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Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling

Meng Yuan, Adam Burman, Changfu Zou

TL;DR

This paper addresses safe and rapid discharging of lithium-ion batteries during pre-treatment for recycling by introducing a robust model predictive control framework that explicitly enforces core-temperature constraints. It develops a control-oriented electrothermal model combining SoC, electrochemical dynamics, and thermal states, and integrates a Kalman filter for state estimation. The robust MPC uses a nominal linear model with a robust invariant set to ensure constraint satisfaction under modelMismatch, and it is benchmarked against CC-CV, CC-CT, and dynamic programming, showing improved safety and reduced discharge time relative to CC-CT. The approach offers a practical path to safer, faster battery recycling and second-life applications, with potential extensions to more complex multiphysics models and pack-level implementations.

Abstract

The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorporates temperature constraints to prevent overheating and potential hazards. By leveraging a control-oriented equivalent circuit model integrated with thermal dynamics, the MPC algorithm dynamically adjusts the discharging profile to maintain safe operating temperatures. Additionally, the robust controller is designed to account for model mismatches between the nonlinear battery dynamics and the linearized model, ensuring reliable performance under varying conditions. The effectiveness of this approach is demonstrated through simulations comparing the robust MPC method with conventional discharging strategies, including constant current-constant voltage (CC-CV) and constant current-constant temperature (CC-CT) methods. Results indicate that the robust MPC framework significantly reduces discharging time while adhering to safety constraints, offering a promising solution for the recycling and second-life applications of lithium-ion batteries.

Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling

TL;DR

This paper addresses safe and rapid discharging of lithium-ion batteries during pre-treatment for recycling by introducing a robust model predictive control framework that explicitly enforces core-temperature constraints. It develops a control-oriented electrothermal model combining SoC, electrochemical dynamics, and thermal states, and integrates a Kalman filter for state estimation. The robust MPC uses a nominal linear model with a robust invariant set to ensure constraint satisfaction under modelMismatch, and it is benchmarked against CC-CV, CC-CT, and dynamic programming, showing improved safety and reduced discharge time relative to CC-CT. The approach offers a practical path to safer, faster battery recycling and second-life applications, with potential extensions to more complex multiphysics models and pack-level implementations.

Abstract

The proper disposal and repurposing of end-of-life electric vehicle batteries are critical for maximizing their environmental benefits. This study introduces a robust model predictive control (MPC) framework designed to optimize the battery discharging process during pre-treatment, ensuring both efficiency and safety. The proposed method explicitly incorporates temperature constraints to prevent overheating and potential hazards. By leveraging a control-oriented equivalent circuit model integrated with thermal dynamics, the MPC algorithm dynamically adjusts the discharging profile to maintain safe operating temperatures. Additionally, the robust controller is designed to account for model mismatches between the nonlinear battery dynamics and the linearized model, ensuring reliable performance under varying conditions. The effectiveness of this approach is demonstrated through simulations comparing the robust MPC method with conventional discharging strategies, including constant current-constant voltage (CC-CV) and constant current-constant temperature (CC-CT) methods. Results indicate that the robust MPC framework significantly reduces discharging time while adhering to safety constraints, offering a promising solution for the recycling and second-life applications of lithium-ion batteries.

Paper Structure

This paper contains 12 sections, 16 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Modeling
  3. Electrothermal model of battery
  4. State of energy computation
  5. Controller formulation
  6. Conventional approach
  7. CC-CV method
  8. CC-CT method
  9. Dynamic programming
  10. Proposed approach
  11. Results and Discussion
  12. Conclusions

Figures (3)

  • Figure 1: Illustration of electrothermal modeling of a lithium-ion battery zou2017electrothermal.
  • Figure 2: Proposed control structure for battery discharging.
  • Figure 3: Battery discharging performance based on different methods: (a) SoE; (b) terminal voltage; (c) input current; (d) core temperature.

Theorems & Definitions (1)

  • Definition 1