Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis

Ramadan, Haïtham Saad Mohamed; Becherif, Mohamed; Claude, F.

doi:10.1016/j.ijhydene.2017.07.219

Cited by 93 publications

(23 citation statements)

References 25 publications

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“…Appl. 2019, 9,x FOR PEER REVIEW 17 of 25 In the first fifth seconds, the panel was put out of the system to observe the behavior of the battery. We note the same decrease in the voltage of the battery with and without a collaborative algorithm.…”

Section: Third Scenario Variation Of Illumination and Temperaturementioning

confidence: 99%

“…In other literatures, authors favor algorithms that rely on instantaneous measurements of voltage and currents across the battery, using programmable models or electronic components to estimate the SOC (State Of Charge). This is done by an extended Kalman filter in References [8,9]. In References [10,11], a group of researchers have thought as well about implementing a MPPT (Maximum Power Point Tracking) algorithm of a variable size incremental conductance method.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Rezoug

Chenni

Taibi

2019

JLPEA

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With the continuous growth of energy consumption, the rationalization of energy has become a priority. The photovoltaic energy sector remains a major occupation for researchers in the field of production optimization or storage methods. The concept developed in this work is a mixed optimization approach for energy management during battery charging with a duty cycle. A selective collaborative algorithm intervenes to choose and use the appropriate results of the few techniques to optimize the charging time of a battery and estimate its state of charge by using the minimum possible tools. This is done using a collective database that is accessible in real time. It also effectively allows the synchronization of information between several customers. This approach is performed on a mobile application on android, through a Google Firebase platform that allows us to secure collaborative access between multiple customers and use the results of the calculations of some algorithms. It gives us the values obtained by the various sensors in real time to accelerate the charging speed of the battery. The validation of this approach led us to practice a few scenarios using an Arduino board to show that this approach has a better performance.

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Section: Third Scenario Variation Of Illumination and Temperaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Rezoug

Chenni

Taibi

2019

JLPEA

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“…Lots of scholars have proposed many SOC estimation methods, such as the open circuit voltage method [3,4], the Coulomb counting method [5], the neural network method [6] and the Kalman filtering algorithm [7]. Among them, the open circuit voltage method was to first establish a corresponding function of the open circuit voltage and the SOC and then obtain the SOC by measuring the open circuit voltage after the battery was stationary [8]; the Coulomb integral method, which discretizes the current flowing through the battery and sums it up, and obtains the SOC value by simple division [9]; the neural network method optimizes the relevant parameters of the SOC estimation algorithm and solves complex abstract problems through autonomous learning [7]; a series of Kalman filtering algorithms based on the extended Kalman filtering algorithm optimize autoregressive data processing, which can make the optimal estimation in the minimum variance sense for the state of the dynamic system [10,11].…”

Section: Introductionmentioning

confidence: 99%

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

et al. 2019

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In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

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“…Online LIB internal resistance measurement application of SOC estimation was realized by using the EKF algorithm (D. Wang, Bao, & Shi, 2017). EKF is used for the accurate SOC estimation of lithium-based batteries together with a comparative analysis (Ramadan, Becherif, & Claude, 2017). The SOC estimation of LIBs was realized by using a grey EKF algorithm and a novel OCV modelling method (Pan, Lu, Lin, Li, & Chen, 2017), and an EKF-based SOC estimation algorithm was proposed to realize the safety protection for the Unmanned Aerial Vehicle (UAV) LIB packs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive State of Charge Iterative Calculation Method by Using the Improved Unscented Kalman Filtering Algorithm for the Lithium Ion Battery Packs

Wang¹,

Fernandez²,

Yu³

et al. 2018

Preprint

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The state of charge estimation is an important part of the battery management system, the estimation accuracy of which seriously affects the working performance of the lithium ion battery pack. The unscented Kalman filter algorithm has been developed and applied to the iterative calculation process. When it is used to estimate the SOC value, there is a rounding error in the numerical calculation. When the sigma point is sampled in the next round, an imaginary number appears, resulting in the estimation failure. In order to improve the estimation accuracy, an improved adaptive square root - unscented Kalman filter method is introduced which combines the QR decomposition in the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The error remains about 1.60% under the condition of drastic voltage and current changes. The conclusion of this experiment can provide a theoretical basis of the state of charge estimation in the battery management of the lithium ion battery pack.

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Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis

Cited by 93 publications

References 25 publications

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm

A Novel Adaptive State of Charge Iterative Calculation Method by Using the Improved Unscented Kalman Filtering Algorithm for the Lithium Ion Battery Packs

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