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Energy consumption and the associated environmental impact are a pressing challenge faced by the transportation sector. Emerging electric-drive vehicles have shown promises for substantial reductions in petroleum use and vehicle emissions. Their success, however, has been hindered by the limitations of energy storage technologies. Existing in-vehicle lithium-ion battery systems are bulky, expensive, and unreliable. Energy storage system (ESS) design and optimization is essential for emerging transportation electrification. This paper presents an integrated ESS modeling, design, and optimization framework targeting emerging electric-drive vehicles. A large-scale ESS modeling solution is first presented, which considers major runtime and long-term battery effects, and uses fast frequency-domain analysis techniques for efficient and accurate characterization of large-scale ESS. The proposed design framework unifies design-time optimization and runtime control. This conducts statistical optimization for ESS cost and lifetime, which jointly considers the variances of ESS due to manufacture tolerance and heterogeneous driver-specific runtime usage. This optimizes ESS design by incorporating complementary energy storage technologies, e.g., lithium-ion batteries and ultracapacitors. Using physical measurements of battery manufacture variation and real-world user driving profiles, our experimental study has demonstrated that the proposed framework effectively explores the statistical design space and produces cost-efficient ESS solutions with statistical system lifetime guarantees.