Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling

Ragone, Marco; Yurkiv, Vitaliy; Ramasubramanian, Ajaykrishna; Kashir, Babak; Mashayek, Farzad

doi:10.1016/j.jpowsour.2020.229108

Cited by 104 publications

(29 citation statements)

References 41 publications

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“…e polarization voltage of the battery is the difficulty and key to estimate SOC [11,12]. e main factors influencing the polarization voltage value are the ambient temperature, working current, the charge and discharge state of the battery, and the degree of aging.…”

Section: Complexity Of Polarization Voltagementioning

confidence: 99%

“…According to equation (11), because of C AI (t, T) > C A2 (t, T), the terminal voltage corresponding to SOC 1 (t) of the unmodified capacity of SOC 1 (t) < SOC 2 (t) is lower and the correction strength of the estimation factor is larger. erefore, the estimated SOC value gradually deviates from the true value, showing a divergence trend.…”

Section: I(τ)dτ + W(t) (18)mentioning

confidence: 99%

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Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

Lu¹,

Wang²,

Wang³

et al. 2021

Advances in Multimedia

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The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments.

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Section: Complexity Of Polarization Voltagementioning

confidence: 99%

Section: I(τ)dτ + W(t) (18)mentioning

confidence: 99%

Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

Lu¹,

Wang²,

Wang³

et al. 2021

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…The data-driven method is based on a large amount of data; however, it does not require a preset model and only requires the construction of a "black box" model that infinitely approximates experimental data by learning existing experimental data. For example, artificial neural network 11,12 (ANN) and radial basis function neural network 13,14 (RBFNN) are used to train data such as a battery's current, voltage, and internal resistance to obtain its relationship with battery SOC, thus estimating battery SOC. Hannan et al 15 used a deep learning method in battery SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Wang

Meng

Chang

et al. 2021

Intl J of Energy Research

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Summary Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium‐ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF‐AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞‐CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium‐ion battery SOC. FF‐AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD‐CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF‐AGLS and H∞‐SVD‐CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

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“…The neural network (NN) method is suitable for all kinds of batteries. The battery is regarded as a black box, and the mapping data between input parameters and output parameters are extracted, and then it is determined by repeated trials during training 4,40‐42 . However, it requires a lot of data, and the estimation structure is greatly affected by the training data and methods.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in practical applications, the hardware requirements are extremely high due to the complexity of the algorithm. Support vector machine (SVM) algorithm is also a data‐driven approach, which has good adaptability to nonlinear problems 41,43,44 . However, similar to the NN method, the dependence on training data and the complexity of the algorithm are both high.…”

Section: Introductionmentioning

confidence: 99%

Improved gray wolf particle filtering and high‐fidelity second‐order autoregressive equivalent modeling for intelligent state of charge prediction of lithium‐ion batteries

et al. 2021

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Summary The rapid development of new energy vehicles puts forward higher requirements for the lithium‐ion batteries model construction, high‐efficiency condition monitoring and collaborative estimation. An improved high‐fidelity second‐order autoregressive model is proposed and constructed, and the autoregressive model is integrated with the second‐order equivalent circuit model, which can achieve an accurate and reliable description of the batteries internal dynamic change process. To achieve the accurate expression of the battery's external characteristics and internal state, the forgetting factor is combined with the recursive least square algorithm to improve the parameter identification accuracy and optimality while reducing the space complexity of the algorithm. A novel gray wolf particle filtering algorithm is proposed, which eliminates the particles severe degradation in traditional algorithms and enhances the ability of particles to resist degradation. The algorithm superiority and generalization are verified under complex working conditions. The experimental results show that the accuracy of the high‐fidelity second‐order autoregressive model can reach 99%, which can well simulate the complex chemical reaction process inside the lithium‐ion battery. Experimental simulation is performed under constant current conditions. Compared with the extended Kalman filter, unscented Kalman filter, and particle filter algorithms, the gray wolf particle filter algorithm has reduced the root mean square error by 3.39%, 0.90%, and 2.84%, and the mean absolute error has reduced by 1.95%, 0.51%, and 2.22%. Under dynamic stress test conditions, the root mean square error is reduced by 1.54%, 0.33%, and 0.78%, and the average absolute error is reduced by 1.4%, 0.22%, and 0.76%. In addition, when tested under different environmental conditions, although the improved algorithm has a relatively long running time, the estimation accuracy of the algorithm is greatly improved and the execution efficiency is high. The improved algorithm provides a theoretical basis for the reliability and stability of the onboard operation of lithium‐ion batteries. Highlights A novel lithium‐ion battery high‐fidelity second‐order autoregressive equivalent model is proposed. The gray wolf particle filter is employed to improve the observation accuracy. A recursive calculation method is derived by the time‐based parameters.

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Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling

Cited by 104 publications

References 41 publications

Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Improved gray wolf particle filtering and high‐fidelity second‐order autoregressive equivalent modeling for intelligent state of charge prediction of lithium‐ion batteries

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