A method to address the challenges of charging conditions on incremental capacity analysis: An ICA-compensation technique incorporating current interrupt methods

This study employs estimated equivalent impedance to counteract additional polarization effects for approximating the quasi-static IC curve. The resulting curves help create an SOH estimation framework utilizing the ICA technique, especially under variable charging conditions. The incremental capacity analysis (ICA) technique is notably limited by its sensitivity to variations in charging conditions, which constrains its practical applicability in real-world scenarios. This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health (SOH) of batteries based on ICA that is applicable under differing charging conditions. This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile. This approach’s efficacy is contingent upon precisely acquiring the equivalent impedance. To obtain the equivalent impedance throughout the batteries’ lifespan while minimizing testing costs, this study employs a current interrupt technique in conjunction with a long short-term memory (LSTM) network to develop a predictive model for equivalent impedance. Following the derivation of ICA curves using voltage profiles under quasi-static conditions, the research explores two scenarios for SOH estimation: one utilizing only incremental capacity (IC) features and the other incorporating both IC features and IC sampling. A genetic algorithm-optimized backpropagation neural network (GA-BPNN) is employed for the SOH estimation. The proposed generalized framework is validated using independent training and test datasets. Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions. These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04% for RMSE and 0.90% for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0% and 70%, which constitutes a major advancement compared to established ICA methods. It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.