As enablers of electric vehicles, lithium-ion batteries are drawing much attention for their high energy density and low self-discharging rate. However, “range anxiety” has remained a significant hindrance to its further development. Of the many design objectives, minimizing the charging time and maximizing the cycle life are conflicting design objectives. 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. However, the battery design and the control are usually coupled that integration of the two discipline, or control co-design, may offer improved performances as compared with traditional sequential optimization approaches. In an previous study, we have shown that efficient control co-design is achievable for Lithium-ion batteries through surrogate modeling. In this work, a reliability-based design optimization framework is integrated to guarantee the performances under parametric uncertainties. The challenges, such as simultaneous model update for the dynamic system and excessive computation burden due to optimal control and reliability assessment, are resolved through coupling the first principle model and the empirical models by an adaptive surrogate modeling process. Such a combination captures the multi-scale nature of the battery and allows efficient numerical analysis for the reliability-based co-design (RBCD) problem. A nested co-design approach and a double-loop reliability assessment method were implemented. The results show that the algorithm can shorten the charging time while satisfying the probability constraint on the cycle-life performances under parametric uncertainties.