A novel state of health estimation approach based on polynomial model for lithium-ion batteries

Abstract

Energy management systems are one of the most critical components in a battery unit's safe and efficient operation. Many measurable or calculable parameters, such as voltage, current, capacity, and internal resistance, are considered when designing energy management systems. Parameters that cannot be measured or are costly to measure, such as battery state of charge (SOC) or battery health state (SOH), can be determined by estimation. SOH, which can be used as both an input and an output parameter for EMS, is an essential parameter for the long-life and high-efficiency use of the battery. Two different approaches, experimental-based and model-based, are used for SOH estimation. Experimental-based methods need all the real-time data of the batteries, which is caused the requirement of a very long time and data storage. The adaptive filtering technique, which is a model-based technique, requires a complete and accurate model of the battery. The nonlinear electrochemical structures of batteries make modeling difficult. On the other hand, optimization-based methods, a data-driven technique, need very high processing power to find the optimal result. In addition, separate data is needed for training and testing in machine learning-based methods and requires high processing power. Contrary to these disadvantages, high-accuracy predictions can be made using low processing power and very little data in the experimental curve fitting technique. In this study, experimental and curve-fitting-based SOH estimation is proposed by using polynomial-based system identification techniques. Polynomial-based identification methods can model the system with high accuracy, low cost, and low processing power. Battery degradation dataset belonging to Oxford University is used. In the aforementioned dataset, the general model inference is made using only %1 of the entire cycle life of the battery. Obtained proposed model's results were compared with different methods in the literature in terms of RMSE, and its superiority was demonstrated.