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Undoubtedly, AI is gaining momentum as a pivotal component of the automotive sector, with its significance growing steadily each day. The automotive AI market, valued at $2.54 billion in 2021, is projected to surge at a compound annual growth rate (CAGR) of 21.6 per cent from 2022 to 2030.

Electric vehicles not only have zero tailpipe emissions, but they also have a much lower cost of ownership when compared to traditional ICE-powered vehicles. Electric vehicles are less expensive to purchase than petrol or diesel vehicles, and they are also easier to maintain. This is because electric vehicles require fewer components than conventional vehicles.

The main components of an electric vehicle are the battery pack, power control unit, electric motor, gearbox, and battery charger.

There are various types of electric vehicle such as:

AI impact on manufacturing of EV components
The production of batteries and assembly line operations are two areas of EV component manufacturing where AI is crucial to optimising various aspects of the process. AI is particularly good at improving the productivity and calibre of battery manufacturing processes.

Battery management systems (BMS) use AI algorithms to monitor and control vital parameters like battery performance, health, and charging cycles. AI algorithms can anticipate possible problems and optimise energy consumption by evaluating real-time data from sensors integrated into the batteries. This allows the battery packs to last longer. This guarantees EVs long-term dependability in addition to improving their overall performance.

Within the realm of electric vehicle technology, AI is vital for autonomous driving. AI algorithms analyse information from multiple sensors, such as lidar, radar, and cameras, to understand the environment around the vehicle and make appropriate decisions. By handling driving duties, self-driving electric vehicles (EVs) can increase safety, lower accident rates, and offer convenience.

The efficiency and dependability of the infrastructure used for charging electric vehicles can be significantly increased with AI. To reduce wait times and guarantee a flawless charging experience, AI algorithms can be used to optimise charging station locations, forecast demand patterns, and control the charging process. AI can also facilitate communication between EVs and the grid, enabling them to act as energy storage and take part in energy balancing.

Through predictive maintenance algorithms, AI is also revolutionising electric vehicle maintenance procedures. These algorithms detect possible problems before they become failures by analysing real-time data from the vehicle's components. AI makes proactive maintenance possible, minimises downtime, and improves vehicle performance by identifying anomalies and forecasting maintenance requirements.

Additionally, manufacturers can detect possible anomalies or faults in battery components before they become expensive failures thanks to AI-driven predictive maintenance techniques. Manufacturers can maximise production efficiency and reduce downtime by proactively addressing these issues.

Future applications of AI include automated highway systems. Advanced infrastructure, such as specific lines on highways, that help vehicles stay in the lane. AI is expected to become less expensive as it is used more widely. This could help EVs become more affordable for a wider range of people.

AI could help reduce the number of gas-powered vehicles on the road by making electric vehicles more affordable and appealing to consumers.

Benefits of electric vehicles
Electric vehicles have the potential to significantly reduce greenhouse gas emissions and air pollution, making them an attractive solution to environmental pollution. Adoption of electric vehicles provides us with a significant opportunity to reduce pollution. Adopting EVs allows us to significantly reduce greenhouse gas emissions, improve air quality, use renewable energy sources, and promote transportation sustainability.

Challenges in the integration of AI
Initial investment costs: A sizable upfront investment in technology infrastructure, hardware, and personnel training is necessary for the implementation of AI-driven automation systems. These expenses may be unaffordable for small and medium-sized manufacturers, which would hinder adoption.

Workforce adaptation and reskilling: As automation grows, worries about job displacement and the need for workforce reskilling are bound to arise. Workers need to adjust as traditional manufacturing roles change to efficiently operate, monitor, and maintain AI-powered systems. A thorough training programme must be funded to close the skills gap and guarantee a seamless transition for staff members.

Data security and privacy issues: As AI and automation proliferate in the manufacturing sector, new data security and privacy issues arise. Since AI systems are gathering and analysing sensitive production data, manufacturers need to put strong cybersecurity measures in place to protect against possible breaches and unauthorised access.

Integration with legacy systems: A lot of manufacturing facilities use outdated software that might not work with the latest automation and AI innovations. Technical difficulties and careful planning are involved when integrating new systems with the current infrastructure to guarantee smooth interoperability without interfering with current operations.

The use of AI and automation in EV component manufacturing has the potential to revolutionise vehicle production, bringing unprecedented levels of efficiency, precision, and sustainability to the industry. However, realising the full potential of these technologies necessitates addressing a wide range of challenges, including initial investment costs, workforce reskilling, and ethical concerns. Electric vehicles have the potential to significantly reduce greenhouse gas emissions and air pollution. The widespread adoption of electric vehicles has enormous potential to reduce environmental pollution. To meet the charging demand for its electric vehicles, India may need more than four million charging stations by 2030.

As manufacturers embark on this transformative journey, collaboration among industry stakeholders, policymakers, and technology providers is critical to overcoming obstacles and unlocking the transformative power of AI and automation in driving the future of electric mobility. By embracing innovation while adhering to principles of sustainability and inclusivity, the automotive industry can pave the way for a brighter, more efficient, and sustainable future powered by electric vehicles.

ABOUT THE AUTHOR:
Bharath Rao is Founder and CEO of Emobi, a Bengaluru-based electric vehicle (EV) pioneering start up specialising in EV design, frugal-engineering methodologies, and lean manufacturing processes across a diverse range of electric vehicles.