Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

Panpan Hu, W. F. Tang, C. H. Li, Shu-Lun Mak, C. Y. Li () and C. C. Lee ()
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Panpan Hu: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
W. F. Tang: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
C. H. Li: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Shu-Lun Mak: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
C. Y. Li: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
C. C. Lee: School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

Energies, 2023, vol. 16, issue 14, 1-19

Abstract: Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

Keywords: lithium-ion batteries pack; EVs; SOC; SOH; joint estimation; NSSR-LSTM; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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