First published 2026-06-27 · Updated June 27, 2026 · Energy & Mobility · 12 min read
A detailed engineering audit of the telemetry architectures of Ola Electric and Ather Energy. Compares edge CAN bus data serialization, cellular MQTT routing protocol over unreliable networks, and cloud-scale ingestion and battery diagnostics pipelines.
Both Ola Electric and Ather Energy rely on high-frequency, local vehicle networks governed by the Controller Area Network (CAN) bus standard. The CAN bus aggregates real-time metrics from the battery management system (BMS), motor controller (MCU), instrument cluster (dashboard), and chassis sensors. The main challenge in electric vehicles is logging battery metrics at high granularity: cell-by-cell voltages, local temperatures, charge/discharge rates, and state-of-health (SoH).
Ather's Telematics Control Unit (TCU) runs a real-time embedded stack that queries the CAN bus at 10 Hz for critical safety variables, and 1 Hz for general logging. In contrast, Ola's MoveOS platform uses an Android-based compute board that reads data through a custom HAL (Hardware Abstraction Layer). This compute layer parses CAN frame packets and writes them to local storage before staging them for cellular uplink.
Because cellular connectivity (4G LTE/2G fallbacks) is highly unstable along Indian national highways and in dense urban basements, both architectures implement robust edge buffering. When cellular handshakes fail, the TCU buffers telemetry data locally on flash memory chips.
Ather uses a binary serialization format (Protocol Buffers) to pack CAN variables into tight, lightweight bytes, minimizing cellular bandwidth overhead. Ola relies on custom JSON payloads. Telemetry is shipped via MQTT (Message Queuing Telemetry Transport) over TLS. When connectivity drops, Ather queues messages in non-volatile memory and uploads them sequentially upon reconnecting, using QOS 1 (Quality of Service: At least once delivery) to prevent data loss. Ola’s MoveOS caches JSON payloads and synchronizes them via HTTP/REST APIs once stable Wi-Fi or 4G is detected.
In the cloud, the cellular ingestion gateway consists of MQTT brokers (such as EMQX or HiveMQ) routing streams to Apache Kafka. The telemetry pipeline processes millions of messages per minute. Stream processors (Apache Flink or Spark Streaming) monitor these inputs for anomaly detection.
The primary analytic objective is thermal runway prediction. Flink jobs continuously compute moving averages of battery pack temperatures and compare them with motor load states. If cell temperatures spike by more than 2°C per second under normal load, a high-priority push notification is routed to the dashboard to alert the rider to park the vehicle immediately. Historical telemetry is piped to ClickHouse to train machine learning models to predict battery degradation curve models.
In response to thermal runway incidents in hot summer months, the Ministry of Heavy Industries mandated the AIS 156 (Amendment 2) battery safety standards. This regulation imposes strict requirements on both hardware and software telemetry layers. Stacks must implement smart alarms that trigger early warnings of thermal anomalies, even when the vehicle ignition is turned off.
Ather's BMS architecture complies with AIS 156 by utilizing redundant cell-level temperature sensors (thermistors) coupled with active cell-balancing algorithms. This telematics system reports any variance in cell voltage greater than 50mV. Ola's MoveOS handles compliance by maintaining a low-power deep-sleep state for its Android-based OS. This OS awakes periodically to scan battery health and push warning payloads via cellular link if thermal indicators drift. This standard has made battery analytics the core safety stack in Indian EV development.
Building high-scale software applications in India requires a deep understanding of local constraints, high latency networks, and rapid regulatory updates. Product managers and engineering leads must prioritize structural data integrity, strict audit logs for compliance, and telemetry monitoring at the edge. By designing architectures that balance user experience with regulatory requirements, platforms can successfully minimize churn, optimize transaction success rates, and build robust technology stacks that support sustainable growth in India's competitive digital economy. Keeping stacks aligned with RBI and government portals is no longer optional; it is the core foundation of product engineering.
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