Estimation of battery parameters and state of charge using continuous time domain identification method

Original citation:

Zhang, Cheng, Dinh, Quang Truong, Allafi, Walid, Marco, James and Yoon, Jong Il (2017)

Estimation of battery parameters and state of charge using continuous time domain

identification method. In: 21st International Conference on Mechatronics Technology, Ho

Chi Minh City, Vietnam, 20-23 Oct 2017 pp. 7-12.

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October 20 23, 2017 in Ho Chi Minh City, Vietnam

Online Estimation of Battery Parameters and State of Charge Using

Continuous Time Domain Identification Method

Cheng Zhang

_{, Quang Truong Dinh}

_{, Walid Allafi}

_{, James Marco}

_{, and Jong Il Yoon}

1 _{WM G, University of Warwick, Coventry, UK}

2_{Korea Construction Equipment Technology Institute, Gunsan 573-540 Sandan-ro 36, Korea} *_{Corresponding: [email protected]}

Abstract Battery equivalent circuit models (ECMs) are widely used in battery management systems (BMSs), such as in electric vehicles (EVs). The battery terminal voltage-current (VI) dynamics and the ECM parameters depend on the operating conditions, such as the state of charge (SOC) and temperature. Online parameter estimation can improve not only the modelling accuracy, but also the performance of model -based S OC estimation, which plays a key role in BMS . This paper presents a continuous-time (CT) domain algorithm for online co-estimation of the battery ECM parameters and SOC, using the linear integral filter (LIF) method. Compared with the conventional discrete time domain least square algorithm, the proposed CT LIF technique has superior performance at capturing the battery slow dynamics, which can further improve the SOC estimation accuracy. Experimental data are collected using a Li ion battery (LIB), and the results are analyzed to verify the efficacy of the proposed algorithm.

Keywords - equivalent circuit model, recu rsive parameter and SOC estimation, linear integral filter.

1

INTRODUCTION

Equivalent circuit models (ECMs) have been widely used in battery management systems of battery energy storage systems due to the ease of parametrization and implementation and acceptable modelling accuracy. An ECM uses a combination of electric components, including resistors and capacitors, to represent the battery terminal voltage-current (VI) behavior. Figure 1 presents a typical ECM, where v i, are the battery terminal voltage and current, respectively. The battery open circuit voltage (OCV) depends on the battery state of charge (SOC). The R₀ stands for the battery series resistance, and the RC networks are used to capture the dynamics voltage relaxation effects.

One key issue with ECM is that the model parameters depend on the operating conditions, for instance, the ambient temperature, SOC and state of health (SOH). For example, a Li ion battery (LIB) cell shows nearly doubled internal resistance as the temperature drops from 25 C to 0 C [1]. The parameter dependency can be described using a look-up table trained offline. However, this offline battery characterization is time-consuming. Moreover, the tabulated ECM parameters will lose efficacy as the battery degrades. Therefore, online recursive parameter identification schemes should be used to

keep track of the evolving ECM parameters to improve modelling accuracy.

SOC estimation is another key issue in BMSs [2]. Existing SOC estimation algorithms based on onboard VI measurements include Ampere hour (Ah) counting and OCV-based algorithms [1, 2]. ECM-based state observer techniques, such as Kalman filter (KF), unscented KF (UKF), H-infinite filter, etc. [3-6], have been widely used for real-time battery SOC estimation by combining the advantages of the Ah and OCV-based methods. The SOC estimation performance depends on the model accuracy, and therefore online estimation of the ECM parameters can improve not only the modelling accuracy, but also the SOC estimation performance [7].

October 20 – 23, 2017 in Ho Chi Minh City, Vietnam

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October 20 – 23, 2017 in Ho Chi Minh City, Vietnam

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October 20 – 23, 2017 in Ho Chi Minh City, Vietnam

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