Inferensys

Glossary

Battery Telemetry

Battery telemetry is the real-time data stream from a battery management system, including metrics like voltage, current, temperature, State of Charge, and State of Health.
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REAL-TIME ENERGY DATA STREAM

What is Battery Telemetry?

Battery telemetry is the continuous, real-time stream of operational data transmitted from a battery management system (BMS) to an external orchestration platform, providing the foundational visibility required for energy-aware fleet scheduling.

Battery telemetry is the real-time data stream from a battery management system, comprising critical metrics such as voltage, current, temperature, State of Charge (SoC), and State of Health (SoH). This data is transmitted over protocols like CAN bus or MQTT to provide a continuous, high-fidelity view of an agent's energy status.

In heterogeneous fleet orchestration, this telemetry serves as the primary input for battery-aware scheduling algorithms. By consuming real-time SoC and thermal data, the orchestration middleware can dynamically route agents to opportunity charging stations or reassign tasks to prevent depth of discharge (DoD) violations, directly optimizing both operational uptime and long-term battery lifespan.

Real-Time Data Streams

Core Battery Telemetry Metrics

The foundational data points streamed from a Battery Management System (BMS) that enable real-time state estimation, predictive maintenance, and energy-aware fleet orchestration.

01

Voltage (V)

The electrical potential difference between the battery's terminals, measured in volts. Open Circuit Voltage (OCV) correlates with State of Charge after a relaxation period, while Terminal Voltage under load reflects internal resistance. A sudden voltage sag during high current draw indicates increased internal impedance, a key degradation marker. Monitoring individual cell voltages in a series pack is critical for detecting imbalances that can lead to thermal runaway.

mV
Measurement Precision
02

Current (A)

The rate of electron flow into (charging) or out of (discharging) the battery, measured in amperes. Coulomb Counting integrates current over time to estimate State of Charge. Precise current sensing is essential for calculating energy throughput and tracking charge/discharge asymmetry. Bidirectional hall-effect sensors capture both regenerative braking energy and propulsion consumption, feeding directly into the Energy Consumption Model.

±0.5%
Typical Sensor Accuracy
03

Temperature

Distributed thermal measurements from multiple points on the battery pack, typically via thermistors. This telemetry feeds the Battery Thermal Model to prevent lithium plating during cold charging and thermal runaway during fast discharge. Key metrics include:

  • Cell core temperature: The internal electrochemical temperature, often estimated via a thermal model.
  • Surface temperature: Measured at cell tabs or casing.
  • Coolant inlet/outlet temperature: For liquid-cooled packs, indicating heat rejection efficiency.
1°C
Measurement Accuracy
04

State of Charge (SoC)

A derived metric, expressed as a percentage (0-100%), representing the remaining capacity relative to the current maximum. Unlike a simple fuel gauge, SoC is algorithmically estimated by the BMS using sensor fusion of coulomb counting and voltage-based correction. Accurate SoC telemetry is the primary input for the Minimum Charge Threshold and Charge Scheduling Algorithm, dictating when an agent must divert from its mission.

±1-3%
Estimation Error
05

State of Health (SoH)

A derived metric, expressed as a percentage, comparing the battery's current maximum capacity and power capability to its factory specifications. SoH degrades due to calendar aging and cycle aging. Telemetry inputs for SoH estimation include tracked capacity fade over many charge/discharge cycles and the rise in internal resistance inferred from voltage drop under load. SoH directly informs the Remaining Useful Life (RUL) prediction.

80%
Common End-of-Life Threshold
06

Internal Resistance (DCIR)

The battery's opposition to direct current flow, measured in milliohms (mΩ). DC Internal Resistance (DCIR) is calculated by the BMS by measuring the instantaneous voltage drop (ΔV) for a known current pulse (ΔI) using Ohm's law (R = ΔV/ΔI). A rising DCIR trend is the primary indicator of power fade and aging. This metric is critical for the Fast Charging Protocol, as higher resistance generates more heat (I²R losses) during high-current charging.

Unit of Measurement
REAL-TIME ENERGY DATA AS A CONTROL PLANE

How Battery Telemetry Enables Fleet Orchestration

Battery telemetry transforms the battery from a passive fuel tank into an active, software-defined node within the fleet control system, enabling dynamic scheduling decisions based on precise energy states.

Battery telemetry is the continuous, real-time data stream from an agent's Battery Management System (BMS), providing the orchestration platform with granular metrics such as voltage, current, temperature, State of Charge (SoC), and State of Health (SoH). This data feed is the foundational input for battery-aware scheduling, converting an opaque energy reserve into a predictable, manageable resource that can be optimized alongside task queues and traffic flows.

Without high-fidelity telemetry, a fleet orchestrator must rely on conservative, static assumptions about energy availability, leading to underutilized assets and unnecessary charging cycles. By ingesting this stream, the orchestration middleware can execute dynamic task allocation, predict Remaining Useful Life (RUL), and trigger opportunity charging precisely when it minimizes operational disruption, effectively making energy a first-class citizen in the multi-agent path planning problem.

BATTERY TELEMETRY

Frequently Asked Questions

Clear, technical answers to the most common questions about the real-time data streams that power battery-aware fleet orchestration.

Battery telemetry is the continuous, real-time data stream transmitted from a Battery Management System (BMS) to an external orchestration platform, providing a digital window into the electrochemical state of a battery. It works by using embedded sensors to measure physical quantities—voltage, current, and temperature—at the cell, module, and pack level. The BMS microcontroller then processes these raw signals, applying algorithms like Kalman filters and coulomb counting to derive critical state estimations. This processed data, including State of Charge (SoC), State of Health (SoH), and cell balancing status, is serialized and transmitted over a communication bus like CAN bus, Modbus, or MQTT to a central fleet manager. This allows the orchestrator to make energy-aware decisions without direct physical access to the agent.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.