Methods for state-of-charge determination and their applications

Piller, S.; Perrin, Marion; Jossen, Andreas

doi:10.1016/s0378-7753(01)00560-2

Cited by 1,004 publications

(465 citation statements)

References 5 publications

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“…Several SOC models have been proposed in the literature (e.g., Piller, et al, 2001, Pop, et al, 2005, which can be grouped in the following categories: i) measurement of electrolyte specific gravity, ii) battery current time-integration, iii) battery impedance/resistance estimation, iv) measurement of the battery open circuit voltage, v) models that take into account the electrolyte temperature, discharge rate and other batteries parameters. Additionally, an accurate SOC estimation needs to take into account the battery environmental conditions, with particular reference to its temperature, as well as the battery behaviour at different discharge rates.…”

Section: Battery Soc Estimationmentioning

confidence: 99%

Design, Implementation and Testing of an Automatic Power Management System for Residential Stand-alone Microgrids with Hybrid Power Supply

Belvedere

Bianchi

Borghetti

et al. 2011

IFAC Proceedings Volumes

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Abstract:The paper presents an automatic power management system (PMS) designed for the on-line operation of an experimental low voltage microgrid equipped with two power supplies, a kW-class fuel cell (FC) and a photovoltaic (PV) module emulator, along with a 100-Ah battery storage system, all connected to a 230 VAC node. The connections of the various components to the common AC bus make use of power inverters with specific functionalities: the FC inverter controls the power production taking into account the FC limitations and requirements, the inverter of the PV emulator tracks its maximum power operating point, whilst, in islanded operating conditions, the bidirectional converter of the battery controls the voltage and frequency of the AC node. The AC node feeds electric active and reactive loads able to reproduce arbitrary and programmable profiles. The automatic PMS is implemented in a real time microcontroller and it is able to provide both the microgrid monitoring and supervisory control during grid-connected and islanded conditions and the FC power scheduling throughout various operating modes. The paper describes also the experimental investigation aimed at assessing the dynamic characteristics of the stand-alone microgrid equipped with the developed PMS.

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Section: Battery Soc Estimationmentioning

confidence: 99%

Design, Implementation and Testing of an Automatic Power Management System for Residential Stand-alone Microgrids with Hybrid Power Supply

Belvedere

Bianchi

Borghetti

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…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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“…An accurate estimation of the SOC is important to prolong the battery life and improve the performance of the electric vehicle [1,2]. However, because the battery is a strongly nonlinear and time-variable system, in practical applications it is hard to measure the SOC directly due to its complicated electrochemical processes and the influence of various factors [3].…”

Section: Introductionmentioning

confidence: 99%

“…Stateequation : x k`1 " A x x k`Bk u k`wk (1) Observationequation : y k " C k x k`Dk u k`vk (2) where u k is the input of the system, including the battery current, residual capacity, battery temperature, among other variables, and y k is the output of the system, which usually indicates the terminal voltage of the battery. The most difficult challenge of the KF method is determining the state equation and the observation equation.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

et al. 2016

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An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

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Methods for state-of-charge determination and their applications

Cited by 1,004 publications

References 5 publications

Design, Implementation and Testing of an Automatic Power Management System for Residential Stand-alone Microgrids with Hybrid Power Supply

Design, Implementation and Testing of an Automatic Power Management System for Residential Stand-alone Microgrids with Hybrid Power Supply

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

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

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