Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles

He, Haibo; Xiong, Rui; Guo, Hongwei

doi:10.1016/j.apenergy.2011.08.005

Cited by 331 publications

(119 citation statements)

References 17 publications

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“…Thus the learned SOC from the batch gradient descent is extrapolated to predict the SOC after time, t. From (14) …”

Section: B Prediction Of Remaining Time Of Operation Of Batterymentioning

confidence: 99%

“…Accurate definition of R and Q is necessary as incorrect values may make the estimation divergent. The correlation of the error sequences between model output and actual output for a fixed duration is used to tune the values of R and Q [14]. The "predict" and "correct" formulation of the 2 state EKF is further implemented [11].…”

Section: B Coulomb Counting and Soc-ocv Tablementioning

confidence: 99%

See 1 more Smart Citation

Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

Babu

Kumar

Roychowdhury

2013

Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering

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Abstract-Active energy management for power optimization is essential for successful commercialization of battery powered electric vehicles. In this paper we propose and implement an energy aware electric vehicle using an efficient battery management system, EffBMS which estimates the remaining charge of the Li-ion battery online, learns the rate of change of SOC and accordingly modulates the velocity and acceleration of the vehicle for optimal energy usage. An Extended Kalman Filter (EKF) was used to estimate the remaining charge of the Li-ion battery pack. An online dual time frame gradient descent algorithm with higher time frame for updating dataset and lower time frame for parameter tuning learns the SOC curve and employs a set point controller for energy management in motion. The entire model was developed in a SPARTAN 6 LX-45 reconfigurable FPGA board and is tested in a laboratory model of differentially driven single geared electric car. Case study of an upward slope hill motion is performed and results shows 10-12% energy savings.(Abstract)

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“…Thus the learned SOC from the batch gradient descent is extrapolated to predict the SOC after time, t. From (14) …”

Section: B Prediction Of Remaining Time Of Operation Of Batterymentioning

confidence: 99%

Section: B Coulomb Counting and Soc-ocv Tablementioning

confidence: 99%

Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

Babu

Kumar

Roychowdhury

2013

Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering

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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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“…In addition, the results of these off-line identifications are always taken as reference values for on-line methods or training data for battery modelling. For the on-line methods, the resistances are always estimated on the basis of equivalent circuit models (ECMs) [5][6][7][8][9][10] or electrochemical models [11][12][13] with the utilization of the recursive optimal estimation algorithms, such as the Kalman-filter-based algorithms and the least-squares-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Wang¹,

Pei²,

Wang

et al. 2015

Energies

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Abstract:A state-of-health (SOH) estimation method for electric vehicles (EVs) is presented with three main advantages: (1) it provides joint estimation of cell's aging states in terms of power and energy (i.e., SOHP and SOHE)-because the determination of SOHP and SOHE can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2) it has wide applicability to various ambient temperatures-to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3) it needs low computational efforts for on-board application-based on a linear equation of cell's dynamic behaviors, the recursive least-squares (RLS) algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

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Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles

Cited by 331 publications

References 17 publications

Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

Energy Aware Battery Powered Electric Vehicles:A Predictive Model Driven Approach

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

On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

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