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And State of Health(Soh) Estimation Methods of Li-Ion Batteries

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And State of Health(Soh) Estimation Methods of Li-Ion Batteries AN OVERVIEW OF STATE OF CHARGE(SOC) AND STATE OF HEALTH(SOH) ESTIMATION METHODS OF LI-ION BATTERIES K. SAQLI(a), H. BOUCHAREB(b), M. OUDGHIRI(c), N.K. M’SIRDI(d) (a),(b),(c)Sidi Mohamed Ben Abdellah University - National School of Applied Sciences, Fez, Morocco (d)LIS - Informatics and Systems Laboratory (LIS CNRS 7020), Aix Marseille University, CNRS,13397 Marseille Cedex, France (a)[email protected], (b)[email protected], (c)[email protected] (d) [email protected] ABSTRACT battery pack. An accurate SOC can be achieved using Battery Management System (BMS) is an essential the proper battery model and estimation algorithm. component for lithium-ion battery-based devices. It Different estimation approaches were introduced in provides a variety of functionalities that help improve the literature in the aim of predicting this parameter, in overall lifespan of the battery, including states estimation (Chang, 2013) Wen-Yeau Chang presented a algorithms. An accurate estimation of the battery State classification of the different mathematical methods that Of Health (SOH) and State Of Charge (SOC) is a crucial were used in literature to predict the battery SOC. task that an advanced battery management system should However, this variable only is not enough for proper perform. utilization of the battery, since the battery is subject to This paper aims to outline the most relevant battery different ageing mechanisms, it’s important to track of model types that were used in literature for Electric the battery health. The battery ageing results in an Vehicle (EV) applications. An overview of the increment in internal resistance and a decrease in estimation algorithms that estimate the battery state of capacity, which affects the performance and the ability to charge and state of health are presented and simulations provide the same energy decreases. Therefore, of some methods are also illustrated in order to test their estimation of the battery SOH can be achieved by accuracy. tracking the change of one of these two metrics. This battery state was the subject of research of different authors who used different estimation algorithms with Keywords: Battery management system, State Of different battery models to provide an accurate Health, State Of Charge, Lithium-ion. estimation. (Ungurean, Cârstoiu, & Groza, 2016), presented a detailed review of the most relevant models, 1. INTRODUCTION algorithms and commercial devices that were used in The global need for a clean and renewable energy literature to estimate the battery Remaining Useful Life sources that can replace fossil energy is essential to (RUL) and SOH. create a more sustainable planet. This global shift favours the use of electric vehicles as a safe and clean alternative This paper outlines the most relevant methods that were of fossil fuels in transportation to help reduce air used in literature to estimate the battery SOC and SOH. pollution. Due to their high power and energy density, First, we present the battery model categories that were Li-ion batteries are widely used to power electric cars. used for EV applications. Then an overview of battery However, to ensure a long-life cycle and avoid any risk state of health and state of charge estimation algorithms, of explosion, a Battery Management System (BMS) is in particular, the coulomb counting method, internal needed to boost the efficiency and guarantee a safe usage resistance method, voltage-based method, Kalman of the battery. filtering based methods, sliding mode observer, fuzzy logic and least squares-based method are presented. an A smart battery management system uses the required equivalent circuit model with 2 RC networks is used to data to estimate the battery states, whether it's the battery test the accuracy of some methods to determine the state of charge (SOC), state of health (SOH), or any other battery SOC and internal resistance. battery state that can help improve the performance. The ability to predict the instantaneous battery state and 2. BATTERY MODELING FOR EVS conditions is a crucial task a BMS should perform with Building a proper battery model that suits better the accuracy. The SOC indicates the battery available target application is a crucial task during BMS design; it capacity to help avoid overcharging/discharging the helps capture the battery electric and thermal behaviors under different operating conditions, to ensure safe and each electrode of the battery by a single spherical particle fast charging for optimal utilization and secure ignoring thermal effects, approximating spatial and time discharging of the battery (Kailong, Kang, Qiao, & characteristics at the