Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

Wang, Shunli; Fan, Yongcun; Yu, Chunmei; Jin, Siyu; Fernandez, Carlos; Stroe, Daniel‐Ioan

doi:10.1002/er.7408

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…sulfides suffer from severe pulverization, poor cycling, and irreversible capacity fading. 11,12 To address these concerns, several approaches have been explored to improve the electrochemical properties of metal sulfides by including carbonaceous materials, such as the conductive matrix, 13,14 tuning the morphology at the nanoscale, and fabricating unique morphologies for the metal sulfides, including hollows, nanosheets, spheres, and nanofibers. 9,[15][16][17] The introduction of the conductive substrates and construction of nanoscale metal sulfides are practical approaches to improving electrochemical performance.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and electrochemical properties of nickel sulfide/carbon composite as anode material for lithium‐ion and sodium‐ion batteries

Lee

Reddy

Hong

et al. 2022

Intl J of Energy Research

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Transition metal sulfides have been considered a novel anode material for lithium-ion and sodium-ion batteries (LIBs/SIBs). However, their practical applications have been limited by their relatively poor cyclic stabilities and low rate performances. This work synthesized carbon-coated nickel sulfide (NiS) composites with a core-shell structure for high-performance LIBs and SIBs by a solvothermal method. The one-step synthesis of nickel sulfide and carbon at a low temperature can affect the thin homogeneous carbon coating, the buffer volume, and the sulfur dissolution of NiS nanoparticles. Due to small particle size dominance, desirable structural flexibility, and core-shell architecture, the as-prepared nickel sulfide/carbon composites showed substantial enhancement in LIBs and SIBs. The nickel sulfide/carbon composite electrodes displayed a high reversible discharge capacity of around 500 and 360 mAh/g after 50 cycles for LIBs and SIBs. The prepared NiS anode materials deliver the enormous potential for developing huge lithium/sodium storage.

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Section: Introductionmentioning

confidence: 99%

Synthesis and electrochemical properties of nickel sulfide/carbon composite as anode material for lithium‐ion and sodium‐ion batteries

Lee

Reddy

Hong

et al. 2022

Intl J of Energy Research

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“…The more typical ones include Hu et al 38 by establishing a fractional-order equivalent circuit model and using a hybrid genetic algorithm/particle swarm optimization method for parameterization, which improves the modeling accuracy of the battery. Wang et al 39 used a new covariance matching-electrical equivalent circuit modeling method to improve the tracking effect of terminal voltage and the estimation accuracy of SOC. In addition to the model-based SOC closed-loop estimation strategy, meta-heuristic optimization algorithms have been applied to the estimation of battery state of charge by researchers.…”

Section: Introductionmentioning

confidence: 99%

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

Shi

Wang

Fernandez

et al. 2022

Intl J of Energy Research

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Temperature and cell hysteretic effects are two major factors that influence the reliability and safety in long-term management of battery-integrated systems.In this paper, a hysteresis-compensated electrical characteristic model is established to track the terminal voltage of batteries with the uncertain hysteretic effect of the open-circuit voltage. Then, an autoregressive exogenous model with multi-feature coupling is employed for the identification of the parameters to make them adaptive to the uncertainties of the temperature and hysteretic effects. After that, a novel method for state-of-charge (SOC) estimation based on an adaptive moving window-square root unscented Kalman filter is constructed to avoid the filtering divergence problem caused by the negative error covariance matrix. Multiple constraints, such as Coulombic efficiency, varying ambient temperatures, and hysteresis voltage, are considered for the SOC estimation. Experimental results show that the root-mean-square error for SOC calculation can be limited to 0.0211 when the temperature varied up to 40 C and the root-mean-square error of the voltage measurement noise up to 61.9 mV. The proposed method provides an effective way for batteryintegrated management of electric vehicles. K E Y W O R D Sadaptive moving window-square root unscented Kalman filter, adaptive noise matching, hysteresis-compensated modeling, lithium-ion battery, state-of-charge

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“…More importantly, it has fewer parameters and higher accuracy with simple structure. [14][15][16] As revealed in the relevant literatures, battery ECMs can be divided into Rint models, 17 RC (Resistance Capacitor) models, 18,19 and PNGV (The Partnership for a New Generation of Vehicles) models, 20 etc. Among them, RC network model has been studied a lot.…”

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confidence: 99%

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

Pang

Geng

Liu

et al. 2022

J. Electrochem. Soc.

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Accurate estimation of battery state of charge (SOC) plays a crucial role for facilitating intelligent battery management system development. Due to the high nonlinear relationship between the battery open-circuit voltage (OCV) and SOC, and the shortcomings of traditional polynomial fitting approach, it is an even more challenging task for predicting battery SOC. To address these challenges, we present a composite SOC estimation approach for lithium-ion batteries using back-propagation neural network (BPNN) and extended Kalman particle filter (EKPF). First, a second-order resistance capacitance model is established to make parameters identification of a lithium-ion battery cell using recursive least squares algorithm with forgetting factors (FFRLS) approach. Then, BPNN is used to fit the desired OCV-SOC relationship with relatively high precision. Next, by incorporating the extended Kalman filter (EKF) into the particle filter (PF), an expected EKPF approach is presented to realize the SOC estimation. Finally, the performances of SOC estimation using different methods, namely the PF, EKF and the EKPF are compared and analyzed under constant current discharge and urban dynamometer driving schedule working conditions. The experimental results show that the proposed method has higher accuracy and robustness compared to the other two SOC estimation methods.

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Improved covariance matching—electrical equivalent modeling for accurate internal state characterization of packing lithium‐ion batteries

Cited by 6 publications

References 56 publications

Synthesis and electrochemical properties of nickel sulfide/carbon composite as anode material for lithium‐ion and sodium‐ion batteries

Synthesis and electrochemical properties of nickel sulfide/carbon composite as anode material for lithium‐ion and sodium‐ion batteries

Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

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