Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network

Abstract

Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation.

Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations

“…Understanding the capacity deterioration and battery internal resistance makes it simple to determine the SOH. It can be defined as the remaining maximum amount of capacity after the charge–discharge cycle (Lin et al, 2022). Three approaches have been developed for predicting the battery SOH, represented by model-free, model-based, and data-driven methods (Lipu et al, 2018).…”

State of health estimation for lithium battery based on multi-source transfer learning

“…Machine learning methods have been widely used to estimate SOH during battery degradation [4]. Guo et al [5], Su et al [6], Lin et al [7], Chen et al [8], and Liu et al [9] respectively studied the performance of SVM, GPR, BPNN, ELM, and CNN-GRU models in battery SOH estimation. The model can fit the relationship between different aging characteristics and SOH and can obtain highprecision results on SOH estimation.…”

A multicell-to-multicell battery equalizer with more balancing modes based on half-bridge bipolar-resonant LC converter

“…Currently, there are two main types of renewable energy storage components used in EVs: lithium batteries and ultracapacitors (UCs). Lithium batteries are widely used in EVs, hybrid electric vehicles (HEVs), and other energy storage systems (ESSs) due to their high energy density, low self-discharge rate, and lack of memory effect [6]- [8]. However, the use of lithium batteries is facing challenges in electric vehicles and new energy power generation due to their inability to meet the high voltage and capacity requirements of these applications on a single battery.…”