AI’s Impact on Automotive Vehicle Design

In the last decade, AI has emerged as a dominant force in automotive engineering, revolutionising the entire process from conception to experience. Gone are the days when car design relied solely on artistry, engineering prowess, and intuition. The introduction of AI has ushered in a new era where algorithms, data, and creativity converge seamlessly. This transformation has redefined automotive design, turning it into a harmonious blend of technological innovation and aesthetic finesse. From generating intricate curves to optimising aerodynamics, AI is at the forefront of reshaping how vehicles are envisioned and brought to life.  

In this blog, we embark on understanding the many ways in which AI is shaping the cars of tomorrow. 

From Breakdowns to Breakthroughs: Predictive Maintenance in Automotive Vehicle Design 

In global automotive, predictive maintenance has witnessed significant advancements in the last few years. Engineers initially gathered and analysed historical data to arrive at strategic decisions. Today, predicitve maintenance enables them to harness the power of machine learning algorithms. ML has the capability to adapt to changing conditions, detect anomalies and predict failures with higher accuracy. In fact, more recent innovations allow data collection and analysis in real time.Digital Twins is one such technology which facilitates precise maintenance predictions by comparing real-time data to virtual representations of physical assets. Its current applications include monitoring critical components like engines and batteries, predicting part failures, and optimising fleet management for commercial vehicles.  

Predictive maintenance has proven to not only saves costs by reducing unplanned downtime but also ensure safer vehicles by preventing accidents due to component failures. Recent statistics suggest a promising future for the automotive predictive maintenance market, with a projected expansion driven by the growing demand for connected mobility, anticipated to grow at a remarkable compound annual growth rate (CAGR) of 28% between 2019 and 2027 according to Transparency Market Research. 

Improving Efficiency with AI 

AI plays a crucial role in optimising power distribution, torque management, and energy efficiency in electric vehicles (EVs). Through dynamic power allocation, AI algorithms analyse sensor data to adjust power distribution between the electric motor and other components based on driving conditions. This includes sending more power to the motor during acceleration and regenerating energy during braking or coasting. AI also manages torque output to ensure smooth acceleration and adaptive torque vectoring for enhanced handling, particularly in corners. Additionally, it utilises predictive energy modeling to estimate energy consumption under different driving scenarios and suggests energy-efficient routes by analysing factors like road gradient and traffic congestion. It monitors battery health and suggests optimal charging times based on user habits and availability of charging stations. AI offers adaptive driving modes to cater to different preferences, adjusting power delivery and energy usage accordingly. Through machine learning and neural networks, it continuously learns from driver behavior to optimise performance, ensuring efficient and personalised driving experiences. 

A Merit expert says, “Investing in AI-driven technologies is not just about staying competitive; it’s about leading the charge in reshaping the future of automotive engineering.” 

Enhanced Safety Features 

Collision avoidance systems in vehicles rely on AI algorithms that analyse data from sensors like radar, lidar, and cameras to detect potential collisions. When a threat is identified, the system takes action autonomously, applying brakes or adjusting steering to avoid accidents. For instance, if a distracted driver fails to notice a pedestrian, the system alerts the driver and initiates emergency braking, potentially preventing a tragic accident. Other safety features include lane-keeping assistance, which gently corrects steering if the vehicle drifts out of its lane, and adaptive cruise control, which adjusts speed to maintain a safe distance from the vehicle ahead. Automatic emergency braking and blind spot monitoring offer additional protection by detecting and responding to potential hazards on the road. Furthermore, cross-traffic alert systems warn drivers of approaching vehicles when reversing out of parking spaces, and pedestrian detection systems identify pedestrians and may apply emergency braking to prevent collisions. These technologies work together to enhance safety on the roads and reduce the risk of accidents. 

Enhanced User Experience 

Personalised content recommendations in modern vehicles are powered by AI algorithms that analyse user preferences and past behavior. These systems intelligently suggest entertainment options like music, podcasts, or audiobooks based on factors such as mood or driving conditions. For instance, after a tiring day at work, the system might recommend calming instrumental music to help unwind. Additionally, AI-driven voice assistants enable hands-free interaction, allowing drivers to control in-car functions with simple commands. These assistants can adjust climate control settings or find nearby restaurants upon request. Advanced navigation systems provide real-time traffic updates and predictive routing, offering alternative routes to avoid congestion. Moreover, safety features are seamlessly integrated into infotainment systems, providing visual and audible alerts for collision avoidance or lane departure. For instance, the system notifies the driver with a visual alert if the car drifts out of its lane without signaling. 

