SLUAB20A May   2025  – July 2025 BQ41Z50 , BQ41Z90

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1Introduction
  5. 2Battery Gauging Algorithm Background
  6. 3Battery Modeling
  7. 4Battery State of Charge Estimation and Remaining Capacity Prediction
  8. 5Challenges Modeling Battery Response to Dynamic Load Currents
  9. 6Approaches to Deal with Battery Dynamics
    1. 6.1 Benefits to Gauging Accuracy for Dynamic Loads
    2. 6.2 Algorithm Performance
  10. 7Summary
  11. 8References
  12. 9Revision History

7 Summary

The Dynamic Z-Track gauging algorithm enables more accurate estimation of the battery resistance and remaining capacity for applications with dynamic load currents. The Dynamic Z-Track relies on a broadband battery model that compensates for battery relaxation in the resistance estimation portion of the gauging algorithm. Accurate tracking of the battery resistance throughout the life cycle enables accurate remaining capacity prediction of the instant when the battery terminal voltage reaches the minimum value for operation of the battery-powered system. The Dynamic Z-Track enhances gauging performance for a wide range of applications such as drones, robots, power tools, and AI-enhanced portable electronics.

A comparison of the performance of Dynamic Z-Track and Impedance Track for stable and dynamic loads is shown in Table 7-1. To determine if a system benefits from Dynamic Z-Track, reach out to a TI representative.

Table 7-1 Comparison of Dynamic Z-Track™ and Impedance Track™
Load Dynamic Z-Track™Impedance Track™CEDV
Resistance estimationState of healthRemCapResistance estimationState of healthRemCapResistance estimationState of healthRemCap
StableAccurateYesYesAccurateYesYesFixed at new cell valueAccuracy declines with ageAccuracy declines with age
Pulsed<10% worst-case error vs. SOC and TYesYes<100% worst-case error vs. SOC and TPossible for constrained current pulse shapeNo<100% worst-case error vs. SOC and TPossible for constrained current pulse shapeNo