Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales

Dai, Haifeng; Xu, Tianjiao; Zhu, Letao; Wei, Xuezhe; Sun, Zhonghui

doi:10.1016/j.apenergy.2016.10.020

Cited by 114 publications

(50 citation statements)

References 27 publications

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“…Remmlinger et al [13] used a specific excitation signal, found during normal operation of a HEV, along with the RLS algorithm to estimate battery impedance. Finally, Dai et al [14] proposed a parameter estimation framework composed of two modules running on different time-scales. The EKF algorithm was applied for estimating the parameters associated with slow battery dynamics, while the RLS algorithm was used for modeling the faster battery dynamics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

et al. 2018

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Safe and efficient operation of a battery pack requires a battery management system (BMS) that can accurately predict the pack state-of-heath (SOH). Although there is no universal definition for battery SOH, it is often defined based on the increase in the battery's internal resistance. Techniques such as extended Kalman filter (EKF) and recursive least squares (RLS) are two frequently used approaches for online estimation of this resistance. These two methods can, however, be computationally expensive, especially in the case of a battery pack composed of hundreds of cells. In addition, both methods require a battery model as well as chemistry specific parameters. Therefore, this paper investigates the performance of a direct resistance estimation (DRE) technique that requires minimal computational resources and can be implemented without any training data. This approach estimates the ohmic resistance only when the battery experiences sharp pulses in current. Comparison of results from the three algorithms shows that the DRE algorithm can accurately identify a degraded cell under various operating conditions while significantly reducing the required computational complexity. The findings will further advance diagnostic techniques for the identification of a weak cell in a large battery pack.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

et al. 2018

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“…20,34 As they are relatively simple to implement and computationally fast, empirical models are frequently found in literature. [34][35][36][37][38][39][40][41][42] However, their application is limited as they can only describe a previously seen and implemented behavior, so an adaption to another cell or even chemistry needs a completely new database. 19,20 Previous literature described several degradation mechanisms on anode as well as cathode in a P2D model.…”

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confidence: 99%

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Kindermann

Keil

Frank

et al. 2017

J. Electrochem. Soc.

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In this paper we introduce a pseudo two-dimensional (P2D) model for a common lithium-nickel-cobalt-manganese-oxide versus graphite (NCM/graphite) cell with solid electrolyte interphase (SEI) growth as the dominating capacity fade mechanism on the anode and active material dissolution as the main aging mechanism on the cathode. The SEI implementation considers a growth due to non-ideal insulation properties during calendar as well as cyclic aging and a re-formation after cyclic cracking of the layer during graphite expansion. Additionally, our approach distinguishes between an electronic (σ SEI ) and an ionic (κ SEI ) conductivity of the SEI. This approach introduces the possibility to adapt the model to capacity as well as power fade. Simulation data show good agreement with an experimental aging study for NCM/graphite cells at different temperatures introduced in literature. © The Author Lithium-ion batteries are one of the most promising candidates for energy storage in future stationary storage systems and electric vehicles.1-3 Enormous research efforts have been conducted to get a thorough understanding of the system "lithium-ion cell" and to further develop it for higher energy and power density, higher safety standards as well as longer cycle life. 4 The aging behavior of lithium-ion batteries has been a focus issue of battery research since the introduction of lithium-ion cells by Sony in 1991. 5 Reviews by Agubra et al., 6,7 Arora et al., 8 Aurbach et al., 9,10 Birkl et al., 11 Broussely et al., 12 Verma et al. 13 and Vetter et al. 14 are just a few examples of the extensive literature regarding aging behavior. Commonly accepted and experimentally verified aging phenomena as mentioned in the previously cited literature are electrolyte decomposition leading to solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) growth, solvent co-intercalation, gas evolution with subsequent cracking of particles, a decrease of accessible surface area and porosity due to SEI growth, contact loss of active material particles due to volume changes during cycling, binder decomposition, current collector corrosion, metallic lithium plating and transition-metal dissolution from the cathode. The listed aging mechanisms can be assigned to three different categories that are a loss of lithium-ions (LLI), an impedance increase and a loss of active material (LAM). 12,[15][16][17][18] The LLI is synonymous to a decrease in the amount of cyclable lithium-ions as they are trapped in a passivating film on either of the electrodes or in plated metallic lithium. Due to the growth of the passivating layers and/or the formation of rock-salt in the cathode (residue of the cathode active material after transition-metal dissolution), kinetic transport of lithium-ions through those inactive areas is limited and results in an impedance rise. An LAM can be caused by the dissolution of transition-metal-ions from the cathode bulk material, changes in the electrode composition and/or changes in crystal structure of the a...

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“…However, the RELS with constant forgetting factor may encounter the difficulties of balancing between stability and convergence if the model parameters change with different rates. The battery dynamics nature of one-order ECM can be distinguished into two slow parameters and one fast parameter [38][39][40]. The fast parameter represents internal ohmic resistance 0 , while the slow parameters include and .…”

Section: Parameters Identificationmentioning

confidence: 99%

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales

Cited by 114 publications

References 27 publications

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

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