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Fundamental Techniques for Optimal Control of Reconfigurable Battery Systems: System Modeling and Feasible Search Space Construction

Changyou Geng, Dezhi Ren, Enkai Mao, Changfu Zou, Mario Vašak, Xinyi Zheng, Weiji Han

TL;DR

This paper tackles two core challenges in optimizing reconfigurable battery systems (RBSs): (1) how to model dynamically reconfigurable configurations without frequent redesign, and (2) how to efficiently search the large configuration space for optimal control. It introduces a complete-system-configuration (CSC) modeling framework that unifies all possible RBS configurations into a single, stable state-space model, with switch states influencing network resistances and the system dynamics via a fixed mesh structure; this enables consistent evaluation of the objective and outputs across configurations, and a power-based computation of the input current. To address the curse of dimensionality, the authors develop a feasible-search-space construction that maps configurations to cell-module P&CPs, enumerates only voltage-feasible configurations (using a DP-based algorithm for parallel-then-series layouts), and converts these to feasible switch-state vectors (SSVs); this substantial reduction in search space is shown to dramatically improve optimization efficiency and safety, both in simulation and experiment. A case study demonstrates that optimization within the feasible space converges and yields balanced SoC across cells, while the complete space struggles to improve performance, underscoring the practical value of the proposed approach for scalable RBS optimization.

Abstract

Reconfigurable battery systems (RBSs) are emerging as a promising solution to improving fault tolerance, charge and thermal balance, energy delivery, etc. To optimize these performance metrics of RBSs, high-dimensional nonlinear integer programming problems need to be formulated and solved. To accomplish this, it is necessary to address several critical challenges stemming from nonlinear battery characteristics, discrete switch states, dynamic system configurations, as well as the curse of dimensionality inherent in large-scale RBSs. Thus, we propose a unified modeling framework to accommodate various possible configurations of an RBS and even to cover different RBS designs and their hybrid combinations, enabling the problem formulation for the RBS optimal control and facilitating the RBS topology design.Further, to solve the formulated RBS optimal control problems, the search space is narrowed to encompass only the feasible solutions, thereby ensuring safe battery connections while substantially curtailing search efforts. These proposed techniques, focusing on unifying the system modeling and narrowing the search space, lay a solid foundation for effectively formulating and efficiently solving RBS optimal control problems. The accuracy and effectiveness of the proposed techniques are demonstrated by both simulation and experimental tests.

Fundamental Techniques for Optimal Control of Reconfigurable Battery Systems: System Modeling and Feasible Search Space Construction

TL;DR

This paper tackles two core challenges in optimizing reconfigurable battery systems (RBSs): (1) how to model dynamically reconfigurable configurations without frequent redesign, and (2) how to efficiently search the large configuration space for optimal control. It introduces a complete-system-configuration (CSC) modeling framework that unifies all possible RBS configurations into a single, stable state-space model, with switch states influencing network resistances and the system dynamics via a fixed mesh structure; this enables consistent evaluation of the objective and outputs across configurations, and a power-based computation of the input current. To address the curse of dimensionality, the authors develop a feasible-search-space construction that maps configurations to cell-module P&CPs, enumerates only voltage-feasible configurations (using a DP-based algorithm for parallel-then-series layouts), and converts these to feasible switch-state vectors (SSVs); this substantial reduction in search space is shown to dramatically improve optimization efficiency and safety, both in simulation and experiment. A case study demonstrates that optimization within the feasible space converges and yields balanced SoC across cells, while the complete space struggles to improve performance, underscoring the practical value of the proposed approach for scalable RBS optimization.

Abstract

Reconfigurable battery systems (RBSs) are emerging as a promising solution to improving fault tolerance, charge and thermal balance, energy delivery, etc. To optimize these performance metrics of RBSs, high-dimensional nonlinear integer programming problems need to be formulated and solved. To accomplish this, it is necessary to address several critical challenges stemming from nonlinear battery characteristics, discrete switch states, dynamic system configurations, as well as the curse of dimensionality inherent in large-scale RBSs. Thus, we propose a unified modeling framework to accommodate various possible configurations of an RBS and even to cover different RBS designs and their hybrid combinations, enabling the problem formulation for the RBS optimal control and facilitating the RBS topology design.Further, to solve the formulated RBS optimal control problems, the search space is narrowed to encompass only the feasible solutions, thereby ensuring safe battery connections while substantially curtailing search efforts. These proposed techniques, focusing on unifying the system modeling and narrowing the search space, lay a solid foundation for effectively formulating and efficiently solving RBS optimal control problems. The accuracy and effectiveness of the proposed techniques are demonstrated by both simulation and experimental tests.
Paper Structure (12 sections, 26 equations, 11 figures, 4 tables)

This paper contains 12 sections, 26 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Formulation of a general optimal control problem for RBSs
  3. Unified modeling framework for RBSs
  4. Complete system configuration for unifying the RBS modeling
  5. Formulation of an RBS model based on its CSC
  6. Model validation by simulation and experimental tests
  7. Construction of feasible solution space
  8. Mapping a system configuration with a system SSV
  9. Identifying all feasible system configurations
  10. Quantifying the narrowing effect of the proposed feasible search space
  11. Case study for comparing the searching effects within the complete and feasible search spaces
  12. Conclusions

Figures (11)

  • Figure 1: Some structure designs for RBSs proposed in recent literature.
  • Figure 2: Structural diagram of the proposed framework.
  • Figure 3: Diagram for RBS design (a) in Fig. \ref{['fig:RBS_designs']}.
  • Figure 4: Illustration of the testbed for RBS experiments.
  • Figure 5: Comparison of the battery system's terminal voltage and input current obtained from model-based simulation with experimental measurement.
  • ...and 6 more figures