1605  Power Budget Calculator

Calculate and Optimize IoT Device Power Consumption

1605.1 Understanding Power Budgets

Power budget analysis is critical for IoT device design. This interactive calculator helps you estimate average power consumption, battery life, and identify optimization opportunities based on your component selection and duty cycle configuration.

NoteAbout This Calculator

This tool allows you to build a power budget by selecting components, configuring duty cycles, and choosing a battery. The calculator provides real-time estimates of battery life and shows a breakdown of power consumption by component.

TipHow to Use
  1. Select Components using checkboxes (MCU, Radio, Sensors, Display)
  2. Configure Operating Modes duty cycles (Active, Sleep, Deep Sleep)
  3. Choose a Battery Type and enter capacity
  4. View Results: Average current, battery life, power breakdown
  5. Review Optimization Suggestions based on your configuration

1605.2 Quick Start Presets

Use these reference configurations as starting points for your own designs:

Preset MCU Radio Sensors Duty Cycle Battery Est. Life
Smart Home Sensor nRF52840 BLE Temp 1% active, 99% deep sleep CR2032 2+ years
Asset Tracker ESP32 LoRa GPS, Accel 5% active, 95% deep sleep 2000mAh Li-Ion 6 months
Environmental Monitor STM32L4 LoRa Temp 0.5% active, 99.5% deep sleep 2x AA 3+ years
Connected Display ESP32 Wi-Fi Temp 10% active, 90% sleep 1000mAh LiPo 2 days
Fitness Tracker nRF52840 BLE Accel 5% active, 95% sleep 150mAh LiPo 1 week
Industrial Gateway ESP32 Wi-Fi + Cellular Multiple Always on USB Power N/A

Design Guidelines:

  • Years of battery life: Use nRF52 + BLE, < 1% active duty cycle, coin cell or 2xAA
  • Months of battery life: Use LoRa, < 5% active duty cycle, Li-Ion 1000-3000mAh
  • Days of battery life: Wi-Fi acceptable with 1000+ mAh battery, minimize active time
  • Always-on: Use USB/mains power, full functionality acceptable

1605.3 Understanding Power Budgets

1605.3.1 Why Power Budgets Matter

For battery-powered IoT devices, understanding power consumption is critical for:

  • Battery life estimation - How long will the device operate?
  • Battery selection - What capacity and chemistry is needed?
  • Design optimization - Where can power be reduced?
  • Cost planning - Battery replacement frequency and costs

1605.3.2 Operating Modes

Most IoT devices operate in multiple power modes:

Operating Mode Power Consumption
Mode Description Typical Current
Active Processing, transmitting, sensing 10-300 mA
Sleep CPU halted, peripherals on 0.1-2 mA
Deep Sleep Minimal functions, RTC only 0.001-0.1 mA

1605.3.3 Duty Cycle Impact

%% fig-alt: Diagram showing how duty cycle affects average current consumption, with three scenarios showing high active time leading to short battery life, balanced duty cycle for moderate life, and aggressive sleep strategy for maximum battery life.
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flowchart LR
    subgraph High["High Active (10%)"]
        H1["10% Active @ 100mA"]
        H2["90% Sleep @ 1mA"]
        H3["Avg: 10.9mA"]
        H4["~8 days on 2000mAh"]
    end

    subgraph Balanced["Balanced (1%)"]
        B1["1% Active @ 100mA"]
        B2["9% Sleep @ 1mA"]
        B3["90% Deep @ 0.01mA"]
        B4["Avg: 1.1mA"]
        B5["~76 days on 2000mAh"]
    end

    subgraph Aggressive["Aggressive Sleep"]
        A1["0.1% Active @ 100mA"]
        A2["0.9% Sleep @ 1mA"]
        A3["99% Deep @ 0.01mA"]
        A4["Avg: 0.12mA"]
        A5["~2 years on 2000mAh"]
    end


%% fig-alt: Decision flowchart for selecting power strategy based on battery life requirements, showing decision points for target lifetime leading to appropriate duty cycle and component selection recommendations.
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#FFFFFF', 'primaryBorderColor': '#16A085', 'lineColor': '#7F8C8D', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#FFFFFF'}}}%%
flowchart TD
    START[Target Battery Life?] --> Q1{Less than<br/>1 month?}
    Q1 -->|Yes| FLEX[Flexible Strategy<br/>Up to 10% active]
    Q1 -->|No| Q2{1-12<br/>months?}
    Q2 -->|Yes| BAL[Balanced Strategy<br/>1-2% active<br/>90% deep sleep]
    Q2 -->|No| Q3{1-5<br/>years?}
    Q3 -->|Yes| AGG[Aggressive Sleep<br/>< 0.5% active<br/>Use low-power MCU]
    Q3 -->|No| EXTREME[Extreme Optimization<br/>< 0.1% active<br/>nRF52 + BLE only<br/>Energy harvesting?]

