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.
Glossary
Battery Telemetry

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.

Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.

Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
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.
Related Terms
Explore the core concepts that interact with and depend upon real-time battery data streams to enable intelligent fleet orchestration.
Battery Management System (BMS) API
The software interface that exposes raw battery telemetry to the orchestration layer. It translates hardware signals into a structured digital format.
- Provides read access to voltage, current, temperature, and SoC.
- Enables write commands for charge current limits and contactor control.
- Often implements CAN bus or Modbus protocols at the physical layer.
State of Health (SoH)
A derived metric, expressed as a percentage, that quantifies a battery's present condition relative to its factory-fresh specifications. It is computed from telemetry data over time.
- Tracks capacity fade and internal resistance increase.
- Critical for predicting Remaining Useful Life (RUL).
- Used to flag batteries for preventative maintenance before failure.
Battery Thermal Model
A predictive simulation that uses real-time temperature telemetry to forecast thermal behavior. It prevents thermal runaway and optimizes charging speeds.
- Inputs include ambient temperature, current draw, and cooling system status.
- Outputs guide dynamic charge rate adjustment to avoid overheating.
- Essential for safely executing fast charging protocols.
Energy Consumption Model
A predictive algorithm that estimates future power draw by combining planned routes with historical telemetry data.
- Factors in payload weight, speed profile, and terrain gradient.
- Enables energy-aware routing by predicting the SoC at each waypoint.
- Validated against real-time telemetry to continuously improve accuracy.
Battery Degradation Model
A data-driven or physics-based model that predicts capacity loss over time. It relies on a continuous stream of telemetry to track stress factors.
- Monitors cumulative charge/discharge cycles and Depth of Discharge (DoD).
- Correlates high-temperature events with accelerated State of Health (SoH) decline.
- Informs warranty analysis and second-life battery valuation.
Charge Scheduling Algorithm
An optimization routine that uses real-time State of Charge (SoC) telemetry to assign agents to charging stations without disrupting workflow.
- Balances opportunity charging against scheduled charging windows.
- Incorporates energy cost functions to leverage low-tariff periods.
- Manages charge queue priority based on mission criticality and minimum thresholds.

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.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us