Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7730275 | Journal of Power Sources | 2015 | 13 Pages |
Abstract
Robust algorithms using reduced order equivalent circuit model (ECM) for an accurate and reliable estimation of battery states in various applications become more popular. In this study, a novel adaptive, self-learning heuristic algorithm for on-board impedance parameters and voltage estimation of lithium-ion batteries (LIBs) in electric vehicles is introduced. The presented approach is verified using LIBs with different composition of chemistries (NMC/C, NMC/LTO, LFP/C) at different aging states. An impedance-based reduced order ECM incorporating ohmic resistance and a combination of a constant phase element and a resistance (so-called ZARC-element) is employed. Existing algorithms in vehicles are much more limited in the complexity of the ECMs. The algorithm is validated using seven day real vehicle data with high temperature variation including very low temperatures (from â20 °C to +30 °C) at different Depth-of-Discharges (DoDs). Two possibilities to approximate both ZARC-elements with finite number of RC-elements on-board are shown and the results of the voltage estimation are compared. Moreover, the current dependence of the charge-transfer resistance is considered by employing Butler-Volmer equation. Achieved results indicate that both models yield almost the same grade of accuracy.
Related Topics
Physical Sciences and Engineering
Chemistry
Electrochemistry
Authors
Alexander Farmann, Wladislaw Waag, Dirk Uwe Sauer,