Identification of load dependent cell voltage model parameters from sparse input data using the Mixed Integer Distributed Ant Colony Optimization solver

Lenz, Martin; Jöst, Dominik; Thiel, Fabian; Pischinger, Stefan; Sauer, Dirk Uwe

doi:10.1016/j.jpowsour.2019.226880

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…These variables are the output errors in the terminal voltage, prior SOC prediction, KF gain, and the estimated value of the terminal voltage. Furthermore, battery model parameters will change dynamically under the influence of the current change rate, which will affect the SOC estimation accuracy of UKF [27]. Thus, the influence of the current change rate should be considered.…”

Section: Novel Soc Estimation Methods Based On Bpnn‐ukfmentioning

confidence: 99%

Algorithm of BPNN‐UKF based on a fusion model for SOC estimation in lithium‐ion batteries

Lin

Yang

Zhou

et al. 2022

IET Power Electronics

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Lithium-ion batteries (LIBs) are widely used as energy storage devices. Accurately estimating the state of charge (SOC) is critical for the safe operation of LIBs, but the complex internal characteristics of LIBs make this a difficult task. In this study, an algorithm called BPNN-UKF, which combines the backpropagation neural network (BPNN) and unscented Kalman filtering (UKF), is proposed. First, a fusion model construction method incorporating AdaBoost and recursive least squares (RLS) is developed to enhance the accuracy and generalization of the battery model. Next, the fusion model is applied to create BPNN-UKF, which reduces the model dependence of UKF and the training requirements of BPNN. In this method, UKF estimates the SOC, and then BPNN learns the nonlinear relationship between SOC estimation error and process variables in UKF preliminary estimation. The BPNN output is used to update the state vector in real time during the new UKF estimation process. Experimental results reveal that the proposed method can drastically improve estimation accuracy compared with the UKF and the simple combined BPNN-UKF, and it exhibits considerable generalization to common disturbances in SOC estimation and various battery working conditions.

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Section: Novel Soc Estimation Methods Based On Bpnn‐ukfmentioning

confidence: 99%

Algorithm of BPNN‐UKF based on a fusion model for SOC estimation in lithium‐ion batteries

Lin

Yang

Zhou

et al. 2022

IET Power Electronics

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“…So, using the facts in this paragraph and considering the fact that i(t) is bounded by assumption, and recalling that x 2 , x 3 , x 2 , x 3 are bounded due to the asymptotic stability results derived above. Then from equations ( 2) -( 5) and ( 17) - (20), it is trivial to see that ẋj , ˙…”

Section: Mathematical Justificationmentioning

confidence: 99%

“…Here y is the output terminal voltge, W represents the states x 1 through x 4 and the discharge current, a represents all the parameters which enter the state and output equations ( 1)-( 6) non-linearly. The observer for estimating these parameters utilizes this exact same nonlinear structure in ( 16)-( 21), and adds an input u to the estimator equations ( 18)- (20). Further, as defined in [23], and as seen in [26] the input used is a Nussbaum function of Mittag-Leffler type.…”

Section: B Persistence Of Excitationmentioning

confidence: 99%

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

Mukhopadhyay

Usman

Rehman

2022

IEEE Open J. Control. Syst.

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This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables update of battery parameters during run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering an induction motor driven prototype electric vehicle (EV) traction system.

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“…However, another important dependency factor for ECM parameters that is generally overlooked is the type of current loads. Different current profiles have been used for ECM parameter estimation, such as the pulsed current test (different types of pulse design as in [36,37,40]), drive cycles [41,42] and constantcurrent charging and discharging [43][44][45]. However, few works have considered the effect of choosing different current profiles on the identified parameters and the model accuracy [41,42].…”

Section: Introductionmentioning

confidence: 99%

Finding a better fit for lithium ion batteries: A simple, novel, load dependent, modified equivalent circuit model and parameterization method

Hua

Zhang

Offer

2021

Journal of Power Sources

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Identification of load dependent cell voltage model parameters from sparse input data using the Mixed Integer Distributed Ant Colony Optimization solver

Cited by 11 publications

References 18 publications

Algorithm of BPNN‐UKF based on a fusion model for SOC estimation in lithium‐ion batteries

Algorithm of BPNN‐UKF based on a fusion model for SOC estimation in lithium‐ion batteries

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

Finding a better fit for lithium ion batteries: A simple, novel, load dependent, modified equivalent circuit model and parameterization method

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