| description abstract | As enablers of electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharge. However, “range anxiety” has remained a significant hindrance to its further development. As an alternative to increasing capacity, fast charging seems a reasonable solution. However, challenges remain due to the conflict between high charging rate and excessive capacity loss. In the past, enormous efforts have been carried out to resolve the dispute between high charging rates and large capacity losses by either improving the battery design or optimizing the charging/discharging protocols. In contrast, this study proposes a novel control co-design framework with adaptive surrogate modeling to address the challenges and to generate the systematic optimal battery design and the corresponding charging protocol simultaneously. The proposed method is ideal for lithium-ion battery systems to offer the improved performances as compared with traditional sequential optimization approaches due to the integration of strong coupling effects between electrode design and control optimization. The integrated adaptive surrogate modeling technique allows model reduction for efficient optimal control and simulation solutions. Meanwhile, it preserves an accurate mapping from the first-principle model to the reduced-order model. A hybrid model like this captures the multiscale nature of the cell, that is, micro-scale parameters affect the macro-scale behavior. It reduces the computational cost significantly. The battery co-design problem is formulated as a nested problem, where the inner-loop solves an open-loop optimal control problem and the outer-loop optimizes the plant design variables. The results show that system-level optimal design can be obtained for minimized charging time at various levels of health requirement. | |
