Abstract
State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.
| Original language | English |
|---|---|
| Title of host publication | Modeling, Dynamics, and Control of Electrified Vehicles |
| Publisher | Elsevier |
| Pages | 1-38 |
| Number of pages | 38 |
| ISBN (Electronic) | 9780128127865 |
| ISBN (Print) | 9780128131091 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
Keywords
- AEKF algorithm
- Electric vehicles
- dual timescales
- evaluation of models
- lithium-ion battery
- modeling
- state of charge
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Mu, H., & Xiong, R. (2017). Modeling, Evaluation, and State Estimation for Batteries. In Modeling, Dynamics, and Control of Electrified Vehicles (pp. 1-38). Elsevier. https://doi.org/10.1016/B978-0-12-812786-5.00001-X