From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Kalawoun, Jana; Biletska, Krystyna; Suard, Frédéric; Montaru, Maxime

doi:10.1016/j.jpowsour.2015.01.038

Cited by 42 publications

(22 citation statements)

References 73 publications

(86 reference statements)

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“…In order to clearly express the influence of sampling delay, Equations (6) and (14), named as the original model and the modified model, are drawn in Figure 2. The meanings of the parameters θ 1 to θ 4 are also signed.…”

Section: Of 16mentioning

confidence: 99%

“…The machine-learning-based models are all black-box models, and the identification results of model parameters strongly depend on the training dataset [9]. Hence, online collecting and online training are required, which needs a lot of memory and computing resources [14]. The ECMs use ideal electric elements, such as voltage source, resistors and capacitors to simulate the external dynamic characteristics of a battery, without considering the chemical reactions inside the battery [11,15,16].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

Jiang

Chen

2019

Energies

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The equivalent-circuit model (ECM) is widely used in online estimating the parameters and states of lithium-ion batteries. However, the sampling delay between the voltage and current of a battery is generally overlooked, which is unavoidable in a modular battery management system (BMS) and would lead to wrong results in the estimation of battery parameters and states. In this paper, with the first-order resistor–capacitor (RC) model as our battery model, we analyze the influence mechanism of sampling delay and then propose an optimized method for online estimating battery parameters. The mathematical model derived from the first-order RC model and the approximation method of first-order derivative are optimized. The recursive least squares (RLS) algorithm is used for identifying the parameters of the model. In order to verify the proposed method, a modular battery test system with high sampling frequency and high synchronization accuracy is developed. The experiment results indicate that the sampling delay would cause the estimation process to fluctuate, and the optimized method effectively improves the tolerance range of sampling delay.

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Section: Of 16mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

Jiang

Chen

2019

Energies

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“…Although numerous SOC estimation methods have been exhibited and classified in [6][7][8][9][10], accurate, efficient, and reliable SOC estimation solutions are still needed in real-time applications. As discussed in our previous work [10], SOC estimation methods are mainly divided into five categories: the Coulomb counting method; the Open Circuit Voltage (OCV) method; the impedance spectroscopy-based method; the model-based method, and the artificial neural network-based method.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

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“…Among the states that should be detected by the BMS, the state-of-charge (SOC) is of great importance [2]. In terms of safety, the SOC should be accurately estimated to avoid over-charge and over-discharge.…”

Section: Introductionmentioning

confidence: 99%

Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

2017

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State-of-charge (SOC) estimation is essential for the safe and effective utilization of lithium-ion batteries. As the SOC cannot be directly measured by sensors, an accurate battery model and a corresponding estimation method is needed. Compared with electrochemical models, the equivalent circuit models are widely used due to their simplicity and feasibility. However, such integer order-based models are not sufficient to simulate the key behavior of the battery, and therefore, their accuracy is limited. In this paper, a new model with fractional order elements is presented. The fractional order values are adaptively updated over time. For battery SOC estimation, an unscented fractional Kalman filter (UFKF) is employed based on the proposed model. Furthermore, a dual estimation scheme is designed to estimate the variable orders simultaneously. The accuracy of the proposed model is verified under different dynamic profiles, and the experimental results indicate the stability and accuracy of the estimation method.

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From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Cited by 42 publications

References 73 publications

Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method

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

Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

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