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(Soc) Estimation in Lithium-Ion Battery Electric Vehicles

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(Soc) Estimation in Lithium-Ion Battery Electric Vehicles energies Review Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles Ingvild B. Espedal 1 , Asanthi Jinasena 1 , Odne S. Burheim 1 and Jacob J. Lamb 1,2,* 1 Department of Energy and Process Engineering & ENERSENSE, NTNU, 7491 Trondheim, Norway; [email protected] (I.B.E.); [email protected] (A.J.); [email protected] (O.S.B.) 2 Department of Electronic Systems & ENERSENSE, NTNU, 7491 Trondheim, Norway * Correspondence: [email protected] Abstract: Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis. Citation: Espedal, I.B.; Jinasena, A.; Keywords: lithium-ion battery; state-of-charge; modelling; battery management systems Burheim, O.S.; Lamb, J.J. Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles. Energies 2021, 14, 1. Introduction 3284. https://doi.org/10.3390/ Energy storage systems (ESSs) are important technologies for future electric cars and en14113284 smart grids [1–4]. Lithium-ion batteries (LIBs) are the fastest growing ESS technology [5]. Academic Editor: Woojin Choi Despite this, the safety and management of LIBs are vital areas that still require further development [6]. Therefore, LIB management systems (BMSs) are critical for the electri- Received: 14 April 2021 fication of battery electric vehicles (BEVs) and encompass a variety of features to ensure Accepted: 1 June 2021 optimal operation (Figure1). Published: 4 June 2021 The developing advanced and smart state-of-charge (SoC) estimators for LIBs have become an active research topic in recent years. The key technological challenges limiting Publisher’s Note: MDPI stays neutral the advancement of SoC can be gathered into three aspects. The first is that the LIB structure with regard to jurisdictional claims in is nonlinear, making it challenging to model accurately. This is due to the multi-scale nature published maps and institutional affil- (e.g., active materials, cells, and battery packs are all in different spatial scales), and time iations. scale aspects (e.g., aging). Second, the internal environment is difficult to determine and is susceptible to fluctuations of the external environment. Scaling up LIBs from laboratory- to industrial-level production decreases the correlation between calculated values and actual values, rendering it difficult to reliably determine the battery’s internal states. Finally, the Copyright: © 2021 by the authors. LIBs discrepancies directly affect the performance of the LIB pack, raising the instability Licensee MDPI, Basel, Switzerland. of the LIB. Estimation measures designed for smaller LIBs are redundant on large-scale This article is an open access article LIBs (i.e., BEV LIBs), and reliable and precise LIB SoC estimation is difficult. Therefore, distributed under the terms and advanced SoC methods are urgently required to overcome these challenges [7,8]. conditions of the Creative Commons Battery state estimation is a key advanced BMS feature in BEVs. Precise modeling Attribution (CC BY) license (https:// and state estimation will allow stable operation, facilitate optimal battery operation, and creativecommons.org/licenses/by/ provide the fundamentals for security supervision [9]. This review discusses BEV LIB 4.0/). Energies 2021, 14, 3284. https://doi.org/10.3390/en14113284 https://www.mdpi.com/journal/energies Energies 2021, 14, x FOR PEER REVIEW 2 of 24 Energies 2021, 14, 3284 2 of 24 provide the fundamentals for security supervision [9]. This review discusses BEV LIB SoC modelling, estimation and methods. The review culminates in a brief discussion of chal- SoC modelling, estimation and methods. The review culminates in a brief discussion of lenges in BEV LIB SoC prediction analysis. challenges in BEV LIB SoC prediction analysis. Figure 1. Battery management system (BMS) functional features for battery electric vehicle lithium-ion batteries. Figure 1. Battery management system (BMS) functional features for battery electric vehicle lithium-ion batteries. 2. Overview of BEV LIB SoC Modelling Approaches 2. Overview of BEV LIB SoC Modelling Approaches The battery models are useful in model-based SoC estimation and can be characterized as physicalThe battery electrochemical models are models useful [in10 model,11], electrical-based SoC equivalent estimation circuit and models can be [ 12character-,13] and izeddata-driven as physical models electrochemical [14,15], with models the two [10 latter,11], being electrical routinely equivalent used in circuit BEV SoC models estimation. [12,13] Thisand data section-driven introduces models these [14,15], models with withthe two emphasis latter being on data-driven routinely used models in BEV and electricalSoC esti- mation.equivalent This circuit section models, introduces and more these information models with about emphasis their application on data-driven and comparisons models and electricalbetween theequivalent models iscircuit given mo in Sectiondels, and4. more information about their application and comparisons between the models is given in Section 4. 2.1. Physical Electrochemical Models 2.1. PhysicalSingle-particle Electrochemical models Models are the simplest established model for physics-based electro- chemicalSingle analysis-particle [16 models]. Here, are a single the simplest particle established reflects the model Li-ion concentrationfor physics-based distribution electro- chemicalin the electrode. analysis [16]. It can Here, be useda single to analyzeparticle reflects primary the output Li-ion andconcentration electrode distribution solid-phase indiffusion the electrode. effect; however,It can be used theprecision to analyze is primary poor. To output improve and the electrode precision, solid a- modelphase hasdif- fusionbeen developed effect; however, that considers the precision the electrolyte’s is poor. To effect improve on the the output precision, potential, a model suggesting has been a partialdevelop differentialed that considers equation the to electrolyte’s conserve the effect liquid on electrolytethe output materialpotential, [17 suggesting]. a par- tial differentialA pseudo-two-dimensional equation to conserve model the liquid has been electrolyte developed material that considers[17]. that the cell anodeA andpseudo cathode-two as-dimensional porous consisting model ofhas ball-like been developed particles with that the considers electrolyte that filling the cell the anodegaps in-between and cathod [18e as]. Asporous the pseudo-two-dimensional consisting of ball-like particles model includeswith the multipleelectrolyte coupled filling partialthe gaps differential in-between equations, [18]. As it the must pseudo be condensed-two-dimensional from the engineeringmodel includes perspective multiple [19 cou-,20]. pled Apartial key intentiondifferential why equations, physics-based it must be models condensed are hard from to the implement engineering is thatperspective a huge [19,20]amount. of unspecified variables are required to be defined using approaches such as global optimization.A key intention Unsurprisingly, why physics they can-based face models overfitting are or hard local to optimization implement issues.is that Without a huge amountcorrect andof unspecified comprehensive variables parameters, are required the open to be loop defined simulations using approaches of electrochemical such as globalmodels optimization. based on physics Unsurprisingly, are not optimal they can for SoCface overfitting calculations. or High-resolutionlocal optimization detailed issues. Withoutmodels usually correct contain and comprehensive numerous nonlinear parameters, partial the differential open loop equations, simulations which of make electro- the chemicalmodel solving models complex based andon physics computationally are not optimal expensive, for SoC and calculations. is not suitable High for- estimationresolution detailedonline, despite models the usually high contain accuracy. numerous There are nonlinear reduced-order partial modelsdifferential that equations, are less complex which makeand take the lessmodel computational solving complex time and than computationally the full-order models expensive, and and can beis not used suitable in online for estimation online, applications despite [21 the]. However,high accuracy. these There advantages are reduced come- withorder the models cost ofthat increased are less complexestimation and errors. take less computational time
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