stability. Strengthening grid capacity involves investing in advanced energy management systems, upgrading transmission lines, and integrating renewable energy sources to support the additional load sustainably. Smart grid technology, such as dynamic load balancing and demand response systems, can improve energy distribution while reducing the chance of outages. Collaboration among utility providers, legislators, and EV stakeholders is critical to ensuring the grid can handle the needs of widespread EV adoption.

ECONOMIC CHALLENGES

High Initial Costs: The integration of AI technologies into EVs often results in higher upfront costs, which may alienate price-sensitive Indian consumers. This challenge is especially important in developing countries, where price is a major factor affecting purchasing decisions. To stimulate adoption, manufacturers must produce cost-effective solutions that use economies of scale, optimise production processes, and use local resources. Government subsidies, tax incentives, and financial support schemes can also help offset the high initial costs for consumers. Additionally, educating buyers about the long-term cost savings from reduced fuel consumption and lower maintenance expenses can help justify the initial investment in AI-enabled EVs (Dhankhar et al.,2024c).

Investment in R&D: Substantial investment in R&D is required to tailor AI technology to the Indian market's specific challenges and requirements, such as various road conditions, traffic patterns, and climate fluctuations. However, this can be a significant constraint for emerging companies with limited financial resources. Collaborative efforts, such as partnerships between start-ups, established manufacturers, and academic institutions, can help share the burden of R&D costs. Government funding, innovation hubs, and incubator programs can all help stimulate innovation. Furthermore, focusing on modular and scalable AI solutions enables businesses to expand their products steadily while remaining within budget.

Return on Investment (ROI) Uncertainty: The long-term financial benefits of integrating AI technologies into EVs remain uncertain, leading to cautiousness among stakeholders. This ambiguity is caused by several factors, including the high initial costs, changing consumer tastes and the nascent stage of AI adoption in the automotive industry contribute to this ambiguity. To address these concerns, companies must provide clear estimates of possible return on investment (ROI) via thorough cost-benefit analyses emphasising increased productivity, lower operating expenses, and better user experience. Building trust among stakeholders can also involve showcasing successful case studies, conducting pilot programs, and emphasising the competitive advantages of early adoption in a rapidly evolving market.

Ethical and Legal Challenges

Ethical Dilemmas: Adopting autonomous driving features introduces complex ethical concerns, such as how AI systems should make decisions in critical, life-threatening situations. For instance, determining the priority of human safety versus property damage in unavoidable accidents poses significant moral challenges. Additionally, legal issues regarding liability in case of accidents, whether with the manufacturer, software developer, or vehicle owner, complicate adoption. Addressing these concerns requires establishing clear, universally accepted ethical frameworks and robust legal guidelines and regulations. Public discourse and collaboration among policymakers, technology developers, legal experts, and ethicists are crucial to ensure that AI-driven systems align with societal values while maintaining accountability and transparency

Regulatory Framework: The lack of comprehensive and specific standards for AI integration in EVs creates uncertainties, potentially delaying large-scale implementation. This regulatory gap leaves manufacturers and developers unsure about compliance requirements, safety standards, and data privacy obligations, which can delay innovation and investment. Establishing a strong regulatory framework is critical for providing clarity and fostering stakeholder confidence. This framework should address key areas such as safety protocols, liability in autonomous operations, data security, and ethical considerations. Collaboration between government agencies, industry leaders, and international regulatory bodies is critical to creating uniform standards that encourage innovation while ensuring safety and consumer protection.

Social Challenges

Consumer Skepticism: Many Indian consumers remain sceptical about AI, often questioning its reliability, safety, and potential risks. This scepticism can hinder accepting AI-integrated EVs, especially regarding autonomous driving and data privacy concerns. Building consumer trust is critical for overcoming this obstacle. Manufacturers and stakeholders should demonstrate AI's benefits and safety features to consumers through awareness campaigns, open communication, and hands-on demonstrations. Offering test drives, highlighting real-world success stories, and addressing common misconceptions can all help boost customer confidence. Establishing certifications and endorsements from trusted regulatory bodies can also reassure buyers about the reliability and safety of AI-powered EVs.

Lack of Technical Expertise: AI in EVs requires specialised skills and technological competence, such as machine learning, data analytics, and modern automotive systems. However, India has a huge shortage of skilled personnel in these sectors, making it difficult to create, install, and maintain AI-powered EVs. For the fulfilment of this gap, targeted measures are required, such as establishing specialised training programs, fostering industry-academic relationships, and boosting STEM education with an emphasis on AI and automotive technology. Furthermore, encouraging upskilling and reskilling programs for existing experts can assist in establishing a skilled workforce to meet the expanding demands of the AI-driven EV sector.

