State of Charge, State of Health, and Degradation Estimation for Second-Life Batteries for Maximum Demand Reductions

Accurate state of charge (SOC), state of health (SOH), and degradation estimation are important when using second-life batteries for maximum demand reduction applications. This paper presents long short-term memory (LSTM) for SOC estimation, incremental capacity method for SOH estimation, and an equation-based model for battery degradation estimation. The SOC model is compared with other estimation methods and results show that it is suitable for second-life batteries as it does not require a battery model, initial SOC value, and regular calibration. The SOH model is used to estimate the SOH of second-life batteries installed on a university campus. The results from the degradation model are validated against an existing MATLAB model. Results show that the average difference between proposed model and the MATLAB model is 0.096.