A Reduced-Order Electrochemical Model for All-Solid-State Batteries

Deng, Zhongwei; Hu, Xiaosong; Lin, Xianke; Xu, Ling; Li, Jiacheng; Guo, Wenchao

doi:10.1109/tte.2020.3026962

Cited by 58 publications

(30 citation statements)

References 31 publications

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“…Note that the diffusion coefficients in the electrolyte are considered independent of the electrolyte concentration c e since the variation of electrolyte concentration is not significant, and within a narrow range of concentration the diffusion coefficients can be considered to be constant. Furthermore, r is a charge carrier generation term, considering ignorable in existing works [19], [20]. The boundary conditions and the initial condition of (1a) are given by…”

Section: B Diffusion In the Solid Electrolytementioning

confidence: 99%

“…However, this method shall create a high system order and the efficiency of the algorithm has not been studied. In [20], a rigorous PDE model has been reduced using a combination of Padé approximation, polynomial profile approximation, and equalreaction-coefficient assumption, and the computational burden was significantly reduced with high fidelity amenable for online parameter estimation and state monitoring [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Wik

Xie

et al. 2022

IEEE Trans. Transp. Electrific.

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Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solidstate batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A controloriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs.

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Section: B Diffusion In the Solid Electrolytementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Wik

Xie

et al. 2022

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

show abstract

“…It is simple and can capture the primary degradation modes of LiBs. 8,9 The main degradation modes of LiBs include the loss of lithium inventory, the loss of active material, and increased internal impedance. 10,11 2.…”

Section: Introductionmentioning

confidence: 99%

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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“…With the large-scale popularization of new energy electric vehicles (EVs), lithium-ion batteries (LIBs) have a longer cycle life, no memory effect in the process of charging and discharging, and no pollution to the environment in the process of production and recycling [1][2][3][4] are widely used. As an important part of EVs, battery management system (BMS) can not only provide the driver with the accurate working state of battery, but also provide the driver with the dynamic control of the energy storage system.…”

Section: Introductionmentioning

confidence: 99%

A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

Min

Wei

et al. 2020

IEEE Access

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Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation，this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model. INDEX TERMS Lithium-ion batteries(LIBs); electric vehicles(EVs); state-of-charge (SOC); sliding window method; least-squares-support-vector-machine (LSSVM); grey wolf optimizer(GWO)

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A Reduced-Order Electrochemical Model for All-Solid-State Batteries

Cited by 58 publications

References 31 publications

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

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

A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

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