Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

Abstract

This paper introduces a novel application of model predictive control (MPC) to cell-level charging of a lithium-ion battery utilizing an equivalent circuit model of battery dynamics. The approach employs a modified form of the MPC algorithm that caters for direct feed-though signals in order to model near-instantaneous battery ohmic resistance. The implementation utilizes a 2 nd-order equivalent circuit discrete-time state-space model based on actual cell parameters; the control methodology is used to compute a fast charging profile that respects input, output, and state constraints. Results show that MPC is well-suited to the dynamics of the battery control problem and further suggest significant performance improvements might be achieved by extending the result to electrochemical models.

Efficient embedded architectures for fast-charge model predictive controller for battery cell management in electric vehicles

“…The long-term performance (durability) of the Li-ion battery pack is significantly affected by the choice of the charging strategy. For instance, exceeding the current and voltage constraints of the Li-ion battery cell can cause irreversible damage and capacity loss that would degrade the long-term performance and curtail the effective life of the battery pack [3]. Conversely, operating within the current and voltage constraints would enhance the durability and prolong the useful life of the battery pack.…”

“…Model predictive controller (MPC) has been investigated as a viable technique for cell-level monitoring and controlling of the battery packs [3]. MPC is a popular control technique that enables incorporating constraints and generating predictions, while allowing the systems to operate at the thresholds of those constraints.…”

“…MPC is a popular control technique that enables incorporating constraints and generating predictions, while allowing the systems to operate at the thresholds of those constraints. For some time, MPC algorithm has been utilized in the industrial processes, typically in non-resource-constrained environments; however, in recent years, this algorithm is gaining interest in the resource-constrained environments, including cyber-physical systems and hybrid automotive fuel cells [3], to name a few. The effectiveness of the MPC algorithm for cell-level monitor/control depends on the accuracy of the mathematical model of the battery cell.…”

“…From these, ECM models are more popular due to their simplicity. In [3], the authors prove the efficacy of controlling and providing a fast-charge mechanism for Li-ion battery cells by integrating the MPC algorithm with an ECM model. This fast-charge MPC mechanism incorporates various constraints such as maximum current, current delta, cell voltages, and cell state of charge, which keep the Li-ion battery cell within its optimal operating parameters while reducing the charging time.…”

“…This fast-charge MPC mechanism incorporates various constraints such as maximum current, current delta, cell voltages, and cell state of charge, which keep the Li-ion battery cell within its optimal operating parameters while reducing the charging time. Thus far, this fast-charge MPC algorithm has been designed and developed in Matlab and executed on a desktop computer [3]. However, in a real-world scenario, it is imperative to execute this fast-charge MPC algorithm on an embedded platform mounted on an individual cell, in order to utilize this algorithm to monitor and control the individual cells in a battery pack.…”