An Algorithmic Safety VEST For Li-ion Batteries During Fast Charging
Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates, which can be driven by lithium plating, increased solid electrolyte interphase growth due to elevated temperatures, and particle cracking due to mechanical stress. Lithium plating depends on the overpotential of the negative electrode, and mechanical stress depends on the concentration gradient, both of which cannot be measured directly. Techniques based on physics-based models of the battery and optimal control algorithms have been developed to this end. While these methods show promise in reducing degradation, their optimization algorithms' complexity can limit their implementation. In this paper, we present a method based on the constant current constant voltage (CC-CV) charging scheme, called CC-CV$\eta \sigma$T (VEST). The new approach is simpler to implement and can be used with any model to impose varying levels of constraints on variables pertinent to degradation, such as plating potential and mechanical stress. We demonstrate the new CC-CV$\eta \sigma$T charging using an electrochemical model with mechanical and thermal effects included. Furthermore, we discuss how uncertainties can be accounted for by considering safety margins for the plating and stress constraints.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy
Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Ele…
Explainable ModelsAdaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols
Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted…
Reinforcement Learning (RL)Safe Reinforcement LearningModel-free fast charging of lithium-ion batteries by online gradient descent
A data-driven solution is provided for the fast-charging problem of lithium-ion batteries with multiple safety and aging constraints. The proposed method optimizes the charging current based on the observed history of me…
Robust Model Predictive Control of Fast Lithium-ion Battery Pretreatment for Safe Recycling
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 …
Model Predictive ControlSafe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitable cost functions. To address these chal…
Bayesian OptimizationModel Predictive Control