In an interview with Aditya Rangroo, Ursaa Energyworx Founder Kapil Sharma saidIf batteries degrade faster than expected, the entire EV economics model falls apart
Everyone talks about EV adoption as if it is a solved problem. It is not. Fleet operators are not struggling to buy EVs; they are struggling to run them efficiently.
The biggest issue is unpredictability. Two identical vehicles, on the same route, on the same day, can deliver completely different performance, and most operators have no idea why. Battery behaviour is not linear. It is affected by temperature, driving style, and charging habits. Yet, most fleets still operate without real visibility into these factors. So what happens? You don’t optimise, you react. And that is where the real cost creeps in.
Right now, most EV fleets are managed based on symptoms, not causes. You see reduced range or a breakdown, but you don’t know what led to it.
Data changes that. Predictive analytics takes it even further. At Ursaa, we are not interested in just showing dashboards, which is useless if it doesn’t drive decisions.
We focus on the questions that actually matter:
If batteries degrade faster than expected, the entire EV economics model falls apart. What we have observed is that most damage isn’t sudden, it is gradual and preventable.
Technology helps in three key ways:
You don’t need magic, you need visibility and discipline. Technology enforces both.
Range anxiety exists because the system is misleading, not intentionally, but because it relies on static estimates in a dynamic environment.
If your range estimate doesn’t account for traffic, load, terrain, or temperature, it’s unreliable.
Data fixes that. When range becomes predictive and context-aware, it stops being a guess and starts being a commitment. For fleets, that means fewer disruptions. For consumers, it builds trust.
And once trust is established, range anxiety fades quickly.
In markets like India, complexity is the default. There is extreme weather, unpredictable traffic, inconsistent infrastructure, and highly variable usage patterns.
If you try to manage this with static systems, you will fail. IoT provides the raw signalcontinuous data from vehicles and batteries. AI transforms that data into real-time decisions.
This combination is what makes EV operations viable at scale in such environments. Without it, you are essentially guessing, and guessing doesnot scale.
Let’s be honest: no logistics company adopts EVs just because it sounds good. They will switch when it makes operational and financial sense.
Battery intelligence platforms make that shift viable. They:
In high-utilisation sectors like last-mile delivery, small inefficiencies quickly turn into significant losses.
Everyone points to charging infrastructure, and yes, it matters. But the bigger gap is intelligence.
We still lack:
Right now, the ecosystem is fragmented. Until these systems start communicating with each other, scaling EVs efficiently will remain more difficult than it should be.
These three domains are set to converge, and faster than we can imagine now.
Vehicles won’t just consume energy; they will become part of the energy ecosystem.
We will see:
The winners won’t be those with the most vehicles. They will be those who understand and control the data layer. That’s where the real leverage lies, and that’s exactly where we are building.
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