Abstract
State of health (SOH) estimation is always an important factor in ensuring the reliability and safety of lithium-ion batteries. In view of the shortcomings of the existing SOH estimation methods, such as non-universal, the estimation of different batteries is limited, and the accuracy is insufficient. A fusion estimation method that depends on an empirical degradation model and a data-driven method is proposed. First, we construct an empirical degradation model of lithium-ion battery SOH with charge-discharge cycles. Four working condition characteristics are extracted from the actual charging and discharging process of batteries. Then, with these features as inputs, the prediction error of the empirical degradation model is taken as the output, and training the error compensation model becomes dependent on the data-driven method. The actual working condition characteristics of the tested lithium ion battery are substituted into the training error compensation model, and the model output is fed back to the prediction results of the empirical degradation model. A high-precision estimation of lithium-ion battery SOH is thereby achieved. Finally, the proposed method is verified based on the NASA lithium-ion battery data set. The results show that the fusion method is applicable to different lithium-ion batteries of the same type, and the mean absolute percentage error of SOH estimation is approximately 2%, indicating that the proposed method exhibits good estimation performance and applicability.
Highlights
Lithium-ion batteries are widely used in electric vehicles, communication equipment, aerospace and other fields due to their high energy density, long cycle life and high safety performance [1]–[3]
This paper proposes a prediction framework based on the fusion of empirical degradation models and data-driven methods to solve the problems of lithium-ion battery health estimation and remaining life prediction, and experimentally validates and evaluates the proposed method based on National Aeronautics and Space Administration (NASA) PCoE battery test data
The work of this paper has two main aspects: first, an empirical degradation model of lithium-ion battery capacity is established; secondly, the actual information of four operating conditions is extracted from the charging and discharging conditions of the lithium-ion battery, and an error compensation model based on a data-driven method is established to describe the difference in operating conditions The impact of battery performance on battery degradation improves the accuracy of state of health (SOH) estimation during battery degradation
Summary
Lithium-ion batteries are widely used in electric vehicles, communication equipment, aerospace and other fields due to their high energy density, long cycle life and high safety performance [1]–[3]. The state of health (SOH) estimation of lithium-ion batteries can effectively evaluate the degree of performance degradation and provide an important assurance for the stable operation of the power system. The SOH estimating methods for lithium-ion batteries are mainly divided into three categories: model-based, The associate editor coordinating the review of this manuscript and approving it for publication was Cristian Zambelli. The model-based methods mainly include electrochemical models [4]–[7], equivalent circuit models (such as the Thevenin model [8], [9], RC model [10]–[14], etc.) and empirical degradation models [15], [16]. Electrochemical and equivalent circuit models are based on the internal physicochemical properties of lithium-ion batteries.
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