A new neural network model for the state-of-charge estimation in the battery degradation process

Kang, Longyun; Zhao, Xuan; Ma, Jian

doi:10.1016/j.apenergy.2014.01.066

Cited by 283 publications

(111 citation statements)

References 22 publications

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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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mentioning

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 [30], Mehra introduces four adaptive EKFs, among which the covariance-matching variation is utilized in this work. Combining Equations (3), (6) and (7), we can obtain the state-space expression as…”

Section: Soc Estimation Based On Aekfmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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To achieve accurate state-of-charge (SoC) estimation for LiFePO 4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert's law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO 4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

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“…Thus according to Equation (18), SOC pack (k) can smoothly transfer between SOC max (k) and SOC min (k). Condition:…”

Section: Battery Pack State Of Charge Estimationmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

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A new neural network model for the state-of-charge estimation in the battery degradation process

Cited by 283 publications

References 22 publications

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

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