    FLEX --> R1[Wi-Fi OK<br/>GPS possible<br/>Display allowed]
    BAL --> R2[BLE preferred<br/>Batch readings<br/>E-Paper display]
    AGG --> R3[LoRa or BLE<br/>No display<br/>Sensor fusion]
    EXTREME --> R4[BLE beacons<br/>Solar panel<br/>Minimal sensing]

    style START fill:#2C3E50,stroke:#16A085,stroke-width:2px,color:#fff
    style FLEX fill:#E74C3C,stroke:#2C3E50,stroke-width:2px,color:#fff
    style BAL fill:#E67E22,stroke:#2C3E50,stroke-width:2px,color:#fff
    style AGG fill:#27AE60,stroke:#2C3E50,stroke-width:2px,color:#fff
    style EXTREME fill:#16A085,stroke:#2C3E50,stroke-width:2px,color:#fff

This decision tree helps you select the right power strategy based on your battery life requirements. Start with your target lifetime and follow the path to find recommended duty cycles and component choices.

1605.3.4 Average Current Calculation

The average current is calculated as:

\[I_{avg} = (I_{active} \times D_{active}) + (I_{sleep} \times D_{sleep}) + (I_{deep} \times D_{deep})\]

Where \(D\) represents the duty cycle fraction for each mode.

1605.3.5 Battery Life Estimation

Battery life is estimated using:

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

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

WarningReal-World Considerations

The calculator provides estimates. Real battery life depends on: - Temperature effects on battery capacity - Self-discharge over time - Voltage regulation efficiency - Peak current handling - Battery aging

1605.3.6 Component Power Comparison

%% fig-alt: Bar chart comparison of active mode power consumption for different IoT components, showing Wi-Fi and Cellular as highest consumers, followed by GPS, then displays, MCUs, and sensors as lowest.
%%{init: {'theme': 'base', 'themeVariables': {'primaryColor': '#2C3E50', 'primaryTextColor': '#FFFFFF', 'primaryBorderColor': '#16A085', 'lineColor': '#7F8C8D', 'secondaryColor': '#ECF0F1', 'tertiaryColor': '#FFFFFF'}}}%%
flowchart TB
    subgraph Radio["Radio Modules"]
        R1["Cellular: 250mA"]
        R2["Wi-Fi: 200mA"]
        R3["LoRa: 120mA"]
        R4["BLE: 12mA"]
    end

    subgraph MCU["Microcontrollers"]
        M1["ESP32: 80mA"]
        M2["STM32L4: 8mA"]
        M3["nRF52: 5mA"]
        M4["ATtiny: 3mA"]
    end

    subgraph Sensors["Sensors"]
        S1["GPS: 45mA"]
        S2["Mic: 1.5mA"]
        S3["Accel: 0.5mA"]
        S4["Temp: 0.35mA"]
    end

1605.3.7 Optimization Strategies

TipPower Optimization Checklist
  1. Maximize deep sleep time - Target >90% in deep sleep
  2. Choose efficient radios - BLE over Wi-Fi when possible
  3. Batch transmissions - Accumulate data, send less frequently
  4. Use hardware peripherals - Offload from CPU where possible
  5. Optimize code - Reduce active time duration
  6. Disable unused peripherals - Power down unused components
  7. Use appropriate clock speeds - Lower frequency = lower power

1605.4 Common Device Profiles

Use these reference configurations for typical IoT applications:

Device Type MCU Radio Battery Target Life Typical Duty Cycle
Smart Home Sensor nRF52 BLE CR2032 (225mAh) 2+ years 99.9% deep sleep
Agricultural Monitor STM32L4 LoRa 2×AA (3000mAh) 5+ years 99.5% deep sleep
Wearable Fitness nRF52 BLE LiPo (150mAh) 1 week 95% sleep
Asset Tracker ESP32 Cellular Li-Ion (2000mAh) 1 month 98% deep sleep
Industrial Gateway ESP32 Wi-Fi Li-Ion (5000mAh) N/A (mains) 10% sleep

1605.5 Knowledge Check

Knowledge Check: Power Budget Fundamentals Quick Check

❓ Question 1: A sensor sends data every 10 minutes, taking 2 seconds to transmit. What percentage of time is the device in deep sleep?

💡 Explanation: 10 minutes = 600 seconds. Active time = 2 seconds. Sleep time = 598 seconds. Sleep percentage = 598/600 = 99.67%. This high duty cycle is typical for battery-powered sensors - the key to year-long battery life.

❓ Question 2: An ESP32 with Wi-Fi uses 200mA active, 0.01mA deep sleep. With 99% deep sleep, what’s the average current?

💡 Explanation: Average = (1% × 200mA) + (99% × 0.01mA) = 2mA + 0.01mA ≈ 2mA. Even at 99% sleep, the 1% active time dominates due to 20,000× current difference. This is why minimizing active time AND active current both matter.

❓ Question 3: Which change provides the BIGGEST battery life improvement for a Wi-Fi sensor sending data every 5 minutes?

💡 Explanation: Radio dominates active power (200mA vs 80mA MCU). Switching to LoRa saves 80mA during transmission - the largest single component reduction. Deep sleep current is already negligible. Doubling battery helps but doesn’t fix the fundamental efficiency problem.

1605.6 What’s Next

Explore related power and energy topics: