A Variable Effective Capacity Model for &lt;inline-formula&gt; &lt;tex-math notation="TeX"&gt;$\hbox{LiFePO}_{4}$&lt;/tex-math&gt;&lt;/inline-formula&gt; Traction Batteries Using Computational Intelligence Techniques

Sánchez, Luciano; Blanco, C.; Anton, J.C.; García, Víctor M.; González, Miguel Ángel Álvarez; Viera, J.C.

doi:10.1109/tie.2014.2327552

Cited by 24 publications

(10 citation statements)

References 38 publications

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“…Other kinds of grey boxes have been successfully used in State of Charge (SoC) predictive models [22,23], and these can be adapted to the problem at hand with certain modifications. These models are based on a physical analogy between charging a battery and filling a flexible vessel, because the function linking the height of a fluid with its mass, in a vessel with the appropriate shape, can also measure the interdependence between the voltage of a battery and its charge.…”

Section: A Semiphysical Model-based Soft Sensor Of the Sohmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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A model-based virtual sensor for assessing the health of rechargeable batteries for cyber-physical vehicle systems (CPVSs) is presented that can exploit coarse data streamed from on-vehicle sensors of current, voltage, and temperature. First-principle-based models are combined with knowledge acquired from data in a semiphysical arrangement. The dynamic behaviour of the battery is embodied in the parametric definition of a set of differential equations, and fuzzy knowledge bases are embedded as nonlinear blocks in these equations, providing a human understandable reading of the State of Health of the CPVS that can be easily integrated in the fleet through-life management.

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Section: A Semiphysical Model-based Soft Sensor Of the Sohmentioning

confidence: 99%

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

et al. 2017

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“…In order to meet the unexpected driver behavior and different load condition, HESS is designed with various artificial intelligence techniques. Recent preachers mostly concentrating on the optimal usage of fuels which are used to drive the electric vehicles mainly on batteries and different algorithms are proposed to find the optimal way of utilizing the energy sources [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Modelling and analysis of a hybrid controller applied to the ultracapacitor based solar powered electric vehicle

Katuri¹,

Gorantla²

2018

MMC_A

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The high power density capability of Ultracapacitor (UC) can be utilized by developing of Hybrid Energy Storage System (HESS) with a conventional power source, battery. UC power mainly used during peak power requirement of electric vehicle (EV) / Hybrid electric vehicle (HEV) on the other hand side battery is treated as a main source of the entire system and it serves the average power to the load. Energy management between the sources, is the primary difficulty associated with HESS powered electric vehicles. The main aim of this work is to design a new control strategy approach which is used to switch the power sources according to the electric vehicle dynamics. Four math functions are created individually according to the speed of an electric motor, named as Math Function Based (MFB) controller. The designed MFB controller is combined with a Proportional Integral (PI) controller in order to achieve the main objective and applied to the solarpowered electric vehicles for a smooth transition between battery and UC. The principal goal of the designed MFB controller always regulates the pulse signals generated by the conventional PI controller, and this scenario happens with respect to the speed of an electric motor. A solar panel is connected to the electric vehicle which is used to charge the battery charge based on irradiance, temperature available conditions and discharge the same amount of energy during unavailable timings of sunlight All modes of the circuit is simulated in MATLAB and results are plotted, discussed in simulation results and discussion section.

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“…The high energy density of a source enhances the driving range and High power density can provide the quick power to the vehicle during transient periods. In order to obtain the efficient energy source for electric vehicle hybridization of both the source are required [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Simulation and modelling of Math Function Based controller implemented with fuzzy and artificial neural network for a smooth transition between battery and ultracapacitor

Katuri¹,

Gorantla²

2018

AMA_C

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Electric vehicles (EVs)/Hybrid electric vehicles (HEVs) are implemented with Hybrid Energy Storage System (HESS) to obtain the effective results. HESS has been framed by combining battery with ultracapacitor (UC). Here the battery is used to supply the average power whereas UC can meet the transient power requirement of an electric vehicle. UC always assists the battery during peak power requirements and starting of the motor can also be done. The problem associated with HESS powered vehicle is switching between battery and UC depending upon vehicle road conditions. The main aim of this work is to design a controller for proper switching of energy sources in HESS. With four individual math function, one controller has been designed based on the speed of the electric motor, named as Math Function Based (MFB) controller, further, this has been integrated with ANN as well as Fuzzy logic made two new hybrid controllers. After that two-hybrid controllers have been implemented for the electric motor, thereafter comparative analysis has been made between them and suggested one good controller based on different comparative factors. The two-hybrid controllers have been implemented in four modes and results are discussed in the simulation results and discussion section.

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A Variable Effective Capacity Model for <inline-formula> <tex-math notation="TeX">$\hbox{LiFePO}_{4}$</tex-math></inline-formula> Traction Batteries Using Computational Intelligence Techniques

Cited by 24 publications

References 38 publications

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

A Model-Based Virtual Sensor for Condition Monitoring of Li-Ion Batteries in Cyber-Physical Vehicle Systems

Modelling and analysis of a hybrid controller applied to the ultracapacitor based solar powered electric vehicle

Simulation and modelling of Math Function Based controller implemented with fuzzy and artificial neural network for a smooth transition between battery and ultracapacitor

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