MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202641071107 A) filed by Dr. M. Saraswathi; Dr. B. Shamreen Ahamed; Dr. Shobana D; Ms. Lakshmi Priya J; Ms. Haritha P G; Dr. M. Sree Rajeswari; Mr. S. Aravinth; and Dr. G. Prathiba on June 08, 2026, for Next-Generation Computing, Ai, And Electric Vehicle Technologies For Intelli-Gent Smart Mobility.
Inventors include Dr. M. Saraswathi; Dr. B. Shamreen Ahamed; Dr. Shobana D; Ms. Lakshmi Priya J; Ms. Haritha P G; Dr. M. Sree Rajeswari; Mr. S. Aravinth; and Dr. G. Prathiba.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: Next-Generation Computing, AI, and Electric Vehicle Technologies for Intelli-gent Smart Mobility Abstract This system links modern computing methods, intelligent algorithms, and new de-signs in electric transport to support flexible, safe, mobile networks. Traffic systems now face worsening city crowding; batteries often deliver short distances between charges due to poor power control, plus delays occur at overloaded stations. Net- works tied to cars are easier targets for complex digital attacks. Heavy streams of sensor information from self-driving or assisted vehicles exceed standard processors’ ability to respond quickly enough. Reducing carbon output demands smarter use of electricity and movement habits across cities. More people driving electric models helps cut pollutants but pressures recharging setups - especially when many plug in at once - and challenges electrical grids during peak times. These vehicles also produce vast amounts of data continuously, overwhelming older computation approaches, so fresh techniques must extract useful insights rapidly under tight time limits shaped by how fast vehicles move. Despite its complexity, the design centers on layered computation tailored for smart transportation needs. Built into cars, road equipment, and control hubs, specialized hardware works alongside traditional chips - some mimicking brain circuits, others drawing from quantum methods - to handle tough decisions fast. Instead of relying solely on distant data centers, analysis happens where it's needed most: close to sen-sors and controls. Routing vehicles while managing battery life becomes more adap-tive through rapid calculations across this spread-out setup. Traffic coordination across different transport modes emerges naturally from continuous adjustments driven by live conditions. Workloads shift automatically based on what tasks matter most at any moment. Neural networks run steadily without draining power, thanks to careful balancing between local and remote processing strength. Learning algorithms refine these choices over time, reacting ahead of congestion or sudden changes in service demands. Efficiency rises when computations follow need, not fixed rules. Deep inside the system, a smart engine handles choices using artificial intelligence. Built on network-like structures, it maps out how traffic moves through space and time. Instead of fixed rules, it learns best actions - how fast to go, when to switch lanes, or recharge - for electric cars. Multiple prediction tools feed it data: past travel routes, weather shifts, city events, infrastructure signals. These pieces come from varied sources but work as one. Privacy stays protected because processing happens locally, sharing only what is needed between cities and fleets. Even under attack, its logic resists manipulation thanks to built-in shields. Reasons behind each choice can be shown clearly - a must where lives depend on reliability. As roads change with seasons or habits, it keeps updating without stopping. Hidden threats like corrupted updates get caught early, keeping decisions sound. Computing strength meets sharp thinking inside advanced electric vehicles, going well beyond standard engine setups. Instead of relying on tradition, new powertrains embed smart layers that adapt in real time. Learning models fine-tune how batteries report charge levels, health status, and lifespan estimates - accuracy improves with constant feedback loops. Heat gets managed through dynamic adjustments; cells stay balanced not by routine but by foresight built into software logic. During slowdowns, energy flows back smarter, recovering more juice when slowing down. Charging ports now allow two-way movement: cars feed homes or grids when demand spikes occur. A central brain oversees these exchanges, timing them around solar and wind patterns rather than fixed schedules. Motors respond faster because prediction shapes torque delivery before conditions shift. Efficiency climbs - not from brute force, but anticipation woven into every rotation. Redundant sensors, along with protected communication units, allow vehicles to share environmental awareness and coordi-nate responses using smart traffic frameworks. Information drawn from electric car diagnostics blends in real time with outside inputs within a thinking sub-layer, shap-ing complete improvements in path selection and power handling. While internal readings flow continuously, coordination emerges not just from local logic but from layered exchanges across networked components. One layer builds on another, creating real gains in how systems run. Fleet operators see lower power use and reduced expenses when routes adapt to hills, congestion, and charger access - maintenance alerts also help avoid breakdowns before they hap-pen. Traffic moves more smoothly across cities and highways because adaptive sig-nals and coordinated speeds are managed by intelligent algorithms. Protection against digital threats grows stronger using encryption methods inspired by quantum principles along with tools that spot unusual behavior in car-to-car and car-to-roadway data exchanges. Expansion into citywide digital ecosystems happens easily, allowing buses, e-scooters, delivery vans, and other services to work together within a cleaner transportation model - this shift makes daily travel better for people, cuts costs for businesses managing vehicles, while pushing emission goals forward by ful-ly leveraging electric transport's potential. Now imagine a system that works just as well on one central server or spread across many devices. Instead of fixed setups, it adjusts itself automatically when needs change, using smart software to manage processing tasks and decision-making func-tions. This flexibility comes alive through special computer chips built to run com-plex calculations directly inside vehicles without draining battery life or taking up too much space. Training those internal systems happens first in digital worlds so re-alistic they mirror actual traffic patterns and city movement. Updates continue over time by learning from live performance data gathered during everyday use. Commu-nication methods do not limit the design - it runs smoothly whether connecting via short-distance wireless links or wider mobile-based vehicle networks. Even towns with limited tech infrastructure can access powerful tools through cloud-style ser-vices, avoiding costly hardware investments. Widespread use of smarter transporta-tion technology follows more easily because of this open structure. Starting with smarter computation, this work combines AI techniques along with ad-vances in electric vehicles to form a base for future transport - marked by better per-formance, stronger safety, care for the environment, and wider access. Instead of treating each technology apart, it tackles persistent gaps through close coupling, ena-bling new behaviors that appear only when systems operate together, pushing for-ward intelligent movement solutions while supporting broader goals for sustainable, adaptive transit networks.
Disclaimer: Curated by HT Syndication.
