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A Study on Battery Management System for Lithium-ion battery: Novel Estimation methods for State-of-Charge and State-of-Health

A Study on Battery Management System for Lithium-ion battery: Novel Estimation methods for State-of-Charge and State-of-Health
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This thesis addresses the novel estimation methods for state-of-charge (SOC) and state-of-health (SOH) of Lithium-ion battery. For the SOC estimation, in Chapter 2, two approaches are proposed: firstapproach estimates the SOC based on a second-order discrete time sliding mode observer (DSMO), and second approach uses a grey prediction-based fuzzy sliding mode observer (GP-FSMO). In the first proposed SOC estimation method, a second-order discrete-time sliding mode observer (DSMO)-based methodis proposed to estimate the state of charge (SOC) of a Li-ion battery. Unlike the first-order sliding mode approach, the proposed method eliminates the chattering phenomenon in SOC estimation. Further, a battery model with a dynamic resistance is also proposed to improve the accuracy of the batterymodel. Similar to actual battery behavior, the resistance parameters in this model are changed by both the magnitude of the discharge current and the SOC level. Validation of the dynamic resistance model is performed through pulse current discharge tests at two different SOC levels. Our experimental results show that the proposed estimation method not only enhances the estimation accuracy but also eliminates the chattering phenomenon. The SOC estimation performance of the second-order DSMO is compared with that of the first-order DSMO. In the second proposed SOC estimation method, a fuzzy sliding mode observer (FSMO) combined with grey prediction is proposed. The proposed method eliminateschattering in SOC estimation in contrast of the existing methods based on a conventional first-order sliding mode observer (SMO) and an adaptive gain SMO. In this method, which uses a fuzzy inferencesystem, the gains of the SMO are adjusted according to the predicted future error and present estimation error of the terminal voltage. To forecast the future error value, a one-step-ahead terminal voltage prediction is obtained using a grey predictor. The proposed estimation method is validated through two types of discharge tests (a pulse discharge test and a random discharge test). The SOC estimation results are compared to the results of the conventional first-order SMO-based and the adaptive gain SMO-based methods. The experimental results show that the proposed method not only reduces chattering, but also improves estimation accuracy. For the SOH estimation, in Chapter 3, we proposeanovel data-driven approach for capacity estimation of lithium-ion battery packs. The approach is based on statistical features extracted from a current curve under a constant voltage (CV) chargingstep. The duration CV charging increases as a battery ages. The age-related changes in the current of this step are statistically characterized by means of the statistical features extracted. The relationships between these features and the battery capacity are developed using a multiple linear regression model and two artificial neural network models: a multi-layer perceptron network and a radial basisfunction (RBF) network. A feature data set extracted from 260 CV charging current curves atvarious capacity is used for model development and validation. The performance of the three regression modelsare compared in terms of root mean square error, mean absolute percentage error, and coefficient ofdetermination. Experimental results show that the constructed RBF model exhibits a higherperformance than the other two regression models in terms of capacity estimation.
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