1606  Power Profile Analyzer

Interactive Analysis of IoT Device Power Consumption Patterns

animation
power
energy
design-strategies
optimization
battery
state-machine

1606.1 Overview

The Power Profile Analyzer is a comprehensive tool for analyzing power consumption patterns in IoT devices. It enables you to configure device profiles, define state machines, visualize power profiles over time, calculate duty cycles, compare multiple configurations, and export power management code.

TipHow to Use This Analyzer
  1. Configure Device Profile: Select MCU, radio, sensors, and operating voltage
  2. Define State Machine: Set up power states and transition triggers
  3. Visualize Power Profile: See time-series current draw with state transitions
  4. Calculate Duty Cycles: Configure wake intervals and estimate battery life
  5. Compare Profiles: Analyze up to 3 different configurations side-by-side
  6. Export Results: Generate JSON profiles or C code for implementation
NoteEducational Value

This tool teaches essential power analysis concepts:

  • Power state modeling - Understanding MCU sleep modes and wake transitions
  • Duty cycle optimization - Balancing functionality against power constraints
  • Profile comparison - Evaluating trade-offs between different designs
  • Real-world estimation - Accounting for peak power, transitions, and leakage

1606.2 Understanding Power Profiles

Power profiling is essential for IoT device design, enabling accurate battery life estimation and optimization of power consumption patterns.

1606.2.1 Power States

Most microcontrollers support multiple power states with different current consumption levels:

Power State Comparison
State Description Typical Current Use Case
Deep Sleep Ultra-low power, minimal functionality 0.1-10 uA Long idle periods
Light Sleep Reduced power, quick wake 0.3-2 mA Short idle periods
Idle CPU halted, peripherals active 1-20 mA Waiting for events
Active Full CPU operation 5-100 mA Data processing
Transmit Radio transmission 50-250 mA Sending data

1606.2.2 State Transition Diagram

%% fig-alt: State machine diagram showing IoT device power states with transitions between deep sleep, light sleep, idle, active, and transmit states, triggered by timer, interrupt, or sensor events.
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#FFFFFF', 'primaryBorderColor': '#16A085', 'lineColor': '#7F8C8D', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#FFFFFF'}}}%%
stateDiagram-v2
    [*] --> DeepSleep: Power On

    DeepSleep --> LightSleep: Timer
    DeepSleep --> Active: Interrupt

    LightSleep --> Idle: Timer
    LightSleep --> Active: Sensor Event

    Idle --> Active: Process Data

    Active --> Transmit: Data Ready
    Active --> LightSleep: No Data

    Transmit --> DeepSleep: TX Complete

    note right of DeepSleep: 0.01 mA
    note right of LightSleep: 0.5 mA
    note right of Idle: 5 mA
    note right of Active: 25 mA
    note right of Transmit: 120 mA


%% fig-alt: Decision tree for selecting appropriate power state based on device requirements and current activity, showing path from highest power (transmit) to lowest (deep sleep).
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#FFFFFF', 'primaryBorderColor': '#16A085', 'lineColor': '#7F8C8D', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#FFFFFF'}}}%%
flowchart TD
    START[Current Activity?] --> Q1{Need to<br/>Transmit?}
    Q1 -->|Yes| TX[Transmit State<br/>120 mA]
    Q1 -->|No| Q2{Processing<br/>Required?}

    Q2 -->|Yes| ACT[Active State<br/>25 mA]
    Q2 -->|No| Q3{Waiting for<br/>Event?}

    Q3 -->|Yes, Quick| IDLE[Idle State<br/>5 mA]
    Q3 -->|Yes, Long| Q4{Need Quick<br/>Wake?}

    Q4 -->|Yes| LIGHT[Light Sleep<br/>0.5 mA]
    Q4 -->|No| DEEP[Deep Sleep<br/>0.01 mA]

    TX --> DONE[Return to Sleep]
    ACT --> DONE
    IDLE --> DONE
    DONE --> START

    style TX fill:#E74C3C,stroke:#2C3E50,stroke-width:2px,color:#fff
    style ACT fill:#E67E22,stroke:#2C3E50,stroke-width:2px,color:#fff
    style IDLE fill:#F1C40F,stroke:#2C3E50,stroke-width:2px,color:#000
    style LIGHT fill:#27AE60,stroke:#2C3E50,stroke-width:2px,color:#fff
    style DEEP fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff

Use this decision tree to select the most appropriate power state for your device’s current needs. Always aim for the lowest power state that still meets functional requirements.

