A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Guo, Ruohan; Shen, Weixiang

doi:10.3390/vehicles4010001

Cited by 61 publications

(9 citation statements)

References 91 publications

(127 reference statements)

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“….) and the battery terminal voltage [11,12]. By increasing the number of model parameters, it is possible to provide a more detailed description of the dynamics of the battery open circuit voltage.…”

Section: Introductionmentioning

confidence: 99%

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

Radaš,

Matić,

Šunde

et al. 2024

Processes

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This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.

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Section: Introductionmentioning

confidence: 99%

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

Radaš,

Matić,

Šunde

et al. 2024

Processes

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“…The Rint model depicted in Figure 6(a), which attributes the entire BU internal voltage response to internal resistance (Rsb), is the fundamental ECM. The major limitation of the Rint model is that the polarization voltage cannot be properly reflected in the state of power estimation of the BU [36]. To overcome this limitation of the Rint model, a linear regression model has been proposed [37].…”

Section: A Dynamic Battery Modelmentioning

confidence: 99%

“…The first-order Thevenin BU model is an enhanced and marginally more intricate comparable circuit model. This formulation includes an extra resistor and capacitor (R1 and C1) that are parallel to one another, but in series with the fundamental Rint model, as represented in Figure 6(d) [36]. The BU's dynamic response is modelled using the additional resistor/capacitor network.…”

Section: A Dynamic Battery Modelmentioning

confidence: 99%

“…A similar methodology to that of [44] has been used to formulate the dynamic second-order Thevenin BU model as a function of SoC [46]. In addition, open circuit tests and hybrid pulse power characterization (HPPC) tests have been performed on first-order and second-order Thevenin BU models to estimate the variations in the OCV, resistance, and capacitance values [47], [48]. Similar to this approach, open circuit tests and HPPC tests were carried out for BU modelling by [49] at various temperatures.…”

Section: A Dynamic Battery Modelmentioning

confidence: 99%

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Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

Prasanthi,

Shareef,

Errouissi

et al. 2024

IEEE Access

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The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

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“…There are review papers in the literature that focus on separate aspects of energy storage systems, such as highlighting the characteristics of these storage systems [12,13] or providing only their electrical circuit models [14,15], while others only briefly discuss some possible schemes for connecting these storage systems in hybrid mode for power electronic applications [16]. However, given the importance of this topic, it is necessary to have a single paper that comprehensively surveys all these aspects of energy storage systems, beginning with their operational principles, characteristics, and how they compare with each other, as well as their various electric models, and the different schemes for connecting them in a hybrid mode.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Energy Storage Systems Characteristics and Models for Automotive Applications

Nkembi,

Simonazzi,

Santoro

et al. 2024

Batteries

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Currently, the electrification of transport networks is one of the initiatives being performed to reduce greenhouse gas emissions. Despite the rapid advancement of power electronic systems for electrified transportation systems, their integration into the AC power grid generates a variety of quality issues in the electrical distribution system. Among the possible solutions to this challenge is the inclusion of continuous storage systems, which can be located either onboard or offboard. The rapid development of energy storage devices has enabled the creation of numerous solutions that are leading to ever-increasing energy consumption efficiency, particularly when two or more of these storage systems are linked in a cascade and a hybrid mode. The various energy storage systems that can be integrated into vehicle charging systems (cars, buses, and trains) are investigated in this study, as are their electrical models and the various hybrid storage systems that are available.

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A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Cited by 61 publications

References 91 publications

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

Comprehensive Review of Energy Storage Systems Characteristics and Models for Automotive Applications

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