EMO Energy has released insights from over four years of real-world electric fleet operations, highlighting how big-scale data is reshaping the understanding of battery performance and intelligence.
During this period, the company has developed and deployed battery systems designed for high-usage electric fleets. Some of its initial battery packs have been in operation for nearly three years, powering delivery vehicles in cities like Bengaluru and Gurugram. These vehicles operate under tough conditions, particularly in fast-commerce applications, often covering ranges of around 100 kilometers per day, relying on several short fast-charging cycles to remain operational.
A segment of these batteries has crossed 75,000 kilometers of real-world use, largely dependent on fast charging. This comprehensive dataset provides valuable operational insights into how EV batteries perform under continuous fleet use in Indian conditions – an area where the industry still lacks sufficient depth of understanding.
At the core of this analysis is SENS, EMO Energy’s proprietary battery health prediction and optimization platform, which captures and processes performance data in real-time. The findings challenge the traditional view of EV batteries as consumable components, instead positioning them as predictive, intelligent infrastructure capable of predicting aging patterns and performance over time.
Commenting on the findings, Shitanshu Tyagi, CEO of EMO Energy, said that battery performance in fleet operations depends not only on chemistry, but also on usage patterns, charging behaviour, thermal management and long-term ageing. He emphasized that a holistic approach is necessary to design battery systems that can meet the demands of modern electric mobility.
A dataset built under difficult circumstances
The dataset comes from 100 EMO battery packs deployed in commercial delivery fleets operating in Bangalore and Gurgaon. These vehicles operate in the toughest conditions for EV batteries: high daily usage, frequent fast charging, and frequent climate exposure in Indian summers, monsoons and winters. In Delhi summer conditions, the ambient temperature can cross 45 °C, which typically increases thermal stress during charging and discharging.
Unlike passenger EVs, which largely rely on overnight charging, these fleet vehicles charge multiple times a day to maximize uptime. The typical pattern involves four to five charging sessions per day, about five minutes per session, adding up to a total charging time of about twenty minutes per day. The battery capacity is 2 kWh, the fast charging power is 3.3 kWh, and the 0-80% charge time is about twenty minutes. Under conventional assumptions, such frequent fast charging is expected to accelerate the degradation. EMO’s long-term field data shows that results can vary materially when the system is designed for this exact use case.
What changes after 75,000 kilometers
In the dataset, the oldest packs have crossed approximately 75,000 kilometers. The EMO reports a relatively gradual decline in health conditions. After 20,000 km, the packs sit around 96-98% health condition. After 50,000 km, they are around 90-92%. At 75,000 km, they remain about 85-88%. In practice, this corresponds to about 15% degradation after 75,000 kilometers of real-world operation.
What makes this remarkable is the comparison to the typical degradation pattern in a conventional EV pack operating under repeated fast charging. In many such systems, a drop of 25–30% by 75,000 kilometers is not unusual, with health conditions often falling in the range of 70–75% at that distance. In the EMO deployment monitored via SENS, the PACS retained approximately 85% of original capacity while operating under the same high utilization pattern. The difference is not only in the last number. This is the ability to manage battery aging in a more controlled, predictable manner.
Why do EV batteries need intelligence, not just monitoring?
There is rarely a single cause of battery failure. It is the result of many small stresses that add up over time. Charging the battery too quickly when it is cold, operating in high ambient temperatures, frequent deep discharge, and hundreds of cycles under inconsistent conditions all affect the way the battery ages.
Traditional battery management systems are primarily designed for safety at this point. They monitor temperature, voltage and current, and intervene if something crosses limits. This is necessary, but it doesn’t answer the question that concerns fleet operators most: how this battery will hold up after months and years of intensive use.
This is where predictive battery intelligence becomes important. SENS continuously analyzes telemetry from charging sessions, temperature profiles, cycle history and usage patterns to determine how each battery is aging. The intention is not only to react when the battery is under stress, but to anticipate how degradation is likely to unfold and adjust charging behavior and operating parameters before long-term wear accelerates. This transforms the battery from a passive component into a predictable energy asset.
Intelligence layer and what it does
SENS acts as an intelligence layer in EMO deployment. It analyzes telemetry such as cell voltage behavior, charge and discharge profiles, temperature distribution, cycle history and usage patterns in the fleet. Using this, it creates predictive models of battery health performance.
The outputs are practical. It can predict degradation trajectories, detect early signs of abnormal behavior, optimize charging profiles for longevity and maintain performance consistency across the fleet. For an operator managing thousands of vehicles, this kind of predictability matters more than dramatic specifications on paper. This helps eliminate surprises, minimize downtime, and plan asset life with greater confidence.
Thermal architecture still matters
Predictive intelligence can’t compensate for poor battery design. EMO batteries use a patented immersion cooling system where the cells are surrounded by a proprietary dielectric coolant circulating throughout the pack. The aim is to achieve uniform thermal management across all cells, including faster charging.
In real-world operation, EMO reports that it keeps the cell temperature between 24°C and 28°C during fast charging. Maintaining a narrow thermal range reduces the electrochemical stress that typically induces rapid degradation. EMO reports zero cases of thermal runaway in the entire dataset even under high ambient temperatures.
What this means for rapid commerce fleets
For accelerated commerce companies, electrifying the delivery fleet is a sustainability as well as an operational decision. These fleets often travel 80–120 km per day, charge multiple times throughout the day, and have minimal tolerance for downtime. Two concerns generally slow adoption: degradation under repeated fast charging and uncertainty about long-term battery health.
Field data from EMO deployments shows that this risk can be significantly reduced when batteries are designed for high-frequency fleet environments and supported by predictive intelligence. The vehicles operating in Bengaluru and Gurgaon, many of which are charged four to five times a day, have clocked over 75,000 kilometers while maintaining more than 85% health. For fleet operators, the question changes. It becomes less about whether EV batteries can survive heavy use and more about how fast fleets can grow when batteries behave like reliable infrastructure.
big takeaway
Much of the EV conversation focuses on vehicles and charging networks. But as fleets electrify in scale and age, it becomes clear that batteries must be treated as long-term assets that require both robust thermal architectures and intelligent systems around them. EMO’s long-term dataset, monitored via SENS, offers a view of what is possible when battery design, fast charging infrastructure and energy intelligence work together.
Because the future of electric mobility will not be defined by batteries alone. It will be defined by the energy systems built around them.