Hello everyone. I am pri today I'll be walking you through how cloud native technologies can fundamentally reshape EV charging. The goal is to show how we can scale, optimize, and make charging smarter, supporting not only drivers, but also the grid and the broader push towards sustainable mobility. Before we dive into technology, let me introduce myself. My name is Pri Ali, and I'm currently working as a senior technical program manager at Ford Motor Company. With my nine plus years of experience, I have been fortunate to lead several initiatives at Ford and other companies that bring together cloud native infrastructure and electric mobility. My background is in scalable systems and program management, which means that I sit at the intersection of technology and execution. This helps me help businesses turn strategy into working infrastructure. My focus has been on making ev charging not just available, but intelligent and user friendly from an educational standpoint. I have a bachelor's and a master's in electrical engineering, and I'm pursuing my PhD in systems engineering. As far as cloud native infrastructure goes, this is becoming more important than ever because ev adoption is accelerating faster than an infrastructure can keep up. Last year alone, 14 million EVs were sold globally. That's a huge leap. Nearly one in five cars were sold worldwide and electric. This shift is more than a trend, it's a revolution in mobility, but with it comes new expectations. Drivers need reliable, predictable charging, just as they have always expected gas stations to be available. Without scalable infrastructure adoption could slow. So the challenge is not just to build more chargers, but to build smarter ones. And that brings us to the reality of today's charging infrastructure and the pain pointed faces. There are three main hurdles that I would like to call out. The first one is scalability. Traditional networks weren't designed for exponential growth or global coverage. Secondly is grid integration. Legacy systems don't adapt well to dynamic load management or to renewable energy. And thirdly, user experience range anxiety remains because availability is patchy. Visibility is poor, and downtime is just too common. These are the gaps that we must address if EP adoption is to continue at its current pace. So how do we solve this? The answer lies in the principle of cloud native design. Cloud Native gives us modularity and flexibility, microservices that helps us break down complex systems into smaller, more manageable services like for payments, for routing, user interfaces, et cetera. Kubernetes that orchestrates them. Scaling automatically as the demand spikes and open APIs allows integration with vehicles, utilities. And even third party applications together. This creates an ecosystem that's a dynamic, scalable and resilient, exactly what EV charging needs at the moment. Now let's see how this foundation translates into practical improvements, starting with reliability. Charge downtime has been one of the biggest foundation frustrations for drivers. With predictive maintenance, we can cut downtime by as much as 40%. Iot sensors that continuously monitor health machine learning that predicts failures before they can even happen, and cloud-based systems schedule repairs automatically. The result is fewer surprises for drivers and more efficient use of technician resources. Reliability builds trust and trust is what drives adoption Beyond reliability, drivers also care about convenience, and that's where smart routing and load balancing comes in Picture. Now cloud services bring real-time visibility into charger status and even Q lens. Intelligent routing algorithms can then guide drivers to the PEs station based on traffic speed of charging and availability. At the same time, distributed load balancing insurers chargers are used optimally while minimizing grid strengths. This creates a system that feels responsive put to the driver and to grid operator. Together, this innovations tackle the most persistent worry for EV drivers range anxiety. Now with live data and smart routing drivers don't have to guess or hope that a charger is free. They know before they get there. Routing adapts dynamically even considering traffic. And on the grid side, load optimization prevents bottlenecks. All of this means a predictable stress-free charging experience. Once drivers stop worrying about being stranded, EV adoption becomes seamless. Of course, smart charging isn't just about drivers. It also plays a major role in how we manage energy. Demand charging patterns can put immense stress on the grid. If unmanaged. Cloud native scheduling solves this by shifting sessions to off peak times. It uses pricing grid conditions and user preferences to automatically schedule charging when it's cheapest and least disruptive. This lowers cost for consumers, reduces stress on the grid and benefits operators. So it's a win-win for all. And once vehicles themselves are seen as assets, not just consumers or power, the possibilities expand even further. Let's talk about vehicle to grid or V two C as it's called. This turns EVs into mobile batteries. Imagine millions of cars acting as distributed storage. Cloud native platforms can orchestrate this at scale, absorbing excess solar or wind when supply is high and feeding power back. When demand spikes, it supports renewable, strengthens the grid, and creates new value streams for owners. This is where mobility and energy ecosystems truly converge. Scaling this vision, however, requires a phased approach. So what we can do. Rollouts typically start with core infrastructure, the essential charging platform. From there, Kubernetes, make horizontal scaling across sites seamless. Once the network is stable, we can layer in advanced features like B two G, AI driven optimization, et cetera. This waste model keeps cost manageable and ensures reliability at each stage of growth and scale matters, especially when we consider global targets For 2030, the International Energy Agency projects a need for four times as many public charges by 2030, which is very close now. That's a massive expansion. Traditional systems would struggle, but cloud native approaches make it feasible. They simplify deployment, standardized management, and allow operators to square quickly while maintaining quality. This is how we keep pace with EV adoption at a global scale. And underpinning all this is the ability to see, analyze, and act on data in real time. Analytics gives operators visibility into performance. Consumption and usage patterns. Edge computing enables faster decision making, locally reducing latency. Meanwhile, machine learning continuously improves predictions for routing, maintenance, and load balancing. Over time, the network learns and adapts becoming more efficient without manual intervention. This leads us to the bigger picture, the path towards truly intelligent mobility. The journey unfolds in three steps. First, build a strong cloud native foundation. Then integrate smart systems like predictive maintenance and dynamic opting, and finally optimize and scale with real time analytics and ai. The outcome is an infrastructure that's not just scalable, but intelligent, capable of adopting to changing demands, technologies, and user needs to close. Let me bring it all together. Thank you for joining me today. Cloud native EV charging is more than just technology. It's the backbone of intelligent, sustainable mobility. By addressing scalability, reliability, and grid integration, we enable a future where electric vehicles are not only viable but preferable. I look forward to hearing from all of you on your thoughts, and if there are any questions, thank you again for giving me the opportunity to speak at com 42 Kubernetes 2025 conference. Thank you. Bye.