Optimising EV Efficiency 

AI plays a crucial role in optimising battery usage in electric vehicles (EVs). Through dynamic battery management, AI algorithms monitor the battery’s state of charge, temperature, and health, adjusting power delivery to prevent degradation. Predictive state of charge estimation helps drivers plan trips and optimise charging stops. AI also optimises fast-charging protocols, balancing speed and battery stress. It schedules charging during off-peak hours to minimise costs and grid load. Moreover, it enhances energy regeneration by optimising regenerative braking and predicting energy harvesting opportunities. Route optimisation and eco-driving assistance maximise driving range by suggesting energy-efficient routes and coaching drivers on efficient driving techniques. Recent innovations include GBatteries’ dynamic charging waveforms and Stanford University’s AI-driven battery recharging optimisation, improving efficiency and longevity. 

AI in Supply Chain Optimisation 

AI analyses supplier performance data and aids in selecting reliable partners for raw matsrials and parts, ensuring quality and cost-effectiveness. Furthermore, AI monitors global market trends, assisting manufacturers in making informed sourcing decisions amidst currency fluctuations and geopolitical factors. It also facilitates efficient inventory management through AI-driven demand forecasting and just-in-time inventory practices, optimising stock levels while minimising costs.  

In production, AI enhances efficiency by monitoring machinery health and optimising production schedules based on demand and resource availability. AI-driven logistics and distribution systems calculate optimal routes, while real-time tracking ensures timely deliveries. Quality control is improved through automated defect detection and early warning systems, ensuring consistent product quality. Recent innovations include predictive analytics for supply chain disruptions and blockchain integration for enhanced transparency. Additionally, AI contributes to sustainability efforts by optimising material usage and recycling processes, promoting a greener supply chain. 

Lifecycle Management 

Electric Mobility as a Service (eMaaS) is changing the game for electric vehicle (EV) adoption, thanks to artificial intelligence (AI) and data analytics. AI makes managing EV lifecycles easier and more efficient. It sorts through tons of data from different sources, like documents and photos, finding patterns and making sense of it all. This helps us make better decisions based on accurate info. Plus, AI helps cut costs by predicting when maintenance is needed, reducing downtime and repairing bills. With smarter maintenance schedules, EVs become more affordable for everyone. And AI doesn’t stop there—it also fine-tunes EV performance in real-time, making sure they run as efficiently as possible. By balancing energy use and managing charging infrastructure, AI ensures EVs play nice with the grid, making electric mobility more practical and accessible for all. 

In the future, AI will revolutionise automotive design, ushering in a new era of electric vehicles (EVs), software-defined vehicles, and autonomous driving. With AI optimising battery efficiency and range, EVs will become more practical and accessible. Software-defined vehicles will offer enhanced infotainment and driver-assistance features, powered by AI-driven software. The emergence of generative AI will streamline R&D processes, leading to faster innovation and cost reduction. Additionally, AI-powered autonomous vehicles will reshape vehicle design and manufacturing, with projections indicating significant market penetration by 2030. Overall, AI adoption promises to transform the automotive industry, offering smarter, safer, and more sustainable transportation solutions. 

Merit’s Expertise in Data Aggregation & Harvesting for the Global Automotive Sector 

Merit Data and Technology excels in aggregating and harvesting automotive data using AI, ML, and human expertise. Our capabilities include: 

• Crafting end-to-end data pipelines and scalable data warehouses 
• Designing compliant governance solutions for seamless integration 
• Utilising high-volume, high-velocity data tools for nuanced insights 
• Extracting retail product attributes and audience data 
• Aggregating industry-specific data points for informed decision-making 

Trusted by leading automotive brands, Merit drives innovation and efficiency by delivering refined, actionable insights.

Key Takeaways

• Automotive engineering is undergoing a profound transformation due to the integration of AI technology. 
• Predictive maintenance powered by AI is revolutionising how vehicle maintenance is approached, minimising downtime and reducing costs. 
• Electric mobility is being propelled forward by AI, optimising battery efficiency, range, and charging infrastructure. 
• The rise of autonomous driving, enabled by AI, is fundamentally changing vehicle design and manufacturing processes. 
• AI is enhancing efficiency, safety, and sustainability across the automotive industry, paving the way for smarter transportation solutions. 
• AI-driven innovations in automotive supply chain management are streamlining processes and enhancing transparency. 
• The concept of Electric Mobility as a Service (eMaaS) is gaining traction, offering new avenues for accessibility and convenience in transportation. 
• The future of automotive engineering lies in the seamless integration of AI, driving advancements in electric mobility, predictive maintenance, and autonomous driving.