A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen, Zheng; Li, Xiaoyu; Shen, Jiangwei; Yan, Wensheng; Xiao, Renxin

doi:10.3390/en9090710

Cited by 40 publications

(22 citation statements)

References 41 publications

(22 reference statements)

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“…Research has been conducted to forecast EV vehicle battery power management to best utilize the advantages of the charging process [12,13]. However, these concepts are not included in the dynamic charge scheduling management facilities based on the vehicle location and SOC [14][15][16]. In order to achieve this, the vehicle and charging station communicate with each other for the reservation of slots according to the availability and the cost functions [17].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…To meet operational power and energy demands, the lithium-ion battery packs in EVs are composed of hundreds to thousands of cells connected in series, parallel, or even more complex manners. Hence, it is imperative to guarantee and oversee the battery pack's proper operation [3,4]. A commonly-accepted solution is that a battery management system (BMS) is employed to monitor a battery pack's status and ensure its safety and performance optimization.…”

Section: Introductionmentioning

confidence: 99%

An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles

Shu

Shen

et al. 2017

Energies

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Battery remaining useful life (RUL) estimation is critical to battery management and performance optimization of electric vehicles (EVs). In this paper, we present an effective way to estimate RUL online by using the support vector machine (SVM) algorithm. By studying the characteristics of the battery degradation process, the rising of the terminal voltage and changing characteristics of the voltage derivative (DV) during the charging process are introduced as the training variables of the SVM algorithm to determine the battery RUL. The SVM is then applied to build the battery degradation model and predict the battery real cycle numbers. Experimental results prove that the built battery degradation model shows higher accuracy and less computation time compared with those of the neural network (NN) method, thereby making it a potential candidate for realizing online RUL estimation in a battery management system (BMS).Keywords: mean square error (MSE); remaining useful life (RUL); support vector machine (SVM); voltage derivative (DV)

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“…The extended Kalman filtering algorithm includes a highly efficient observer, which also presents high robustness against non-linear systems [8,9]. To apply the EKF in the insulation monitoring system, the system should be described by a state space model [10], which can be expressed as: Equation (6) can be acquired via the following steps:…”

Section: Design Of the Discrete Extended Kalman Filtermentioning

confidence: 99%

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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Abstract:To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

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A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Cited by 40 publications

References 41 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

An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

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