Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

Meng, Jinhao; Luo, Guangzhao; Ricco, Mattia; Świerczyński, Maciej; Stroe, Daniel‐Ioan; Teodorescu, Remus

doi:10.3390/app8050659

Cited by 211 publications

(102 citation statements)

References 70 publications

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“…The content of model‐based SOC estimation method incorporates of two main parts: application of battery models and appropriate algorithm. The battery model is used to calculate the voltage, current and temperature, and the estimation algorithm evaluates the SOC . At present, there are two kinds of electrical battery models used in SOC estimation, namely, electrochemical model and ECM.…”

Section: Model‐based Bms Applicationmentioning

confidence: 99%

A review on battery management system from the modeling efforts to its multiapplication and integration

2019

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Summary Progress in battery technology accelerates the transition of battery management system (BMS) from a mere monitoring unit to a multifunction integrated one. It is necessary to establish a battery model for the implementation of BMS's effective control. With more comprehensive and faster battery model, it would be accurate and effective to reflect the behavior of the battery level to the vehicle. On this basis, to ensure battery safety, power, and durability, some key technologies based on the model are advanced, such as battery state estimation, energy equalization, thermal management, and fault diagnosis. Besides, the communication of interactions between BMS and vehicle controllers, motor controllers, etc is an essential consideration for optimizing driving and improving vehicle performance. As concluded, a synergistic and collaborative BMS is the foundation for green‐energy vehicles to be intelligent, electric, networked, and shared. Thus, this paper reviews the research and development (R&D) of multiphysics model simulation and multifunction integrated BMS technology. In addition, summary of the relevant research and state‐of‐the‐art technology is dedicated to improving the synergy and coordination of BMS and to promote the innovation and optimization of new energy vehicle technology.

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Section: Model‐based Bms Applicationmentioning

confidence: 99%

A review on battery management system from the modeling efforts to its multiapplication and integration

2019

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“…The authors showed that increasing the number of equivalent models by more than two brings no benefits as the complexity during parameterization increases along with the estimation error, whereas the EKF cannot overcome this modeling weakness which results in the SoC error becoming increasingly large when using a high-order RC model. Lastly, in [97], authors use a GA to investigate four different modeling approaches of an LFP 10 Ah cell. A crude approximation of the LFP's dynamics is made with a mathematical combined model, a better performance is achieved with a 2nd order ECM, whereas a single-particle model and a data-driven with support vector machine shown the best accuracy but with significantly increased required computational time.…”

Section: Parameter Extraction With Heuristic Optimizationmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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“…The assumption of 100% rise considered here is taken as a worst case scenario. Based on the objectives of the investigation, the use of various battery models such as empirical models, physico-chemical models and equivalent circuit models is prevalent [33]- [36] in scientific circles. Physico-chemical models are the closest to the underlying electrochemical processes taking place within the cell, whereas the equivalent circuit models, although quite popular in their usage, present a higher degree of abstraction, employing electrical circuit analogies to approximate accuracy [38]- [41].…”

Section: B Battery Systemmentioning

confidence: 99%

Topology and Efficiency Analysis of Utility-Scale Battery Energy Storage Systems

Parlikar¹,

Hesse²,

Jossen³

2019

Proceedings of the 13th International Renewable Energy Storage Conference 2019 (IRES 2019)

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Energy storage is an important flexibility measure to stabilize and secure the electrical energy supply system. Lithium-ion battery energy systems (BESS) are, owing to their characteristics, uniquely poised to support and augment the functioning of the energy supply system. It is crucial to identify and analyze the factors which play a role in their efficient and effective operation. This paper identifies and analyses three such major factors-application scenarios, power electronics with power distribution strategies, and battery parameters which influence the efficiency of a BESS. The applications analyzed are primary control reserve and peak shaving. Two Power electronics topologies and their load distribution strategies are presented, with their influence on the conversion efficiency being evaluated subsequently. Two commercial lithium-ion technologies-a Lithium Iron Phosphate cathode/Graphite anode cell and a Lithium Nickel Manganese Cobalt Oxide cathode/Graphite anode cell are also simulated for two states of health (SOH). The aged cells are considered to possess a capacity equal to 80% of original nominal capacity and a cell resistance twice that of the new cells. It is found that the system conversion efficiency can be greatly improved in applications with low active chargebased and high temporal utilization ratios by deploying a suitable power electronics topology and load distribution strategy. For applications with high active charge-based and low temporal utilization ratios, the battery resistance and the serial-parallel combination play an important role.

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Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles

Cited by 211 publications

References 70 publications

A review on battery management system from the modeling efforts to its multiapplication and integration

A review on battery management system from the modeling efforts to its multiapplication and integration

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Topology and Efficiency Analysis of Utility-Scale Battery Energy Storage Systems

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