Work

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

Year: 2021

Type: article

Abstract: 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.

Source: IEEE Transactions on Industrial Electronics

DOI: 10.1109/tie.2021.3108715

Authors: Xiaopeng Tang, Furong Gao, Kailong Liu, Qi Liu, Aoife M. Foley

Institutions: Hong Kong University of Science and Technology, Guangzhou HKUST Fok Ying Tung Research Institute, University of Warwick, City University of Hong Kong, Queen's University Belfast

Language: English

Topic: Battery Modeling and Management

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

Funders: Queen's University Belfast, Queen's University, Science, Technology and Innovation Commission of Shenzhen Municipality, High Value Manufacturing Catapult