A simple battery model for EV range prediction: Theory and experimental validation

Barcellona, Simone; Grillo, Samuele; Piegari, Luigi

doi:10.1109/esars-itec.2016.7841441

Cited by 21 publications

(30 citation statements)

References 28 publications

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“…In the following, starting from the simplest procedures, the identification techniques proposed in the literature will be quickly reported. In [146,147], with reference to an electrical equivalent model with only one RC branch (see Figure 5), the parameters were obtained using simple constant current discharge curves. Because the dependence of the model parameters on the current and temperature was considered, the tests were repeated at different temperatures and currents.…”

Section: Offline Identification Methodsmentioning

confidence: 99%

“…The tests were performed in a climate chamber, but the temperature of the cell was not controlled. In [147], tests were also used to define an apparent state of discharge accounting only for the usable part of the residual energy inside the battery. In [124], for the same model, the resistive and capacitive parameters were estimated using pulsed discharge tests.…”

Section: Offline Identification Methodsmentioning

confidence: 99%

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Lithium Ion Battery Models and Parameter Identification Techniques

2017

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Nowadays, battery storage systems are very important in both stationary and mobile applications. In particular, lithium ion batteries are a good and promising solution because of their high power and energy densities. The modeling of these devices is very crucial to correctly predict their state of charge (SoC) and state of health (SoH). The literature shows that numerous battery models and parameters estimation techniques have been developed and proposed. Moreover, surveys on their electric, thermal, and aging modeling are also reported. This paper presents a more complete overview of the different proposed battery models and estimation techniques. In particular, a method for classifying the proposed models based on their approaches is proposed. For this classification, the models are divided in three categories: mathematical models, physical models, and circuit models.

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Section: Offline Identification Methodsmentioning

confidence: 99%

Section: Offline Identification Methodsmentioning

confidence: 99%

Lithium Ion Battery Models and Parameter Identification Techniques

2017

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“…IS was applied to measure the SOC of a Li-ion battery. In the IS method, different current frequencies are applied across the Li-ion battery to determine the impedance [35][36][37][38]. Once the internal impedance is known, it can be plotted easily against the SOC.…”

Section: Impedance Spectroscopy (Is)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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“…In the four mentioned models, the battery SoC is directly incorporated into the model using its relationship with the battery OCV. If the scope of the model is to simulate dynamics slower than 1 s, it is possible to use an ECM like the described Thevenin model [51]. A possible variation is not to incorporate the battery SoC in the equations, but to estimate the battery OCV using the model In practical, all the parameterization values of the resistances and capacitors depend on the particular operating point of the battery, so they are vary with time, temperature, power demand, SoC, and SoH.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

“…The impedance spectroscopy of a battery cell can be approximated with two circles on the Nyquist plane [51]. In recent years, methods based on electrochemical impedance spectroscopy (EIS) have found application for accurate analysis of occurring electrochemical processes and diagnosis of LiBs, as the cell chemistry has a strong effect on the curve of the impedance spectrum [52,53].…”

Section: Impedance Measurement-based Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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A simple battery model for EV range prediction: Theory and experimental validation

Cited by 21 publications

References 28 publications

Lithium Ion Battery Models and Parameter Identification Techniques

Lithium Ion Battery Models and Parameter Identification Techniques

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

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