Control-Oriented Modeling of All-Solid-State Batteries

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The emerging all-solid-state 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 control-oriented 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 is 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. 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 be used for advanced control design in future intelligent management systems of ASSBs.

– An electrochemical model of ASSBs is established for the purpose of battery
design, management, and control.
– The infinite-dimensional PDE model is systematically simplified to a low-order model with a simple structure and guaranteed observability.
– The isothermal reduced-order model is validated with experimental data from the literature.
– The reduced-order model can be used for the design of model-based state
estimation and optimal control algorithms, such as the Kalman filters and MPC

– Y. Li, T. Wik, C. Xie, Y. Huang, B. Xiong, J. Tang, and C. Zou, “Control-oriented modeling of all-solid-state batteries using physics-based equivalent circuits,” IEEE Trans. Transport. Electrific., 2021, early access, 10.1109/TTE.2021.3131147.
– Y. Li, D. Karunathilake, D. M. Vilathgamuwa, Y. Mishra, T. W. Farrell, S. S. Choi, and C. Zou, “Model order reduction techniques for physics-based lithium-ion battery management: A survey,” IEEE Ind. Electron. Mag., 2021, early access, 10.1109/MIE.2021.3100318.
– Y. Li, D. M. Vilathgamuwa, E. Wikner, Z. Wei, X. Zhang, T. Thiringer, T. Wik, and C. Zou, “Electrochemical model-based fast charging: Physical constraint-triggered PI control,” IEEE Trans. Energy Convers., vol. 36, no. 4, pp. 3208–3220, Dec. 2021, 10.1109/TEC.2021.3065983.
– Y. Li, B. Xiong, D. M. Vilathgamuwa, Z. Wei, C. Xie, and C. Zou, “Constrained ensemble Kalman filter for distributed electrochemical state estimation of lithium-ion batteries,” IEEE Trans. Ind. Informat., vol. 17, no. 1, pp. 240–250, Jan. 2021, 10.1109/TII.2020.2974907.

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