A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

Zhang, Caiping; Jiang, Jiuchun; Zhang, Linjing; Liu, Sijia; Wang, Leyi; Loh, Poh Chiang

doi:10.3390/en9110900

Cited by 144 publications

(62 citation statements)

References 34 publications

(36 reference statements)

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“…Here the voltage limitation is essential to avoid the generalized problems outlined in literatures are taken into consideration for the operating range of the SOC. Less than 20% and more than 90% of SOC limits are harmful to the battery lifetime [24].…”

Section: Coulomb Counting Methodsmentioning

confidence: 99%

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

et al. 2017

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Abstract:The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate user's "range anxiety". The existing charging stations fail to adopt power provision, allocation and scheduling management. To improve the existing charging infrastructure, data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on the road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

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Section: Coulomb Counting Methodsmentioning

confidence: 99%

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

et al. 2017

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“…The greater the order of the polynomial, the more precise the model is, but the harder it is to implement in real-time systems. Some other models are proposed based on combining exponential and logarithmic functions [20,22,34], and they are said to be the most accurate. In fact, the Weng model presented in [22] is proven to be more accurate than polynomial models and tracks the phenomenological aspect of the battery, but it presents a great number of parameters.…”

Section: Ocv Model Functionsmentioning

confidence: 99%

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

et al. 2017

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Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

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“…Here the voltage limitations are essential to avoid the generalized problems outlined in literature are taken into consideration for the operating range of the SOC. Less than 20% and more than 90% of SOC limits are harmful to the battery lifetime [22].…”

Section: Coulomb Counting Methodsmentioning

confidence: 99%

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Bharatiraja¹,

Padmanaban²,

Siano³

et al. 2017

Preprint

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Abstract:The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users' "range anxiety". The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

show abstract

A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

Cited by 144 publications

References 34 publications

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems

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