Optimizing Parameter Estimation for Electrochemical Battery Model: A Comparative Analysis of Operating Profiles on Computational Efficiency and Accuracy

TL;DR

<3-5 sentence high-level summary> This work tackles the challenge of parameter estimation in electrochemical battery models by systematically evaluating how different operating profiles influence estimation accuracy and computational cost for NMC batteries. It uses a Single Particle Model with parameter grouping to reduce dimensionality, generates 31 composite profile configurations from five base tests, and assesses 961 cross-validations via PSO-based parameter identification. Key findings show that using all profiles minimizes voltage RMSE, while certain subsets (e.g., C/5, C/2, P, DST) balance accuracy and efficiency, with DST data generally improving parameter identifiability. The results provide practical guidance for selecting current profiles to tailor parameter-estimation workflows for SOC and SOH tasks in real-world battery management contexts.

Abstract

Parameter estimation in electrochemical models remains a significant challenge in their application. This study investigates the impact of different operating profiles on electrochemical model parameter estimation to identify the optimal conditions. In particular, the present study is focused on Nickel Manganese Cobalt Oxide(NMC) lithium-ion batteries. Based on five fundamental current profiles (C/5, C/2, 1C, Pulse, DST), 31 combinations of conditions were generated and used for parameter estimation and validation, resulting in 961 evaluation outcomes. The Particle Swarm Optimization is employed for parameter identification in electrochemical models, specifically using the Single Particle Model (SPM). The analysis considered three dimensions: model voltage output error, parameter estimation error, and time cost. Results show that using all five profiles (C/5, C/2, 1C, Pulse, DST) minimizes voltage output error, while {C/5, C/2, Pulse, DST} minimizes parameter estimation error. The shortest time cost is achieved with {1C}. When considering both model voltage output and parameter errors, {C/5, C/2, 1C, DST} is optimal. For minimizing model voltage output error and time cost, {C/2, 1C} is best, while {1C} is ideal for parameter error and time cost. The comprehensive optimal condition is {C/5, C/2, 1C, DST}. These findings provide guidance for selecting current conditions tailored to specific needs.

Figures (6)

• Figure 1: Schematic diagram of the research framework.
• Figure 2: Schematic diagram of the SPM.
• Figure 3: Current profiles of the five battery testing protocols considered in this study: (a) constant current discharge at C/5 rate, (b) constant current discharge at C/2 rate, (c) constant current discharge at 1C rate, (d) pulse charging and discharging profile, and (e) DST profile. All current values are shown as functions of time.
• Figure 4: RMSE matrix obtained for each considered Case and Scenario. The reported values are $10^2\times$RMSE.
• Figure 5: Scenario 1 RMSE vs. Total Time by Cases(parameter sets).