Estimators
Estimate the state of charge, the terminal resistance, and the state of health of batteries by using coulomb counting and Kalman filters algorithms.
Blocks
Featured Examples
Estimate Maximum Charging and Discharging Power of Battery Module
Estimate the maximum charging and discharging power of a battery module by using the Battery Power Estimator block.
Estimate Battery State of Charge Using Bar-Delta Filtering
Estimate the state of charge (SOC) of a battery cell by using bar-delta filtering. The battery pack comprises five series-connected cells. Each battery cell has an initial SOC that varies between 0.45 and 0.65. The estimation technique uses a pack bar SOC estimator to obtain the pack-average SOC. A cell delta SOC estimator uses the pack-average SOC to estimate the cell SOC. The battery keeps charging and discharging for six hours. The estimator converges to the real value of the SOC in less than 10 minutes and then follows the real SOC value. The bar-delta filtering is computationally efficient as it uses one full Kalman filter and Ns one-state Kalman filters, where Ns is the number of series-connected cells.
Estimate State of Charge of Lithium Iron Phosphate Battery
Estimate the state of charge (SOC) of lithium iron phosphate (LFP) batteries by using the Coulomb Counting method with error correction. The Coulomb counting method is implemented at 1 second sample time. To correct the estimate of the Coulomb counting method, the example implements an extended Kalman filter with a sampling time of 10 seconds. The initial SOC of the battery is equal to 0.4. The estimator uses an initial condition for the SOC equal to 0.6. The LFP battery keeps charging and discharging for six hours. The estimator converges to the real value of the SOC in approximately half an hour and then follows the real SOC value.
Battery State-of-Charge Estimation
Estimate the battery state of charge (SOC) by using a Kalman filter. The initial 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 6 hours. The extended Kalman filter estimator converges to the real value of the SOC in less than 10 minutes and then follows the real SOC value. To use a different Kalman filter implementation, in the SOC Estimator (Kalman Filter) block, set the Filter type parameter to the desired value.
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.
Charge and Discharge Battery Module with State of Charge Estimator
Cyclically charge and discharge a battery module while estimating the state of charge (SOC) of the three parallel assemblies of the module over time. This example uses the SOC estimation to switch between the charging and discharging profiles. For the estimation, the Kalman filter uses an initial SOC estimation and the voltage, current, and temperature of the parallel assemblies. The model estimates the temperature of the parallel assemblies from the average of the cell temperatures of each parallel assembly.
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.