1606.3 Key Formulas

1606.3.1 Average Current Calculation

\[I_{avg} = \sum_{i=1}^{n} \frac{I_i \times t_i}{T_{cycle}}\]

Where: - \(I_i\) = Current in state \(i\) (mA) - \(t_i\) = Time in state \(i\) (seconds) - \(T_{cycle}\) = Total cycle time (seconds)

1606.3.2 Duty Cycle

\[D = \frac{t_{active}}{T_{cycle}} \times 100\%\]

Where \(t_{active}\) is the sum of all non-sleep states.

1606.3.3 Battery Life Estimation

\[t_{life} = \frac{C_{battery} \times \eta}{I_{avg}}\]

Where: - \(C_{battery}\) = Battery capacity (mAh) - \(\eta\) = Efficiency factor (typically 0.8) - \(I_{avg}\) = Average current (mA)

1606.3.4 Energy per Cycle

\[E_{cycle} = \sum_{i=1}^{n} V \times I_i \times t_i\]

Where \(V\) is the operating voltage.

1606.4 Component Power Comparison

%% fig-alt: Comparison chart showing relative power consumption of different IoT components including MCUs, radios, and sensors in various power states.
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#FFFFFF', 'primaryBorderColor': '#16A085', 'lineColor': '#7F8C8D', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#FFFFFF'}}}%%
flowchart TB
    subgraph MCUs["Microcontrollers (Active Mode)"]
        M1["ESP32<br/>80 mA"]
        M2["STM32L4<br/>8 mA"]
        M3["nRF52840<br/>5 mA"]
        M4["ATmega328P<br/>6 mA"]
    end

    subgraph Radios["Radio Modules (TX Mode)"]
        R1["Wi-Fi<br/>240 mA"]
        R2["LTE-M<br/>250 mA"]
        R3["LoRa<br/>120 mA"]
        R4["Zigbee<br/>35 mA"]
        R5["BLE<br/>12 mA"]
    end

    subgraph Sensors["Sensors (Active Mode)"]
        S1["Camera<br/>200 mA"]
        S2["GPS<br/>45 mA"]
        S3["Accelerometer<br/>0.5 mA"]
        S4["Temperature<br/>0.35 mA"]
    end

    style M1 fill:#3498DB,stroke:#2C3E50,color:#fff
    style M2 fill:#9B59B6,stroke:#2C3E50,color:#fff
    style M3 fill:#27AE60,stroke:#2C3E50,color:#fff
    style M4 fill:#E67E22,stroke:#2C3E50,color:#fff

    style R1 fill:#E74C3C,stroke:#2C3E50,color:#fff
    style R2 fill:#2980B9,stroke:#2C3E50,color:#fff
    style R3 fill:#F39C12,stroke:#2C3E50,color:#fff
    style R4 fill:#8E44AD,stroke:#2C3E50,color:#fff
    style R5 fill:#16A085,stroke:#2C3E50,color:#fff

1606.5 Optimization Strategies

TipPower Optimization Checklist
  1. Maximize Sleep Time: Spend >95% of time in deep sleep for multi-year battery life
  2. Minimize Active Duration: Optimize code to reduce processing time
  3. Batch Transmissions: Accumulate data and transmit less frequently
  4. Use Efficient Radios: BLE and LoRa are far more efficient than Wi-Fi
  5. Disable Unused Peripherals: Power down sensors when not sampling
  6. Lower Operating Voltage: Many MCUs consume less at lower voltages
  7. Use Hardware Peripherals: DMA and hardware timers reduce CPU wake time
WarningCommon Pitfalls
  • Ignoring Leakage Current: Even “off” components draw microamps
  • Underestimating Transitions: State transitions consume energy
  • Overlooking Peak Current: Battery must handle transmit peaks
  • Forgetting Regulator Losses: LDOs waste significant power
  • Not Measuring Real Hardware: Datasheets are often optimistic

1606.6 What’s Next

After analyzing your power profiles, explore these related topics:

Interactive analyzer created for the IoT Class Textbook - POWER-PROFILE-001