Optimal Lithium-Ion Battery Charging Using Hildreth-based Constrained Model Predictive Control

Abstract

Lithium-ion batteries (LiBs) have become the pri-mary energy storage technology in electric vehicles, portable elec-tronic devices, and other battery-driven systems. The charging strategy used significantly influences LiB’s performance, safety, and degradation characteristics. The constant current constant voltage (CCCV) charging profile is used in the majority of these applications as a standard charging procedure. However, this approach does not account for the battery’s State of Health (SoH), which is critical for longevity, and lacks adaptability under varying operating conditions. Model Predictive Control (MPC) provides a systematic framework for overcoming these limitations and optimally charging the battery while satisfying its operating constraints. However, implementing MPC on real-time embedded platforms is limited by the computational burden of solving a Quadratic Programming Problem (QPP) in real time. To address this issue, this paper proposes an optimal LiB charging procedure based on constrained MPC, with the Hildreth algorithm as the solver. Owing to its simpler iterative structure and reliable real-time implementation, the Hildreth algorithm is well-suited for embedded applications. The Hildreth-based solver shows improved computational performance over conventional Interior Point Method (IPM) and Active Set (AS) solvers, achieving approximately 98% and 69% reduction in computation time, respectively. Consequently, the overall MPC runtime is reduced by approximately 93% and 40%, respectively, with a lower average iteration count.