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State of Charge and parameter estimation for a 48V Lithium-Ion battery based on temperature dependent second-order RC model

Leonardo Pasquali

State of Charge and parameter estimation for a 48V Lithium-Ion battery based on temperature dependent second-order RC model.

Rel. Angelo Bonfitto, Sara Luciani, Pier Giuseppe Anselma. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

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As part of the midterm evaluation of the 2022-2025 Light-Duty Vehicle Greenhouse Gas (GHG) Standards, the U.S. Environmental Protection Agency (EPA) developed simulation models for studying the effectiveness of 48V mild hybrid electric vehicle (MHEV) technology for reducing CO2 emissions from light-duty vehicles. Simulation and modeling of this technology requires a suitable model of the battery. The goal of this thesis work is to define an equivalent model of a lithium-ion battery (LiF eP O4) 48V for mild hybrid applications, which is able to correctly simulate its behavior. The battery model is a standard equivalent circuit model with the two-time constant resistance-capacitance (RC) blocks. Resistances and capacitances were modeled using lookup tables, allowing flexibility for the model, to closely match measured data. Pulse discharge curves and charge curves are collected experimentally to characterize the battery performance at various operating points depending on state of charge and temperature. It can be extremely difficult to fit the simulation model to the experimental data using optimization algorithms, due to the number of values in the lookup tables. This challenge is addressed using a layered approach to break the parameter estimation problem into smaller tasks. The size of each estimation task is reduced to a small subset of data and parameter values, so that the optimizer can better focus on a specific problem. The layered approach was successful in fitting an equivalent circuit model a data set. Moreover the model has been validated with different currents profile such as RW and WLTP, which simulates behaviour on road: urban, suburban roads and highway. Furthermore, the model would be the starting point for building state of charge and health estimators of a battery by means of an unscented Kalman filter (UKF). These estimators are essential for the correct management of the battery system during its operation. In fact, depending on the operating conditions, it could be called upon to deliver / absorb certain quantities of energy according to certain power profiles. This thesis work was supervised and done with the LIM mechatronics lab at Politecnico di Torino which is a leading institution in the field of engineering research, especially in automotive.

Relators: Angelo Bonfitto, Sara Luciani, Pier Giuseppe Anselma
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 98
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Politecnico di Torino
URI: http://webthesis.biblio.polito.it/id/eprint/23537
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