India has built the policy scaffolding for electric mobility. The next dividend will come from vehicles that understand their own energy systems, provided artificial intelligence is deployed with a defined purpose.
By: Sharad Gupta | Co-Founder | Exeliq Tech Solutions
India’s electric mobility story has moved from a question of adoption to one of optimisation. According to the International Energy Agency (IEA), total electric vehicle (EV) sales in India reached a record of around 2.3 million units in calendar 2025, with electric car sales rising by more than 75 per cent. Manufacturers have widened their portfolios, charging networks are expanding and battery technology continues to mature.
The policy scaffolding has kept pace. The Prime Minister Electric Drive Revolution in Innovative Vehicle Enhancement (PM E-DRIVE) scheme, run by the Ministry of Heavy Industries with an outlay of ₹10,900 crore, has been extended to March 2028 and earmarks roughly ₹2,000 crore for public charging infrastructure. More than 29,000 public charging stations were installed nationally by early 2026.
What happens next depends less on how many EVs reach the road than on how intelligently each one uses, stores and manages energy. That is where artificial intelligence (AI) becomes an infrastructure layer.
The data already exists. The decisions do not.
A modern EV generates a continuous stream of operating data: battery voltage, current, cell temperature, charging behaviour, energy consumption and driving patterns. Most is collected. Little is converted into decisions. Conventional battery management systems (BMS) run on predefined rules and thresholds, holding the pack within safe limits. That is essential, but reactive. AI extends the logic by finding patterns fixed thresholds cannot capture.
Instead of merely displaying the state of charge (SOC), an intelligent system can read SOC alongside temperature, driving behaviour, charging history and pack condition, and recommend a strategy suited to that vehicle on that day. One that learns its owner’s driving patterns can also predict range more accurately.
Battery health as a living estimate
Degradation is where the difference becomes commercially and environmentally significant. A battery does not move from “healthy” to “failed”. Its performance shifts gradually with temperature, charging behaviour, depth of discharge and duty cycle, and AI can read those trajectories to anticipate degradation before it becomes visible to the user.
That turns battery health from a periodic inspection into a continuously updated estimate. Rather than reporting only the present state of health (SOH), an intelligent system can identify the trend and project how the pack will behave, informing maintenance scheduling, warranty administration and resale valuation. The value is not a more sophisticated number on a display, but a better decision taken because of it.
It is also becoming a compliance asset. With 11,49,334 electric two-wheelers sold in India in the financial year 2024-25 alone, according to Ministry of Heavy Industries data, retired packs will arrive at scale. Under the Battery Waste Management Rules, 2022, producers carry Extended Producer Responsibility (EPR) obligations, with material recovery targets rising to 90 per cent by FY2026-27. Accurate SOH estimation is what routes a retired pack to second-life storage where residual capacity justifies it, and to recycling where it does not. Guesswork sends usable cells to the shredder.
From reactive repair to early detection
EV batteries and their power electronics usually signal trouble before they fail. Thermal drift, widening voltage differences between cells, irregular charging profiles or unexplained consumption can each indicate an emerging fault, and continuous anomaly detection can surface those signals early enough to matter. Maintenance then begins when behaviour deviates, not after a breakdown. This is not speculative: a domestically developed AI-based diagnostic system featured on NITI Aayog’s Frontier Tech platform reports having analysed more than five lakh batteries.
Charging is a grid question, not only a vehicle question
Much of the industry conversation has focused on cutting charging time. The more consequential shift may be from faster charging to smarter charging. A vehicle weighing pack condition, expected journey, dwell time, temperature and tariff signals can select a strategy rather than simply drawing maximum current, and can recommend a station on remaining range, availability and route, not distance alone.
This matters beyond the individual vehicle. As Time-of-Day tariffs arrive alongside the smart meter rollout, and as Bureau of Indian Standards (BIS) specifications establish the grammar for smart and bidirectional charging, an EV that can shift a charging session by two hours stops being a consumer convenience. It becomes a distributed grid asset.
Where the limits sit
AI does not become intelligence simply because an algorithm has been added to a vehicle. Its output depends on the quality and relevance of the underlying data. Battery behaviour varies across chemistries, manufacturers, ambient conditions and vehicle architectures, and a model trained under one set of conditions may generalise poorly to another. Dependable models need structured data spanning real operating conditions and every stage of battery life, an area where industry-wide data discipline counts for more than any single company’s dataset.
Safety sets a harder boundary. India’s framework has already tightened, with Automotive Industry Standard (AIS) 156 incorporating thermal propagation requirements addressing thermal runaway in traction batteries. AI can support prediction and recommendation, but safety-critical functions cannot rest on the assumption that a model will always behave correctly. A clear separation between prediction, recommendation and automatic control is essential, and more so as vehicles turn software defined.
Data governance forms the third boundary. Vehicle telemetry frequently constitutes personal data under the Digital Personal Data Protection Act, 2023, and consent, purpose limitation and auditability are far cheaper built into the architecture than retrofitted later.
AI should have a purpose
The next generation of EV innovation will not be defined by larger battery packs, faster chargers or more powerful motors alone, but by how well a vehicle understands its own energy system and what it does with that understanding: learning from its battery, anticipating faults, optimising charging around grid conditions and telling its owner something meaningful.
One principle should anchor that transition: AI should have a purpose. It will not transform electric mobility because it has been added to vehicles. It will do so when manufacturers can state precisely what they want it to achieve inside the vehicle and can measure whether it did. Those that grasp this will convert data into decisions, decisions into efficiency, and efficiency into a better ownership experience.
The EV of the future will not simply be electric. It will be aware, predictive and accountable about the energy it uses.
