SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
High-Order Collocation Methods for Differential Equations with Random Inputs
SIAM Journal on Scientific Computing
Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures
SIAM Journal on Scientific Computing
Risk aversion min-period retiming under process variations
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Large-scale battery system modeling and analysis for emerging electric-drive vehicles
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Fair energy resource allocation by minority game algorithm for smart buildings
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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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. Based on an ESS modeling solution that considers major run-time and long-term battery effects, the proposed framework unifies design-time optimization and run-time control. It conducts statistical optimization for ESS cost and lifetime, which jointly considers the variances of ESS due to manufacture tolerance and heterogeneous driver-specific run-time use. It 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 can effectively explore the statistical design space, and produce cost-efficient ESS solutions with statistical system lifetime guarantee.