Aging-Aware Battery Control via Convex Optimization

TL;DR

This paper presents a convex optimization-based model predictive control approach for battery management that balances short-term performance objectives with long-term aging considerations, optimizing trade-offs through simulations.

Contribution

It introduces a convex optimization framework incorporating aging dynamics for battery control, enabling effective trade-off management between performance and degradation.

Findings

Quantifies trade-offs between economic benefits and battery aging.

Demonstrates effectiveness of MPC in balancing objectives.

Provides simulation results for load smoothing and arbitrage.

Abstract

We consider the task of controlling a battery while balancing two competing objectives that evolve over different time scales. The short-term objective, such as arbitrage or load smoothing, improves with more battery cycling, while the long-term objective is to maximize battery lifetime, which discourages cycling. Using a semi-empirical aging model, we formulate this problem as a convex optimization problem. We use model predictive control (MPC) with a convex approximation of aging dynamics to optimally manage the trade-off between performance and degradation. Through simulations, we quantify this trade-off in both economic and smoothing applications.