An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

Abstract

Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively. INDEX TERMS Lithium-ion battery, state of charge (SOC), electrochemical model, Nernst equation I. INTRODUCTION With numerous advantages, such as high energy density, low self-discharging rate, no memory effects, and long lifespan, and so on, lithium-ion batteries are generally regarded as a leading energy storage candidate for electric vehicles, renewable energy systems, portable electronic devices, and many other applications [1-3]. In most lithium-ion battery energy storage systems, a battery management system (BMS) is employed to manage the performance and maintain the longevity of battery cells [1, 4, 5]. The functions of a BMS include mainly cell voltage detection and balancing [6, 7], estimation of state of charge (SOC) [8-11] and state of health (SOH) [12-14], and thermal management [15-18]. Defined as the ratio of the residual charge stored in a battery to its maximum available capacity, the SOC is one of the primary and critical parameters for the development of battery management strategies [19]. Accurate SOC estimation plays a vital role in controlling the safe charging and discharging of the battery while guaranteeing its efficiency. However, the SOC cannot be measured directly and accurately by sensors like ordinary physical quantities in practices. Despite the huge research efforts, the battery SOC estimation still poses a significant challenge due to the complexity and nonlinearity of e...

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

“…33 In addition, there are a series of side reactions between the positive and negative plates and the electrolyte, so there are a variety of electrochemical reactions coupled inside the battery. 34 In the LIB application process, the battery material, aging degree, ambient temperature, overcharge and over-discharge, and other factors affect the internal reaction process of LIBs, 35 thereby greatly increasing the difficulty in SOC estimation. 36 The commonly used SOC estimation methods include the following types.…”

A Collaborative Estimation Scheme for Lithium-Ion Battery State of Charge and State of Health Based on Electrochemical Model

“…At this time, the negative electrode lithium ion concentration (c s,n,soc = 0% ) is a fixed value. 43 As the battery ages and decays, the active lithium ions in the battery are continuously consumed, that is, the number of lithium ions that can be deintercalated in the battery is continuously reduced. At this time, during the charging and discharging process of the battery from 0% to 100% SOC or from 100% to 0% SOC, the number of lithium ions obtained by the positive and negative electrodes decreases.…”

An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery

“…Moreover, the model-based methods have been proposed for SOC estimation. The battery models mainly include equivalent circuit model (ECM) [11] and electrochemical model [12], and the former is mostly used. The battery models are usually used combined in conjunction with control theory algorithms, such as Kalman filter (KF, EKF, UKF) [13][14][15], particle filter (PF) [16], and adaptive observers [17].…”