Stability Analysis for Li-Ion Battery Model Parameters  and State of Charge Estimation by Measurement  Uncertainty Consideration

Yuan, Shifei; Wu, Hongjie; Ma, Xuerui; Yin, Chengliang

doi:10.3390/en8087729

Cited by 31 publications

(11 citation statements)

References 28 publications

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“…The self-discharge resistance is neglected, as these losses are minimum in Li-Ion technology (2%-10% per month). Authors of [37] apply this model in their stability analysis and SoC estimation method design for a Li-Ion battery. Authors of [38], however, apply this model in their study of batteries parallelization.…”

Section: (First-order) Thevenin Modelmentioning

confidence: 99%

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

et al. 2019

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Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for traction, especially nickel-manganese-cobalt (NMC) and nickel-cobalt-aluminum (NCA). An appropriate model of these batteries is fundamental for the simulation of several processes inside an EV, such as the state of charge (SoC) estimation, capacity and power fade analysis, lifetime calculus, or for developing control and optimization strategies. There are different models in the current literature, among which the electric equivalent circuits stand out, being the most appropriate model when performing real-time simulations. However, impedance models for battery diagnosis are considered very attractive. In this context, this paper compares and contrasts the different electrical equivalent circuit models, impedance models, and runtime models for battery-based EV applications, addressing their characteristics, advantages, disadvantages, and usual applications in the field of electromobility. In this sense, this paper serves as a reference for the scientific community focused on the development of control and optimization strategies in the field of electric vehicles, since it facilitates the choice of the model that best suits the needs required.

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Section: (First-order) Thevenin Modelmentioning

confidence: 99%

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

et al. 2019

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“…The lower sampling time reduced the accuracy of C a and R a , which also changed the estimation accuracy of SOC. According to the Lyapunov's first stability criterion, perturbation caused by noise and unmodeled dynamics has significant effect on the accuracy of battery parameter identification [57]. In other words, the sampling period must be chosen in a way that the substantial changes in the system can be captured.…”

Section: Sensitivity Analysismentioning

confidence: 99%

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

et al. 2018

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Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications.

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“…The increase of 35.5% and 37.8% in estimation error was observed at 0.1 A uncertainty in first and second order battery models respectively. The effects of sensor error and sampling time on states estimation of LIB was studied in [27]. The authors used A first order RC circuit in their work.…”

Section: Introductionmentioning

confidence: 99%

Effect of Sensors Sensitivity on Lithium-Ion Battery Modeled Parameters and State of Charge: A Comparative Study

et al. 2019

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The accurate estimation of the state of charge (SOC) is usually acknowledged as one of the essential features in designing of battery management system (BMS) for the lithium-ion batteries (LIBs) in electric vehicles (EVs). A suitable battery model is a prerequisite for correct SOC measurement. In this work, the first and second order RC autoregressive exogenous (ARX) battery models are adopted to check the influence of voltage and current transducer measurement uncertainty. The Lagrange multiplier method is used to estimate the battery parameters. The sensitivity analysis is performed under the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500 mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative analysis of both models under the perturbed environment has been carried out. The effects of the sensor’s sensitivity on the different battery structures and complexity are also analyzed. Results shows that the voltage and current sensor sensitivity has a significant influence on SOC estimation. This research outcome assists the researcher in selecting the optimal value of sensor accuracy to accurately estimate the SOC of the LIB.

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Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration

Cited by 31 publications

References 28 publications

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

Effect of Sensors Sensitivity on Lithium-Ion Battery Modeled Parameters and State of Charge: A Comparative Study

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