Researching Battery Smart: Uptime Business or Fast-Scaling Network?

Vansh set out to answer a single question about Battery Smart: was the company building a genuinely attractive and defensible business, or was it simply scaling quickly?

He began with the customer problem. For a commercial electric two-wheeler or three-wheeler driver, the core issue is not battery ownership but vehicle downtime. Traditional charging can idle a vehicle for several hours; Battery Smart's swap model reduces that to roughly two minutes. This reframe led him to define the business as selling uptime rather than batteries, which shaped every subsequent research decision.

He structured the analysis into five areas. First, he sized the market by narrowing the addressable universe to commercial two-wheelers and three-wheelers where swapping was technically and economically relevant, rather than using the entire EV market. He estimated TAM, SAM, and SOM using commercial fleet data, EV penetration rates, regional registration data, and the company's disclosed active driver base. His estimate suggested Battery Smart still had relatively low penetration despite its scale.

Second, he assessed operating scale. He reviewed the company's network of more than 1,600 stations across over 70 cities, more than 100 cumulative swaps, and approximately one lakh active drivers to evaluate whether the business had moved beyond the pilot stage and demonstrated repeatable execution.

Benchmarking and the Infrastructure vs. Utilization Distinction

Third, he benchmarked Battery Smart against Indofast, Sun Mobility, and ChargeUp. He compared station footprint, city presence, rollout speed, operating model, and swaps per station per day. Battery Smart appeared to have stronger station utilization than its closest scaled competitor. However, Indofast had built a comparable physical footprint much faster by using Indian Oil's fuel station network. This led him to distinguish between infrastructure scale, which may be replicable, and operating utilization, which may be harder to copy.

Fourth, he examined financial and strategic risk. Publicly available figures showed strong revenue growth, but operating costs had risen rapidly alongside it. Station-level contribution margin, station payback period, battery depreciation, and new-city economics were not disclosed and could not be triangulated from public data without making unsupported assumptions.

Converting Gaps into Diligence Questions

Fifth, rather than forcing a positive or negative recommendation, he converted the unanswered issues into a diligence agenda. The investment case, he concluded, depended on contribution margin per swap, mature versus new-city payback, battery cycle economics, and whether the utilization advantage would remain durable as competitors scaled.

He triangulated findings across company and founder disclosures, industry research, government registration data, competitor disclosures, funding coverage, and regulatory documents rather than relying on any single source. The project taught him that good startup research is not about building the most exciting narrative. It is about identifying what is already proven, what is claimed, and which unanswered questions would actually change the investment decision.