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Robust Data-Driven Battery State of Charge Estimation for Hybrid Electric Vehicles
Journal Article
14-11-02-0017
ISSN: 2691-3747, e-ISSN: 2691-3755
Sector:
Topic:
Citation:
Feraco, S., Anselma, P., Bonfitto, A., and Kollmeyer, P., "Robust Data-Driven Battery State of Charge Estimation for Hybrid Electric Vehicles," SAE Int. J. Elec. Veh. 11(2):213-230, 2022, https://doi.org/10.4271/14-11-02-0017.
Language:
English
Abstract:
This article presents a method to estimate the State of Charge (SOC) in
Lithium-ion batteries of Hybrid Electric Vehicles (HEVs) with Artificial Neural
Networks (ANNs). The inputs of the SOC estimation algorithm are the measured
values of current, voltage, and temperature. In the article, two different
battery packs are considered for a power-split full HEV. The training and
validation datasets needed for developing the ANNs are generated exploiting a
numerical model of two different configurations of an HEV performing real-world
driving missions or the Worldwide Harmonized Light Vehicle Test Procedure (WLTP)
cycle, while the testing dataset is collected experimentally on battery cells.
Specifically, the capacity values for the considered battery pack sizes are 1.82
kWh and 1.06 kWh. The proposed method uses a Nonlinear AutoRegressive with
eXogenous input (NARX) recurrent ANN, which has been observed to have reasonable
computational cost in prior research. The performance of the investigated
technique is demonstrated by estimating the SOC with a low estimation error for
both the considered battery sizes. Coulomb counting is used to compute the
reference value of the SOC during the real charge/discharge cycles. An analysis
of the robustness of the proposed estimation method to offset errors on the
measured input current is also performed.