SOH Estimator (Capacity-Based)
Libraries:
Simscape /
Battery /
BMS /
Estimators
Description
The SOH Estimator (Capacity-Based) block implements an estimator that calculates the state of health (SOH) of a battery based on the capacity fade. The SOH reflects the condition of a battery relative to its ideal conditions. Health variations imply that the capacity and power of the battery fade over time.
This block supports single-precision and double-precision floating-point simulation.
Note
To enable single-precision floating-point simulation, the data type of all inputs and
parameters must be single.
This diagram shows the structure of the block:
Equations
The cell capacity is an important indicator of the aging of a battery. During the life cycle of a battery, the cell capacity decreases over time. Usually, the cell SOH is 0 when the cell capacity decreases to about 80% of its nominal value.
The SOH Estimator (Capacity Based) block computes the SOH as a function of the cell capacity Q:
where Qeol is the end-of-life capacity and Qnew is the capacity when the battery is new.
Examples
Battery State-of-Health Estimation
Estimate the battery internal resistance and state-of-health (SOH) by using an adaptive Kalman filter. The initial state-of-charge (SOC) of the battery is equal to 0.6. The estimator uses an initial condition for the SOC equal to 0.65. The battery keeps charging and discharging for 10 hours. The unscented Kalman filter estimator converges to the real value of the SOC while also estimating the internal resistance. To use a different Kalman filter implementation, in the SOC Estimator (Kalman Filter) block, set the Filter type parameter to the desired value.
Estimate Battery State of Health Based on Capacity Fade
Estimate the battery capacity and state of health (SOH) by using a Kalman filter. The initial state of charge (SOC) of the battery is equal to 0.5. The estimator uses an initial condition for the SOC equal to 0.8. The battery keeps charging and discharging for 50 hours. The example estimates the battery capacity, in ampere-hour, and the SOC by using an extended Kalman Filter. The estimation error for the battery capacity is less than 4%. The SOC is estimated using an extended Kalman filter. When using fixed capacity the estimated SOC value diverges from the true value. To demonstrate the functionality of the estimator and to restrict the duration of the simulation, this example models an increased capacity fade rate.
Ports
Input
Output
Parameters
Cell capacity, in ampere-hour, when the battery is new.
Cell capacity, in ampere-hour, when the battery is at the end of its life.
Since R2025a
Option to choose the data type for the block algorithm, specified as one of these values:
Inherit: auto— You can simulate the block in bothsingleanddoubleprecision. You must explicitly provide the inputs and parameters as eithersingleordouble.double— The block algorithm casts all inputs and parameters todoubledata type.single— The block algorithm casts all inputs and parameters tosingledata type.<data type expression>— The block algorithm casts all inputs and parameters to the data type object you specify.
Click the Show data type assistant button 
to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant and Control Data Types of Signals.
References
[1] Noura, Nassim, Loïc Boulon, and Samir Jemeï. “A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges.” World Electric Vehicle Journal 11, no. 4 (October 16, 2020): 66. https://doi.org/10.3390/wevj11040066.
