Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles

He, Haibo; Zhang, Xiaowei; Xiong, Rui; Xu, Yide; Guo, Hongwei

doi:10.1016/j.energy.2012.01.009

Cited by 430 publications

(186 citation statements)

References 18 publications

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“…The recursive least squares (RLS) algorithm is usually used to estimate parameters in the normal battery model [19]. In this paper, the RLS algorithm was used to estimate model parameters such as and (Figure 2b).…”

Section: Ocv Estimationmentioning

confidence: 99%

“…A normal Li-ion battery can be represented by an equivalent circuit model (Figure 2a) [15,17,19] that consists of OCV , internal resistance , load current and terminal voltage . The Li-ion …”

Section: Equivalent Circuit Modelsmentioning

confidence: 99%

“…A normal Li-ion battery can be represented by an equivalent circuit model (Figure 2a) [15,17,19] that consists of OCV V oc , internal resistance R, load current I and terminal voltage V t . The Li-ion battery with ISCr can be represented by a similar equivalent circuit model (Figure 2b) [15,17,20] where I 1 f is the current that flows within the battery, and I 2 f is the current that flows through the R ISC f .…”

Section: Equivalent Circuit Modelsmentioning

confidence: 99%

“…The simulation model was configured using MATLAB/Simulink [23,24]. Resistance 0 , resistance 1 and capacitance 1 ( Figure 6) were estimated using the RLS algorithm with experimental data of cell A and DST 5 A [19], then used to build the simulation model. …”

Section: Configuration Of Simulation Environmentmentioning

confidence: 99%

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Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

et al. 2017

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Abstract:Early detection of an internal short circuit (ISCr) in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM) is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R ISC f estimates. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R ISC f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R ISC f . Then the next R ISC f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R ISC f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

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Section: Ocv Estimationmentioning

confidence: 99%

Section: Equivalent Circuit Modelsmentioning

confidence: 99%

Section: Equivalent Circuit Modelsmentioning

confidence: 99%

Section: Configuration Of Simulation Environmentmentioning

confidence: 99%

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Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

et al. 2017

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“…The key piece of information required in order to provide an accurate range estimation is the State of Charge (SoC) of the vehicle's traction battery [3][4][5][6][7][8][9][10][11][12]. In mass produced EVs, this data is calculated as part of a comprehensive battery management and protection system.…”

Section: Introductionmentioning

confidence: 99%

Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions

Tannahill

Muttaqi

Sutanto

2016

IET Electrical Systems in Transportation

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, "Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions," IET Electrical Systems in Transportation, vol. 6, (2) pp. 107-116, 2016. Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions AbstractAs the technology supporting electric vehicles (EVs) is rapidly progressing and the cost of EV components is reducing, EVs are becoming more feasible for use in Australia and in many countries around the world. However, the public perception of EVs and their perceived limitations result in a slow uptake of the technology, partially because of the uncertainty regarding the ability of an EV to meet the driving needs of the general population. Range anxiety is a particular concern with drivers having fear of being stranded by a depleted EV battery. This study explores means of reducing range anxiety by taking into account a variety of environmental and behavioural factors. By considering such factors and implementing it in conjunction with a recently proposed improved state of charge (SoC) estimation method by the authors, a range estimate can be produced that is much more accurate than the conventional methods which consider the SoC and vehicle efficiency alone. This range estimate can be used to inform the driver of the capabilities of the EV and advise if a recharge is required to reach the intended destination. authors, a range estimate can be produced that is much more accurate than the conventional methods which consider the SoC and vehicle efficiency alone. This range estimate can be used to inform the driver of the capabilities of the EV and advise if a recharge is required to reach the intended destination.

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Battery Modeling

2018

Advanced Battery Management Technologies for Electric Vehicles

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Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles

Cited by 430 publications

References 18 publications

Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

Driver alerting system using range estimation of electric vehicles in real time under dynamically varying environmental conditions

Battery Modeling

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