Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

Ines Baccouche, Sabeur Jemmali, Bilal Manai, Noshin Omar and Najoua Essoukri Ben Amara
Additional contact information
Ines Baccouche: LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia
Sabeur Jemmali: LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia
Bilal Manai: IntelliBatteries Company, SoftTech Firm Incubator, Technopole of Sousse, BP 24 Sousse Corniche 4059, 4002 Sousse, Tunisia
Noshin Omar: MOBI-Mobility, Logistics and Automotive Technology Research Center, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Najoua Essoukri Ben Amara: LATIS-Laboratory of Advanced technology and Intelligent Systems, ENISo, Sousse University, BP 526, 4002 Sousse, Tunisia

Energies, 2017, vol. 10, issue 6, 1-22

Abstract: Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

Keywords: Li-ion batteries; open circuit voltage; battery modeling; battery characterization; state of charge estimation; extended Kalman filter (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

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