Public Awareness: Limited understanding of AI technologies among the general public often leads to resistance, misconceptions, and apprehension, which can negatively impact the adoption rates of AI-integrated EVs. Many consumers may regard AI as extremely complex, dangerous, or obtrusive, exacerbating their concerns. Raising public awareness is critical to closing this knowledge gap, which can be accomplished through focused educational campaigns, interactive seminars, and community engagement programs that explain AI and show its practical benefits. Demonstrating practical applications, emphasising safety features, and addressing common concerns like data privacy can all contribute to consumer trust and acceptance. Collaboration with media, influencers, and educational institutions can also help to broaden outreach and build a more informed public opinion on AI technologies.

Implementation

The implementation of AI-driven EV technologies in India has significant implications for consumers, the government, and automobile companies. For consumers, AI enhances driving safety and convenience through advanced driver-assistance systems (ADAS), predictive maintenance, and real-time energy management. These innovations address key barriers such as range anxiety, charging accessibility, and maintenance costs, making EVs more appealing. AI-powered traffic management and smart navigation further improve efficiency, reducing travel time and energy consumption. For the government, AI-integrated EVs align with India's sustainability goals by reducing carbon emissions and enhancing grid efficiency. AI-driven predictive analytics can optimize urban planning, regulate charging infrastructure, and ensure seamless smart grid integration, thereby promoting widespread EV adoption. Additionally, AI-enhanced cybersecurity measures mitigate risks related to data privacy and autonomous vehicle regulation. The Indian government can leverage AI to formulate evidence-based policies, ensuring efficient subsidy distribution and targeted incentives for EV adoption. For automobile companies, AI opens avenues for innovation in autonomous driving, battery management, and personalized in-car experiences. It enables predictive analytics to improve supply chain efficiency and optimize vehicle design, reducing production costs and enhancing market competitiveness. Additionally, AI-driven insights help manufacturers understand consumer preferences, allowing for tailored marketing strategies and product development. Companies investing in AI-based technologies gain a competitive edge by offering advanced features that cater to the evolving needs of Indian consumers. Overall, the integration of AI in EVs fosters a consumer-friendly ecosystem, supports governmental sustainability initiatives, and accelerates the transformation of India’s automotive industry into a global leader in smart mobility solutions.

Artificial intelligence (AI) is playing a transformative role in shaping the future of electric vehicles (EVs), addressing key challenges while driving advancements in efficiency, safety, and sustainability. The integration of AI in EVs has led to significant improvements in autonomous driving, predictive maintenance, energy management, driver assistance, and vehicle safety, all of which enhance the overall performance and user experience. These AI-driven technologies mitigate major barriers to EV adoption, such as range anxiety, battery optimization, and the availability of charging infrastructure, making EVs a more attractive alternative to traditional vehicles.

Looking ahead, AI has immense future potential in the EV industry. Emerging innovations such as advanced autonomous driving, smart grid integration, vehicle-to-everything (V2X) communication, real-time traffic management, and smart charging infrastructure will revolutionize transportation. AI-enabled smart grids will optimize energy consumption, while real-time traffic and vehicle communication systems will enhance road safety and reduce congestion. These advancements will not only make EVs more efficient but also contribute to a more sustainable and intelligent transportation ecosystem.

Despite its promising prospects, the large-scale adoption of AI in EVs faces several challenges. Technical barriers such as algorithm reliability and cybersecurity risks must be addressed to ensure safe and secure AI integration. Infrastructure constraints, including the lack of widespread charging networks and high-speed connectivity, pose additional hurdles. Economic factors, such as the high initial costs of AI-driven EVs and uncertainty in return on investment, also influence adoption rates. Furthermore, ethical and legal challenges, particularly in autonomous decision-making and regulatory compliance, require clear policies and standardized frameworks. Overcoming social barriers, such as consumer skepticism and a shortage of skilled AI professionals, is equally crucial for seamless AI integration.

The implementation of AI in EVs has significant implications for multiple stakeholders. Consumers will benefit from enhanced driving experiences, improved safety, and lower operational costs through predictive maintenance. Governments can leverage AI for sustainable mobility, reducing carbon emissions while optimizing transportation infrastructure and smart grids. Automobile manufacturers stand to gain a competitive edge by adopting AI-driven innovations that enhance vehicle performance, reduce production costs, and cater to evolving consumer preferences.

In conclusion, AI is not just an enabler but a catalyst for the future of electric vehicles, revolutionizing the transportation industry. While challenges remain, proactive strategies, investment in AI research, and strong policy support will accelerate the transition toward a smarter, safer, and more sustainable EV ecosystem. By addressing these barriers and harnessing AI’s full potential, the EV industry can pave the way for a greener and more intelligent mobility future.

Declarations

Funding: No funding is available for this research

Conflict of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

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