Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer

Abstract

Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium‐ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium‐ion battery. Here, a first‐order resistor‐capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test.

State-of-charge estimation method for lithium-ion batteries based on competitive SIR model

“…At present, the definition of SOC is not unified (Sakile and Sinha, 2021). Only a few simple definitions are introduced below, and the following definitions are given from different perspectives.…”

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

“…Currently, the SOC estimation methods for lithium batteries are primarily categorized into direct and indirect methods, as illustrated in Figure 1. Traditional SOC estimation methods include the ampere-hour integration method [2], open-circuit voltage method [3], equivalent circuit model method [4], adaptive filtering methods (including Kalman filtering [5] and extended Kalman filtering [6][7][8]), and nonlinear observer methods (including the sliding mode observer [9,10], proportionalintegral observer [11], and nonlinear observer [12]). The advantages and disadvantages of the traditional approach are summarized in Table 1.…”

An Initial Value Problem for State of Charge Estimation of Lithium‐Ion Batteries with an Adaptive Fractional‐Order Unscented Particle Filter

“…The state of charge (SOC) estimation of LIBs has turned into a crucial topic for power battery applications to prevent overcharge and over-discharge for the use of LIBs. [3][4][5][6] Methods that are not based on the model of LIBs, such as the ampere-time integration method [7] and the open-circuit voltage method, [8,9] generally have the disadvantage of low accuracy. Additionally, SOC DOI: 10.1002/adts.202300302 estimation is being done using a growing amount of data-driven methodologies.…”