Modeling and simulation of first-order Li-Ion battery cell with experimental validation

Nemeș, Raul; Ciornei, Sorina; Ruba, Mircea; Hedeşiu, Horia; Marțiș, Claudia

doi:10.1109/mps.2019.8759769

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…The equivalent circuit model can more accurately describe the dynamic behavior of a battery, and it is easy to implement and readily improves the accuracy in SoC estimation. For the Rint model [23], Thevenin model [15], and the second-order resistance capacitance parallel model [24] in the equivalent circuit model, fully considering the complexity and accuracy of the model, the commonly used Thevenin equivalent circuit model with a first-order RC circuit was selected to establish the battery state space model, as Figure 1 illustrates. Based on the Thevenin equivalent battery model, and according to Kirchhoff's law, the continuous state space equation of the battery is obtained, that is, the voltage and current are as follows (2) [25]:…”

Section: Battery State Space Modelmentioning

confidence: 99%

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

Liu,

Zhang

2023

Applied Sciences

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In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integralalgorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.

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Section: Battery State Space Modelmentioning

confidence: 99%

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

Liu,

Zhang

2023

Applied Sciences

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“…The description of ECM is more simple and more intuitive, the equation is simpler, the detailed calculation of the internal electrochemical process is avoided, and the parameters of the model are easier to identify (Lai et al, 2018;Dong et al, 2018b). Common ECMs include R int (Nemes et al, 2019), Thevenin (Hentunen et al, 2014), and PNGV (Zhang et al, 2017). In order to realize dynamic SOC estimation, the filtering algorithm is often combined with a battery model to form a model-based SOC estimation method.…”

Section: Literature Reviewmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter

Zhang

Zhu

et al. 2022

Front. Energy Res.

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Accurate estimation of the state of charge plays a very important role in ensuring the safe and effective operation of battery lithium-ion batteries and is one of the most important state parameters. However, the estimation method of state of charge has various limitations, so it is of great significance to improve the accuracy and calculation speed of the method. In this article, we propose an improved recurrent neural network model to estimate lithium-ion battery state of charge. Simple recurrent units are used to replace the traditional recurrent neural network basic unit or long short-term memory unit, and the computation speed is improved by implementing parallel processing. Finally, the prediction results of the model are fed into an unscented Kalman filter module to remove the interference of noise on the prediction. This article studies the prediction accuracy and speed of Samsung INR 18650-20R and INR 18650-25R under various ambient temperatures, initial state of charge values, and electric vehicle drive cycles. The results show that the proposed method can obtain accurate state of charge estimation results in the INR 18650-20R data set. For different temperatures and initial SOC, the root mean square error is less than 0.015 and 0.016, and the prediction speed is about 30% higher than that of long short-term memory. In the INR 18650-25R data set, for three different driving cycles, the root mean square error is less than 0.034, and the average test speed is about 2.7s, which proves the effectiveness of this method in estimating accuracy and speed.

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“…Higher-order models would provide more accurate estimates; however, for our case study, a first-order model is sufficient to model the bus battery. In addition, the first-order model is ideal for integration into the global model, as using more complex models would require more computational cost [37].…”

Section: Battery Modelmentioning

confidence: 99%

Integral Thermal Management Studies in Winter Conditions with a Global Model of a Battery-Powered Electric Bus

et al. 2022

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The potential of e-bus transportation to improve air quality and reduce noise pollution in cities is significant. In order to improve efficiency and extend the useful life of these vehicles, there is a growing need to investigate improvements for the thermal management system of electric city buses. In electric vehicles, there are several systems whose thermal behaviors need to be regulated, such as batteries, electric machines, power electronics, air conditioning, and cabin. In this study, a 0D/1D model of an electric city bus is developed that integrates all sub-models of the powertrain, auxiliaries, and thermal management system. This model is used to evaluate different configurations and thermal management strategies of the electric urban bus by simulating public transport driving cycles in Valencia, Spain, under winter conditions. First, the original thermal–hydraulic circuit of the bus was modified, resulting in an improvement in the battery energy consumption with savings of 11.4% taking advantage of the heat produced in the electric motors to heat the battery. Then, the original PTC heating system of the bus was compared with a proposed heat pump system in terms of battery power consumption. The heat pump system achieved an energy savings of 3.9% compared to the PTC heating system.

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Modeling and simulation of first-order Li-Ion battery cell with experimental validation

Cited by 22 publications

References 8 publications

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

State of Charge Estimation for Lithium-Ion Batteries Using Simple Recurrent Units and Unscented Kalman Filter

Integral Thermal Management Studies in Winter Conditions with a Global Model of a Battery-Powered Electric Bus

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