Technical Insight
Role of AI and ML in the Automotive Sector: A Roadmap for 2022 MUDASSIRKHAN PATHAN
Intangles Lab Pvt. Ltd.
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ith the pandemic leaving a trail of disruption in its wake, the global automotive industry has been quick to adapt and evolve. The most prominent of these transitions is undoubtedly the growing relevance of AI and ML in the automobile sector. With industries around the world pivoting towards digital innovation, the automotive industry is one of the few sectors that is leading this transformation by harnessing these new age tech advancements. Let’s see what role AI and ML are slated to play in the automobile industry in 2022: With Artificial Intelligence and Machine Language taking center stage across numerous arenas in the automobile industry, the most prominent perhaps is the EV sector. In stark contrast to the traditional IC powered engines that are a product of 120 years of research and development, the EV is still at an evolutionary stage and is yet to be fully embraced across mainstream vehicle platforms, especially in a country like India. However, as concerns for planetfriendly and emission free transportation grow deeper, the focus on EVs as the next
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automotive frontier is poised to increase. And at the heart of the EV revolution is the optimization of AI and ML to enhance efficiency and effectiveness where it matters MOST. When it comes to EVs, the most crucial aspect is battery management, hands down. As the single most critical component of electric vehicles, the EV battery is responsible for dictating the overall shelf life of the vehicle. Therefore, EV players are laying deep emphasis on utilizing AI and ML for various battery management metrics like range prediction to gain accurate statistics on effective mileage achievable and health monitoring modules which can gauge the remaining useful life of the battery. When it comes to EVs, battery management facets cannot be compromised. It is important that EV users gain a clear picture of battery performance beforehand to avoid unnecessary hindrances on account of the battery running out. Here AI and ML assay a huge role in determining the battery performance which depends upon a number of variables such as acceleration patterns, deceleration patterns, transmission utilization trends, and use of headlights and
HVAC. The AI models need to contextually track the various behavioral patterns that take a toll on the battery. This includes being intuitive of subtle trends in operating modes which can affect the current output of the battery, such as the exact time when the lights are switched on. The AI and Mlbased predictive system must also include various ambient factors like temperature conditions and humidity levels which can impact the overarching performance of the vehicle. In case of impending battery heating or leakage issues, these next-gen systems can profile factors which lead to chemical discharges and alterations prior to such events. Therefore Machine Learning systems must predict a plethora of both intrinsic and extrinsic factors to precisely estimate battery longevity. A number of deep tech firms are innovating various cutting-edge algorithms to assert accurate range-prediction for heavy duty vehicles. AI and ML based systems are also being utilized for quantifying performance metrics of emergent EV classes such as fullyelectric trucks which are still at an extremely nascent stage. AI and ML are also crucial for making smart recommendations regarding the charging infrastructure for EVs. These intelligent systems can take into account the preset destination and combine that with the nearest charging destination with respect to the runtime battery needs of the vehicle. AI and ML based models can expertly gauge the chemical health of the battery and make appropriate recommendations regarding slow and fast charging. The commonly-held perception is that EVs are less prone to failure than ICE vehicles due to a limited number of moving components. However, a closer look reveals that though the number of moving