separator region to 0 and assuming Cheng, 2019). all unknown states to be scalar and uniform Figure 1. SPM are simple and they can be adopted for real time Literature has presented numerous battery models with applications, however, they lack accuracy at high C-rate. different complexity and fidelity scales. For EV applications the battery model needs to be simple, To overcome this limitation an extended version of these computationally efficient and suitable for high discharge model that incorporates the electrolyte dynamics has rates. Therefore, two main groups of EV battery models been developed and they were proved to maintain a high were presented: Equivalent Circuit Models (ECM) and accuracy prediction even at high C-rates conditions Reduced Order Models (ROM) (A., J., K., & Longo S. (Moura, Argomedo, Klein, & Krstic, 2016). and Wild, 2016). 2.1. Equivalent Circuit battery Model Equivalent circuit models on the other hand are widely used when developing a BMS for EV application thanks to their speed, simplicity and reasonable accuracy (Rincon-Mora, 2006). The model uses a combination of resistors, capacitors and voltage sources, to describe the battery behavior under load. Starting by the most basic model that represents the battery as an ideal voltage source mounted on series with internal resistance to describe the voltage polarization, researchers have include different electrical components, in the interest of building improved versions which take into account the dependence of the battery cell on state of charge, temperature, and other characteristics (Kempera, Li, & Kum, 2015). Figure 1: P2D and single particle battery model Figure 2 illustrates a typical ECM that was widely used in literature for battery states estimation. The resistor- capacitor networks are used to describe the battery Another version that aims to reduce the computational charge transfer or diffusion processes (Kailong, Kang, burden of the P2D model was presented in the literature Qiao, & Cheng, 2019). as the Simplified P2D model (SP2D), it describes the dynamic concentration profiles derived from the P2D model to help improve the accuracy of the BMS (G., X., & M., 2018). Once the battery model has been decided, it can be used as an input for the states estimation algorithms whether it is the SOC, SOH or any other state. 3. BATTERY SOC AND SOH ESTIMATION 3.1. Coulomb Counting Figure 2 : Second order equivalent circuit model Coulomb counting, also known as Ampere-hour method is one of the most common techniques that were used to estimate the battery states and especially the battery state 2.2. Reduced-Order battery Model of charge. As the name suggests, this method calculates Reduced-order models are simplified version of the accumulated current that flows in or out during the electrochemical battery model (A., B., & M., 2016) . The charge-discharge process to determine the battery state governing Partial Differential Equations (PDE) of charge (equation 1) (Fleischer, W., Z., & D.U., 2013) equations that describe the electrochemical reactions or state of health (equation 2) (Ungurean, Cârstoiu, & inside the battery are approximated into low order Groza, 2016). systems of ODEs equations using a set of Model Order Reduction (MOR) techniques (Fan & Canova, 2017). 1 푡 The most basic electrochemical model is known as 푆푂퐶(푡) = 푆푂퐶(푡 ) − ∫ 휂(푡)퐼 푑푡 (1) 0 푄 푏푎푡 Single Particle Model (SPM) (Figure 1), it represents 푟푎푡푒푑 푡0 Where 푆푂퐶(푡0) represents the initial SOC, 푄푟푎푡푒푑 is the rated capacity and 퐼푏푎푡 is the battery current. 1 푡 (2) 푆푂퐻(푡) = ∫ 퐼(푡)푑푡 푄 푟푎푡푒푑 푡0 Where I is the discharge current. Coulomb counting method requires an accurate estimation of the initial SOC and a precise measurement of the battery current to be able to estimate the battery states as correctly as possible. However, in reality, the measured current includes sensor noise, and it doesn't take into consideration the self-discharge current and current losses during charging and discharging, which makes the measured current different from the true cell current. To overcome those limitations, several researchers have Figure 3 : Internal resistance estimation using DRE proposed modified versions of the coulomb counting approach method. For example, in (Berecibar, et al., 2016) The initial value of the state charge was first estimated using a SOC-OCV mapping function, and then a periodic re- Figure 3 shows the implementation of this approach calibration of the capacity was performed. The measured using the same battery model as 3. The estimated results results have shown a reliable estimation. slightly converge to the true value of the battery internal resistance. The implementation of this method requires a 3.2. Direct Resistance Estimation Algorithm smaller memory space since no training data or initial The life evolution of Li-ion battery
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