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This is the first study that presents a supply chain (SC) resilience measure with the ripple effect considerations including both disruption and recovery stages. SCs have become more prone to disruptions due to their complexity and strategic outsourcing. While development of resilient SC designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. More adversely, resilience assessment in multi-stage SCs is particularly challenged by consideration of disruption propagation and its associated impact known as the ripple effect. We theorize a new measure to quantify the resilience of the original equipment manufacturer (OEM) with a multi-stage assessment of suppliers’ proneness to disruptions and the SC exposure to the ripple effect. We examine and test the developed notion of SC resilience as a function of supplier vulnerability and recoverability using a Bayesian network and considering disruption propagation using a real-life case-study in car manufacturing. The findings suggest that our model can be of value for OEMs to identify the resilience level of their most important suppliers based on forming a quadrant plot in terms of supplier importance and resilience. Our approach can be used by managers to identify the disruption profiles in the supply base and associated SC performance degradation due to the ripple effect. Our method explicitly allows to uncover latent, high-risk suppliers to develop recommendations to control the ripple effect. Utilizing the outcomes of this research can support the design of resilient supply networks with a large number of suppliers: critical suppliers with low resilience can be identified and developed.

Auxiliary power demand in battery electric vehicles continues to increase as manufacturers transition toward multi-low-voltage architectures that combine 48 V and 12 V buses to improve load distribution flexibility and overall system efficiency. This paper evaluates several auxiliary power module (APM) architectures in terms of scalability, efficiency, complexity, size, and cost for supplying two low-voltage buses (e.g., 48 V and 12 V) from the high-voltage battery. Based on this assessment, a cascaded APM configuration is adopted, consisting of an isolated dual active bridge (DAB) converter followed by a non-isolated synchronous buck converter. A multi-objective optimization framework based on the NSGA-II algorithm is developed for the DAB stage to maximize efficiency and power density while minimizing cost. The optimized 13 kW DAB stage achieves a peak efficiency of 95% and a power density of 4.1 kW/L. For the 48 V/12 V buck stage, a 2 kW commercial GaN-based converter with a mass of 0.5 kg is used as the reference design, achieving a peak efficiency of 96.5%. Dedicated PI controllers are designed for both the DAB and buck stages using their respective small-signal models to ensure tight regulation of the two LV buses. The overall system stability is verified through impedance-based analysis. Experimental validation using a DAB prototype integrated with a multi-phase buck converter confirms the accuracy of the DAB loss modeling used in the design optimization framework as well as the control design implemented for the cascaded converters.