SOH Estimator (Capacity-Based)

State of health estimator based on capacity fade

Since R2023b

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Libraries:
Simscape / Battery / BMS / Estimators

Description

This 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:

$$SO{H}_{\text{Q}}=\frac{Q-Q_{\text{eol}}}{Q_{\text{eol}}-Q_{\text{new}}},$$

where Q_eol is the end-of-life capacity and Q_new 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.

Open Model

New

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.

Since R2024a

Open Model

Ports

Input

AH — Cell capacity

scalar | vector

Cell capacity, in ampere-hour, specified as a scalar for a single cell or a vector for multiple cells.

Output

SOH — Battery state of health

scalar | vector

State of health of the battery, returned as a scalar or vector with entries in the range [0, 1]. The size of this output port is equal to the size of the AH input port.

Parameters

Cell capacity when battery is new, AH_new, (A*Hr) — Cell capacity when battery is new

27 (default) | nonnegative scalar

Cell capacity, in ampere-hour, when the battery is new.

Cell capacity at end of life, AH_eol, (A*Hr) — Cell capacity at end of life

21.6 (default) | nonnegative scalar

Cell capacity, in ampere-hour, when the battery is at the end of its life.

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.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2023b