Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

Wang, Shunli; Fernandez, Carlos; Shang, Liping; Li, Zhanfeng; Li, Jianchao

doi:10.1016/j.est.2016.09.008

Cited by 56 publications

(20 citation statements)

References 65 publications

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“…However, the accuracy of this method depends on the establishment of a battery equivalent model, and some physical properties of the battery model are nonlinear. The EKF algorithm [20,21] and the UKF algorithm are improved KF algorithms. The EKF algorithm implements recursive filtering by linearizing nonlinear functions [22], and the UKF algorithm applies nonlinear system equations to the standard Kalman filter system by means of unscented transformation (UT).…”

Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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Abstract:The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over-and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm.

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Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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“…They also discussed the battery geometry effects on the battery parameters. Some variants of the EKF have also been reported [88][89][90]. The robust EKF (REKF) addresses the uncertainty in the battery modelling and linearization error.…”

Section: Reference Mae (%)mentioning

confidence: 99%

“…He et al 2011 [83] ≤± 1.06% Zhu et al 2012 [84] ME ≤ ± 4.2% Xiong et al 2012 [81] ≤± 2.0% Jiang et al 2013 [82] ≤± 1.0% Hu et al 2013 [88] ≤± 1.0% Chen et al 2013 [85] ≤± 3.0% Xiong et al 2014 [91] ≤± 1.5% Sepasi et al 2014 [89] ≤± 1.5% Wang et al 2017 [90] Unspecified Xie et al 2018 [86] ME ≤ ± 2.0% Yang et al 2017 [92] ME ≤ ± 3.0% Pan et al 2017 [93] ≤± 1.3% Huang et al 2018 [78] Unspecified Xu et al 2012 [94] ME ≤ ± 0.6%…”

Section: Reference Mae (%)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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“…To overcome these issues, model-based, rule-based and artificial intelligence-based techniques have been proposed in recent research findings. The first family includes the Kalman Filter (KF) [10], Extended Kalman Filter (EKF) [11][12][13][14], Unscented Kalman Filter (UKF) [15,16], Adaptive Particle Filter (APF) [17], and Smooth Variable Structure Filter (SVSF) [18,19]. These solutions exploit the aforementioned direct methods for tuning of the reference model and are heavily dependent on its accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

et al. 2019

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This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

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Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

Abstract: Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method. 2017

Cited by 56 publications

References 65 publications

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

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

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

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