How to Improve The Accuracy of SOC and SOH?

What are effective strategies and methodologies to improve the accuracy of State-of-Charge (SOC) and State-of-Health (SOH) estimates for batteries?

The primary objective in designing an accurate battery management system (BMS) is delivering precise state-of-charge (SOC) and state-of-health (SOH) assessments for determining the remaining run time and lifespan status of the battery pack. Some may assume that highly precise cell voltage measurement through expensive analog front-end (AFE) hardware is necessary to enable accuracy. However, the cell model fidelity and fuel gauge algorithm optimization have an even more significant impact than measurement resolution alone on overall calculation precision. Following are some tips to improve the accuracy of SOC and SOH estimation:

  1. Use an accurate battery model that captures key parameters like capacity fade and internal resistance increase. Having a model that closely reflects the actual battery behavior will make the SOC and SOH estimates more precise.
  2. Characterize the battery accurately by doing full charge/discharge cycles periodically. This allows you to update the parameters in the battery model to match the actual capacity and resistance.
  3. If possible, use direct measurements of battery parameters like open-circuit voltage or internal resistance in addition to current & voltage measurements. This provides more observational data to correlate with SOC and SOH.
  4. Implement algorithms like Kalman filters or particle filters that can fuse the battery model with measurements to continuously correct the SOC and SOH estimates. The fusion algorithms help reduce errors.
  5. If the battery supports it, use coulomb counting (integrating current over time) between full charge events to correct SOC drift. This gives you an absolute SOC reset point.
  6. Maintain an accurate temperature history of the cells and account for temperature effects on capacity, resistance, and charging efficiency. Knowing the temp profile will improve accuracy.
  7. Keep historical data on capacity fade and resistance increase over the battery lifetime, generated from periodic full charge/discharge cycles. Use this to correct model parameters and thus SOH accuracy.
  8. Validate and tune the model parameters and estimation algorithms through lab experiments under different conditions. Having robust and validated implementations leads to higher accuracy.

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