Project 17: Power and Battery Optimization Lab (Sleep, DFS, Radio Tuning)

Build a policy-driven power manager that quantifies and optimizes battery life without breaking responsiveness.

Quick Reference

Attribute Value
Difficulty Expert
Time Estimate 2 weeks
Main Programming Language C
Alternative Programming Languages C++, Rust
Coolness Level Level 4
Business Potential Level 4
Prerequisites ESP-IDF power APIs, profiling basics
Key Topics Light/deep sleep, wake sources, dynamic frequency scaling, Wi-Fi power policy

1. Learning Objectives

  1. Measure real current draw per mode and workload.
  2. Implement policy transitions across active/sleep states.
  3. Tune display/radio settings for runtime gains.
  4. Estimate battery health trends over cycle usage.

2. Theory

2.1 Power State Economics

Battery life is shaped by transition policy, not just low-power mode availability.

2.2 Wake and Resume Experience

Aggressive sleep can damage UX unless wake latency is engineered deliberately.

3. Specification

  • Multi-mode power policy (performance, balanced, saver, ultra).
  • Wake source routing and latency logs.
  • Battery health telemetry model.

Output:

I pwr: mode=balanced current=149mA
I pwr: light_sleep current=31mA wake=timer
I pwr: deep_sleep current=0.23mA wake=gpio
I pwr: wake_latency light=7ms deep=182ms

4. Architecture

[Policy Engine] -> [Clock Manager]
               -> [Display Manager]
               -> [Radio Manager]
               -> [Sleep Controller]

5. Implementation Guide

Core question:

“Which policy delivers the best battery life per user-visible value?”

Design questions:

  1. When should light sleep transition to deep sleep?
  2. How is wake source priority determined?
  3. How is low-battery UX communicated?

6. Testing

  • Per-mode current profile runs.
  • Wake source correctness checks.
  • Mixed workload battery runtime tests.

7. Pitfalls

  • Ignoring peripheral leakage.
  • Deep sleep overuse causing poor interactivity.
  • Wi-Fi reconnect storms from aggressive sleep windows.

8. Extensions

  • Adaptive policy via learned usage patterns.
  • Battery aging compensation model.

9. Completion

  • Power policy reduces runtime draw measurably.
  • Wake behavior is deterministic and documented.
  • Battery/runtime tradeoffs are quantified.