Power prediction from a battery state estimator that incorporates diffusion resistance

Wang, Shuoqin; Verbrugge, Mark W.; Wang, John S.; Liu, Ping

doi:10.1016/j.jpowsour.2012.04.070

Cited by 82 publications

(36 citation statements)

References 25 publications

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“…For the bulk and SEI resistance (i.e., Rbulk and RSEI), the resistance increment is a near linear function of temperature change; however, for the charge transfer resistance (i.e., Rct), the resistance value is inversely proportional to the electrochemical reaction rate and the reaction rate follows an Arrhenius dependence with temperature (T) [30]. Therefore, the correlation between the ohmic resistance change (ΔRo,t) and T can be deduced as Equation (10). Wherein, the subscript "t" indicates the change caused by ambient temperatures; the coefficients κ1-κ4 will be determined by experimental data; and Tstd indicates the nominal temperature which is set to 30 °C in this paper:…”

Section: The Effects Of Temperature On Internal Resistancesmentioning

confidence: 99%

“…The procedure is listed as follows: (1) on-line ohmic resistance identification: through the measurements of battery voltage and current, the ohmic resistance values can be identified on line using the RLS algorithm; (2) SOHP estimation: according to the measured ambient temperature, the ohmic resistance increment caused by various temperatures can be calculated by Equation (10), and then this increment is subtracted from the identified resistance, which is a resistance normalization process, so the normalized resistance can be used to reflect battery aging levels and estimate SOHP by Equation (2); (3) SOHE estimation: the capacity loss can be obtained by substitution of the normalized Ro increment into Equation (9), and then the SOHE can be calculated according to Equation (1).…”

Section: State Of Health (Soh) Estimation Proceduresmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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Section: The Effects Of Temperature On Internal Resistancesmentioning

confidence: 99%

Section: State Of Health (Soh) Estimation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…As for battery models, (semi-)empirical models based on equivalent circuit [1][2][3][4][5][6][7] and electrochemical models [8,9] are widely applied. Despite potential benefits from informative states and/or parameters of the electrochemical models, its implementation into the computationally light BMS is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Despite potential benefits from informative states and/or parameters of the electrochemical models, its implementation into the computationally light BMS is difficult. As for a state and/or parameter estimator, two different estimators are mainly used, such as the Kalman filter [1,2] and the least mean squares (LMS) filter [3][4][5][6][7]. With the respect of computational efficiency in BMS, the LMS filter is significantly effective to the Kalman filter due to the absence of complex matrix calculations such as inversions.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of LIB Degradation using Estimating Cell Resistance for Hybrid Electric Vehicles

Park¹,

Sung²,

Lee³

2016

Journal of Electrical Engineering and Technology

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-To get computationally efficient algorithm which can diagnose the cell degradation by estimating the cell resistance in a battery management system (BMS), the ordinary least squares estimator (LMS filter) is employed to identify the parameter in the simple resistance model in the form of a first-order polynomial. As a result, the determined parameter estimate, the slope in the model, can be adapted to changes in the battery resistance. The devised algorithm is validated by using the cells with different state-of-health. These cells are aged at the elevated temperature over 24 weeks and tested at every four weeks to monitor changes in the cell resistance. Consequently, the devised algorithm can distinguish between the cell resistances which increase by 2 to 4% at every month.

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Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

Xiao,

Shi,

et al. 2024

Energy Storage

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Lithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends.

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Power prediction from a battery state estimator that incorporates diffusion resistance

Cited by 82 publications

References 25 publications

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

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

Diagnosis of LIB Degradation using Estimating Cell Resistance for Hybrid Electric Vehicles

Machine Learning Applied to Lithium‐Ion Battery State Estimation for Electric Vehicles: Method Theoretical, Technological Status, and Future Development

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