Extended Kalman Filter based battery state of charge(SOC) estimation for electric vehicles

Jiang, Chenguang; Taylor, Allan; Duan, Chang‐Kui; Bai, Hua

doi:10.1109/itec.2013.6573477

Cited by 43 publications

(20 citation statements)

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“…In [91,92], NN; in [93], adaptive wavelet neural network (AWNN); and in [94], Elman neural network (ENN) methods are proposed to estimate the SOC of lithium ion batteries. Many researchers pay attention to KF [95,96]; and its derivatives broadly such as series Kalman filter (SKF) [97]; EKF [10,[98][99][100][101][102][103][104][105][106][107][108]; improved extended Kalman filter (IEKF) [109]; AEKF [110][111][112][113]; model adaptive extended Kalman filter (MAEKF) [114]; robust extended Kalman filter (REKF) [115]; multiscale extended Kalman filter (MEKF) [116]; UKF [117][118][119]; adaptive unscented Kalman filter (AUKF) [120,121]; sigma point Kalman filter (SPKF) [122,123] and iterated extended Kalman filter (ITEKF) [124]. In [119], a modified battery equivalent circuit model is designed that contains the impact of different temperatures and current rates on the SOC.…”

Section: Battery State Of Charge (Soc) Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

305

119

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Section: Battery State Of Charge (Soc) Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

305

119

View full text Add to dashboard Cite

“…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%

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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“…Accurate SOC estimation improves battery life, prevents over‐discharge, and assists application developers to adopt rational control strategies to save energy . State of charge for smartphone battery is estimated based on coulomb counting and terminal voltage method . Coulomb counting estimates SOC based on an accumulative current drop by directly accessing the current sensor within smartphone devices.…”

Section: Introductionmentioning

confidence: 99%

A survey on energy estimation and power modeling schemes for smartphone applications

Ahmad

Gani

Hamid

et al. 2016

Int J Communication

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In the last decade, the rising trend in the popularity of smartphones motivated software developers to increase application functionality. However, increasing application functionality demands extra power budget that as a result, decreases smartphone battery lifetime. Optimizing energy critical sections of an application creates an opportunity to increase battery lifetime. Smartphone application energy estimation helps investigate energy consumption behavior of an application at diversified granularity (eg, coarse and fine granular) for optimal battery resource use. This study explores energy estimation and modeling schemes to highlight their advantages and shortcomings. It classifies existing smartphone application energy estimation and modeling schemes into 2 categories, ie, code analysis and mobile components power model-based estimation owing to their architectural designs. Moreover, it further classifies code analysis-based modeling and estimation schemes in simulation-based and profiling-based categories. It compares existing energy estimation and modeling schemes based on a set of parameters common in most literature to highlight the commonalities and differences among reported literature. Existing application energy estimation schemes are low-accurate, resource expensive, or non-scalable, as they consider marginally accurate smart battery's voltage/ current sensors, low-rate power capturing tools, and labor-driven lab-setting environment to propose power models for smartphone application energy estimation. Besides, the energy estimation overhead of the components power model-based estimation schemes is very high as they physically run the application on a smartphone for energy profiling. To optimize smartphone application energy estimation, we have highlighted several research issues to help researchers of this domain to understand the problem clearly. KEYWORDS application energy, energy estimation, energy profiling, profiling overhead 1 | INTRODUCTION Nowadays, with the proliferation of portable devices, the energy-efficient system design has become a must-to-meet requirement for recent resource-constrained smartphone devices. The ever growing smartphone user's demands encourage application developers to enrich the legacy applications that as a result, increase smartphone's computational and communication cost. Among all smartphone-based applications, on-demand video, 1 multi-agent-based distributed games, 2 spatial locality-based social applications, 3 pedestrian tracking, 4,5 and context-based advertisement services 6 are the most energy-hungry services. The inherent features of these services considerably increase the energy demands of processors when executed on smartphones. 7-9 It is estimated that in last 2 decades, processor power budget has increased significantly because of smartphone application's high resource use. To efficiently exploit smartphone's power budget, software optimization minimizes inter-component interaction among hardware modules that as a result, increases battery's lifetime. ...

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Extended Kalman Filter based battery state of charge(SOC) estimation for electric vehicles

Cited by 43 publications

References 1 publication

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

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

A survey on energy estimation and power modeling schemes for smartphone applications

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