ScholarMate
客服热线:400-1616-289

A Balancing Current Ratio Based State-of-Health Estimation Solution for Lithium-Ion Battery Pack

Tang, Xiaopeng; Gao, Furong; Liu, Kailong*; Liu, Qi; Foley, Aoife M.
Science Citation Index Expanded
5

摘要

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved.

关键词

Batteries Estimation Computational modeling Sensors Battery charge measurement Aging Current measurement Balancing current ratio (BCR) electric vehicle lithium-ion battery pack state-of-health (SoH) estimation