In recent years, the automotive industry has been heavily driven by the “experience” aspect. Artificial intelligence (AI) has played a crucial role in this change, with the automotive industry progressively becoming data-driven – from vehicle manufacturing to connectivity.
AI-augmented IoT systems collect massive volumes of data, which are then processed and applied to improve the offerings. Self-driving cars are the industry’s best example of AI-driven systems. In fact, the worldwide autonomous vehicle market is predicted to increase from 21 million units in 2021 to 65 million units by 2030.
AI offers a wide range of applications in the automobile industry. Its features that enable predictive maintenance have a much bigger influence today than it did previously. Today’s automotive sector employs AI in various ways and processes, ranging from design and supply chain to production and post-production. In the transportation and logistics industries, driver assistance systems are most used. The aftermarket services industry, such as maintenance and insurance, are also adopting AI-powered systems to provide more comprehensive and data-driven offerings.
Five key applications of AI in the automotive industry
AI-powered designs and systems can be seen beyond self-driving automobiles with the rise of Industry 4.0. Today, every automaker’s goal is to leverage AI/ML to cut production costs, improve product quality, increase efficiency, accelerate product development, and create a more sustainable ecosystem. The automotive industry is becoming increasingly data-driven, and the data collected is being used to continuously improve internal systems and the overall driver experience.
Driverless Vehicles: Semi-autonomous and fully driverless vehicles have become increasingly popular in recent years. Tesla, Ford, and other big brands have successfully developed self-driving vehicles, and many more are on their way. Self-driving cars are predicted to revolutionize the logistics and transportation industry by eliminating a massive amount of manual labor and the risks that come with it. In fact, driverless vehicles are predicted to account for 40% of all distance traveled in Europe by 2030. AI-powered autonomous vehicles have a promising future, from Tesla’s “autopilot” mode to a variety of other brands offering AI-assisted features like automatic parallel parking.
Driver Assistance: AI developments have enabled automakers to scale up driver assistance technologies and provide customers with a more personalized and predictive driving experience. Identifying road conditions while driving, pedestrian behavior, and traffic flow are just a few of the top features many automakers are adding to their vehicles today. Such systems aid drivers in taking the path of least resistance, avoiding traffic congestion, driving safely, and alerting drivers to risks such as collisions with other vehicles/objects in their path, sudden lane changes, and weather conditions.
Connected Vehicle Platforms: Self-driving cars and connected car platforms would struggle without one another. Automakers are still looking for ways to manufacture more of the latter. Because of the popularity of IoT in the automotive industry, connectivity is rapidly evolving. It would allow the industry to concentrate on interoperability and ensure that all systems function effectively without jeopardizing driver safety and security. In this circumstance, near-perfect communication is essential – thanks to faster and more reliable networks, that is no longer a challenge. Additionally, over-the-air problem detection and resolution can significantly boost customer satisfaction. See how HELA, Zimetrics’ connected vehicle platform, helps automakers ace their vehicle connectivity.
Battery Management Systems: Electric vehicles (EVs) have gained popularity in recent years, particularly in India, as a sustainable solution to the future of automobiles. Did you know that the battery accounts for 25% of the total cost of an electric vehicle? Increasing battery life through regular optimization is the most crucial component of enhancing EV performance. AI is making battery technology better and has made it possible to charge an electric vehicle (EV) in the time it takes to stop at a gas station.
AI systems analyze the battery usage in EVs and how often they are charged. It then builds a mathematical model to optimize the fast-charging capabilities without shortening the life of the battery. This improves battery performance, battery life cycle management, driving range, charging time, and the life of the vehicle. It can also help speed up battery research by making it easier to find and test better materials for Lithium batteries.
AI algorithms examine the battery utilization and charging frequency of electric vehicles. It then creates a mathematical model to improve the fast-charging capabilities while preserving the battery’s life. This increases battery performance, life cycle management, driving range, charging time, and vehicle life. It can also potentially help manufacturers accelerate and innovate battery development by making it easier to identify and test better materials as an alternative to Lithium batteries.
ProEV Edge is an AI-based intelligent battery management system by Zimetrics that has helped many EV manufacturers optimize their battery performance.
Vehicle Manufacturing: Vehicle manufacturing is one of the key areas where AI technologies are utilized to improve the design and maintain production unit quality. For example, IoT, AI, and ML technologies provide predictive maintenance of the equipment. Robotics and mechatronics enable automakers to automate monotonous activities with Computer Vision, allowing engineers to focus on more vital jobs. Machine Learning is used on modern shop floors to increase driver safety by employing deep learning algorithms to improve quality control. Instead of checking a random number of units, they can verify each one of them.
AI technologies are also being employed more and more to improve global supply chains. With cognitive forecasts and recommendations, AI-powered supply chains improve company performance. Automobile manufacturers reap numerous benefits, including improved and data-driven decision-making and enhanced exposure. ML algorithms assist them in identifying areas for improvement. Demand forecasting, vehicle prototyping, and warehouse sorting are some of the primary use cases of AI in the automotive manufacturing sector today.
Conclusion
Vehicle production and distribution have increased dramatically in the post-pandemic years, with EVs garnering the most headlines. With growing investments in AI-powered systems throughout the automotive industry, transportation and logistics have great potential to become truly intelligent.
Zimetrics is a data technology company that has helped various automakers leverage next-gen technology solutions in various stages of the manufacturing process. To learn more about our unique solutions for the automotive industry and how we can help you with your technology needs, initiate a conversation with us today.
