The application of new-age tech paradigms like Artificial Intelligence and Machine Language is disrupting every industry. Using state-of-the-art AI and ML-powered predictive tools and analysis, tech experts have been able to unravel new frontiers of productivity and profitability. One such sector that is at the forefront of leveraging these emergent technologies is the Auto industry, particularly commercial vehicles.
The optimization of AI and ML in commercial vehicles has dramatically improved overall performance metrics for countless fleet operators around the country, bringing in a new era of efficiency and profitability. With a plethora of light, medium, and heavy commercial vehicles perambulating the entire length of the national expanse, utilizing intelligent, continuously learning systems is creating a world of difference for fleet operators.
So then, how do AI and ML play such an essential role in improving performance benchmarks in the commercial vehicle industry?
Principally, AI and ML facilitate replicating and enhancing the behavior of numerous sensors integrated with modern vehicles. By optimizing these novel technologies, mobility experts can improve the resolution of pre-embedded sensors in the virtual realm while extending the same to create digital replicas of vehicular systems and components. Moreover, AI-based modelling of these virtual entities aid in gauging wear and tear resulting from rugged usage in the harsh driving environments of India.
This is where the revolutionary concept of Digital Twins enters the picture. Again, this begs the question, what is a Digital Twin?
A Digital Twin combines a computational model and a real-world system designed to monitor, control, and optimize its functionality. Through data and feedback, both simulated and real, a Digital Twin can develop autonomy to learn from and reason about its environment. The Digital Twin model simulates components as multi-dimensional data models by using quantifiable features on wear and tear derived from said models that map against a diverse, highly representative population with similar specifications to generate predictive insights.
Many leading solution providers in the mobility industry are heralding massive waves of disruption by utilizing the Digital Twin paradigm. These Digital Twin companies extend niche solutions in the mobility space to mine data streams, derive actionable insights, and deliver KPI-centric business value for their customers.
Let’s see how AI and ML-led predictive insights are helping enhance the performance & safety of commercial vehicles.
Component level predictive insights
By deriving component-level predictive insights, the Digital Twin technology enables a continuously learning virtual sensor for each system that can predict and identify impending sub-system level failure modes to avert significant damages in the future. These predictive insights, coupled with machine learning, help fleet operators conduct preventive and proactive maintenance, increasing vehicle uptime and efficiency.
Predictive diagnostics for vital powertrain systems like the fuel injection assembly, lubrication, and exhaust after-treatment help in limiting the output of NOx and unburnt hydrocarbons. Optimal engine health, coupled with the elimination of fuel pilferage and improved driver behavior, helps reduce the CO2 footprint of vehicles. Hence, Digital Twins have a pivotal role in the environmental sustainability of the modern-day, drive-by-wire automobile.
Fuel efficiency and pilferage
New-age mobility technologies use the data derived from OEM fitted sensors to assess fuel levels and vehicles’ performance accurately. Specialized algorithms are tailor-made to consume data from different parts of the fuel tank to monitor instances of fuel pilferage. These fuel tracking solutions help fleet owners achieve substantial savings by eliminating fuel pilferage by the vehicle operator, thereby ushering a tectonic shift in legacy fleet management practices. As fuel theft, underfilling, and sensor clip removal continue to be the leading malpractices undertaken by drivers, mobility companies are enabling fleet owners to keep a tab on intrinsic fuel-related details of their vehicles. This helps fleet operators save significant amounts of capital.
Driving behavior analysis
Driving behavior is a vital predictor in enhancing fuel economy and reducing the risk of accidents. AI and ML-enabled technologies provide advanced solutions for monitoring driving exceptions like hard braking, harsh acceleration, coasting & wasteful idling, and comprehensive profiling of gear utilization trends for each driver. This assists fleet operators in quantifying the performance of each driver along with their respective pros and cons. Aided by customized training and performance-based incentives, driver behavior analytics can instrument considerable savings for the fleet.
Operational automation proves fruitful in bridging information gaps in OEM service infrastructure to enable higher customer satisfaction, increased vehicle uptime, and cost-efficiency. The Digital Twin model can make dynamic recommendations for servicing visits through accurate real-time vehicle health profiling.
In the context of fleet management KPIs, data models built around Digital Twins can furnish precise statistics around fuel consumption, distance, engine hours, and operational metrics such as schedules, delays, and loads.
By introducing cutting-edge innovations focused on vehicle health and performance, the commercial vehicle industry is becoming smarter, more efficient, and more agile globally. Against the backdrop of escalating energy costs, environmental sustainability, and operational overheads, India is emerging as a torchbearer in the adoption of these advanced technologies.
This Post first appeared on TOI