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1. Broad-Scale Converter Optimization Utilizing Discrete Time State-Space Modeling

   https://doi.org/10.1109/DMC55175.2022.9906473  

   Baxter, Jared A.; Costinett, Daniel J. (September 2022, IEEE Design Methodologies Conference (DMC))

   Schematic-level optimization and steady-state loss modeling play a vital role in the design of advanced power converters. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques. In this work, we investigate methods for the incorporation of rapid gradient-based optimization techniques leveraging discrete time state-space modeling and showcase the utility of the approach for use in the converter design process. 

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2. Power Converter and Discrete Device Optimization Utilizing Discrete Time State-Space Modeling

   https://doi.org/10.1109/COMPEL52896.2023.10221030  

   Baxter, Jared A; Costinett, Daniel J (June 2023, 2023 IEEE 24th Workshop on Control and Modeling for Power Electronics (COMPEL))

   Broad-scale modeling and optimization play a vital role in the design of advanced power converters. Optimization is normally implemented via brute force iterations of design variables or utilizing metaheuristic techniques which are time consuming for a wide range of potential topologies, device implementations, and operating points. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques across multiple converter parameters. In this work, we investigate methods to incorporate rapid gradient-based optimization techniques to leverage discrete time state-space modeling and showcase the approach in the power converter design process. The method is validated on a 48-to-1V converter designed using the proposed techniques. 

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3. Steady-State Convergence of Discrete Time State-Space Modeling with State-Dependent Switching

   https://doi.org/10.1109/COMPEL49091.2020.9265734  

   Baxter, Jared A.; Costinett, Daniel J. (November 2020, 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL))

   null (Ed.)

   Steady-state modeling plays an important role in the design of advanced power converters. Typically, steady-state modeling is completed by time-stepping simulators, which may be slow to converge to steady-state, or by dedicated analysis, which is time-consuming to develop across multiple topologies. Discrete time state-space modeling is a uniform approach to rapidly simulate arbitrary power converter designs. However, the approach requires modification to capture state-dependent switching, such as diode switching or current programmed modulation. This work provides a framework to identify and correct state-dependent switching within discrete time state-space modeling and shows the utility of the proposed method within the power converter design process. 

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4. Natural Balancing of Flying Capacitor Multilevel Converters at Nominal Conversion Ratios

   https://doi.org/10.1109/COMPEL.2019.8769724  

   Xia, Ziyu; Dobbins, Benjamin L.; Stauth, Jason T. (June 2019, 2019 20th Workshop on Control and Modeling for Power Electronics (COMPEL))

   This work explores the mechanisms and limitations of natural voltage balancing in flying capacitor multilevel (FCML) DC-DC converters. A simple discrete-time state space model is used to explore the fundamental conditions that will lead to (or prevent) natural balance of flying capacitor voltages, along with the balancing dynamics. The treatment is used to highlight straightforward ways to alleviate problems with natural imbalance by adjusting the switching scheme. The model is compared against circuit simulations and the proposed switching scheme is verified in a hardware prototype. 

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5. Power Converter Circuit Design Automation Using Parallel Monte Carlo Tree Search

   https://doi.org/10.1145/3549538  

   Fan, Shaoze; Zhang, Shun; Liu, Jianbo; Cao, Ningyuan; Guo, Xiaoxiao; Li, Jing; Zhang, Xin (March 2023, ACM Transactions on Design Automation of Electronic Systems)

   The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient—the sequential version can generate topologies with the same quality while being up to 67% faster. The parallelization schemes can further achieve high speedups compared to the sequential version. 

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