A control-oriented electrochemical model for lithium-ion battery, Part I: Lumped-parameter reduced-order model with constant phase element

Chu, Zhengyu; Plett, Gregory L.; Trimboli, M. Scott; Ouyang, Minggao

doi:10.1016/j.est.2019.100828

Cited by 47 publications

(41 citation statements)

References 33 publications

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“…The predicted voltage curves are very close to the measured voltages, with a root mean squared error (RMSE) of 0.67 mV. The RMSE is low enough to approach the limit of the control-oriented electrochemical model, 44,45 thereby proving the accuracy of the parameters identified by the BPNN.…”

Section: Capacity Loss Of the Libmentioning

confidence: 61%

Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

et al. 2021

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The aging of lithium-ion batteries (LiBs) is inevitable during their operation owing to their irreversible side reactions. It is practical to capture only the dominant physicochemical processes with a physics-based model for engineering applications, as the degradation mechanism of LiBs is complex and interconnected. Numerous factors dramatically affect the performance of LiBs; thus, it is necessary to use the real-time operational data to estimate the model parameters online. The combination of the degradation mechanism and real-time data can effectively reduce the complexity of the parameter identification, and offers practicality from the perspective of engineering applications. In this study, a transfer learning method based on a back propagation neural network (BPNN) is introduced as the data-driven parameter identification method for the physics-based fractional-order model (FOM) of the LiBs. Accelerated aging tests are designed for commercial LiBs to reduce the experimental time, and the reference performance tests under different aging states are implemented to capture the degradation modes with increasing cycle numbers. The initial key parameters of the fresh FOM are estimated based on the BPNN with two hidden layers. The model-based transfer learning is proposed to estimate the key parameters of the aging FOM. The parameter identification results at different aging states are validated under various working loads. The good fit with the experimental data indicated that the parameters can be identified accurately using the transfer learning with BPNN. The outstanding results show the effectiveness of the parameter identification method based on transfer learning.

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Section: Capacity Loss Of the Libmentioning

confidence: 61%

Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

et al. 2021

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“…Thus, a minimum set of parameters was obtained, making system identification possible. 44,45 Based on this approach, this study applied the fast charging strategy calibration to actual batteries using reference electrodes. Strategy calibration experiments were performed in a temperature chamber at 25 C. As shown in Figure 4, both sides of a battery with a reference electrode were fixed in place using a pair of aluminum splints.…”

Section: Fast Charging Strategy Calibrationmentioning

confidence: 99%

“…This research reformulated the original electrochemical model using lumped parameters to produce a full‐order model with the number of parameters reduced from 36 to 24. Thus, a minimum set of parameters was obtained, making system identification possible 44,45 . Based on this approach, this study applied the fast charging strategy calibration to actual batteries using reference electrodes.…”

Section: Experimental Design and Implementationmentioning

confidence: 99%

Lithium‐plating‐free fast charging of large‐format lithium‐ion batteries with reference electrodes

Liu

Chu

Li³

et al. 2021

Int J Energy Res

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Fast charging without lithium deposition has been one of the most important goals for commercial lithium-ion batteries. This study developed a fast charging strategy for a commercial large-format NCM/graphite lithium-ion battery with a nominal capacity of 120 Ah. Reliable reference electrodes, whose performances were thoroughly investigated with high fidelity, were implanted into the cells to provide anode potential signals during the charging process. The charging current was strictly modulated to maintain both the anode potential and temperature within the safety regions. The cell's full charging capability was exploited and twice the charging speed was realized using the proposed strategy compared with the manufacturer's fast charging strategy. Following the cycle life tests, an incremental capacity analysis and scanning electron microscopy tests were conducted to identify the degradation mechanism under fast charging. The results indicated that after the fast charging, the battery's decay rate was similar to that of a battery subjected to slow charging after 100 cycles, while no plated lithium was detected on the negative electrode. An electric vehicle (EV) could be driven 300 km after charging for 16 minutes without lithium deposition. It can be concluded that the proposed lithium-plating-free fast charging strategy has high battery applicability and manufacturer friendliness, and provides a new perspective on EV fast charging. Highlights • A method for developing a fast charging strategy for large-capacity lithium batteries is proposed. • The cell's full charging capability was exploited and twice the charging speed was realized using the proposed strategy compared with the manufacturer's fast charging strategy. • The battery's decay rate was similar to that of a battery subjected to slow charging after 100 cycles, while no plated lithium was detected on the negative electrode.

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“…White has applied various methods such as orthogonal decomposition [133] , state-space reformulation [134,135] , reducing the system of partial differential equations to ordinary differential equations [135,136] , and applying polynomial representations of the Li ion concentration in the solid phase [137] . In recent years, the popularity of these models [138][139][140][141][142][143][144][145][146][147][148][149] has increased due to their relative speed and accuracy.…”

Section: Plating Modelingmentioning

confidence: 99%

Lithium Plating Detection Methods in Lithium-Ion Batteries

2020

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Lithium-ion batteries provide a low-cost, long cycle-life and high energy density solution to the expediting energy requirement of the automotive industry. There is a growing need of fast charging batteries for commercial application. However, large charging currents may cause Lithium plating which describes the deposition of metallic Lithium at the anode surface. It takes place at conditions of high currents and/or low temperatures because of kinetic limitations. The main reason behind plating is the slow solid-state diffusion of Lithium ions inside the active material. If the anode surface potential falls below 0 V versus Li/Li+, the formation of metallic Lithium is thermodynamically feasible. To avoid or reduce the amount Lithium plating, it is essential to detect its onset during a charging event. Determination of accurate Lithium plating curve is crucial in estimating the boundary conditions for battery operation without compromising life and safety. There are various data analysis methods involved in deriving the Lithium plating curve: anode potential using a three-electrode cell, variation of relaxation voltage after charging (dV/dt), variation of accumulated charge with voltage (dQ/dV) and coulombic efficiency of charge/discharge with %SOC (state of charge) are more commonly employed techniques. In addition to these methods, estimation of its occurrence is typically underpinned by electrochemical models where the negative (anode) electrode potential is expressed by a set of partial differential equations based on the electrochemical and physical properties of the battery components such as the electrodes and electrolyte. The present paper reviews the common test methods and analysis that are currently utilized in Lithium plating determination. Knowledge gaps are identified, and recommendations are made for the future development in the determination and verification of Lithium plating curve in terms of modelling and analysis.

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A control-oriented electrochemical model for lithium-ion battery, Part I: Lumped-parameter reduced-order model with constant phase element

Cited by 47 publications

References 33 publications

Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

Lithium‐plating‐free fast charging of large‐format lithium‐ion batteries with reference electrodes

Lithium Plating Detection Methods in Lithium-Ion Batteries

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