Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

Deng, Zhongwei; Yang, Lin; Cai, Yunze; Deng, Hao; Sun, Luyan

doi:10.1016/j.energy.2016.06.130

Cited by 133 publications

(40 citation statements)

References 27 publications

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“…This kind of SOC estimation often has certain limitations. For example, the mean error in the method of [18] can be as high as 1.64%, the maximum error in a recent method of [19] is at least 0.46%, and these errors could exceed 2% under certain driving cycles [18][19][20]. For this reason, it is advised to take the average value of the above SOC range, i.e., 37% as the best SOC value.…”

Section: Case Studymentioning

confidence: 99%

Optimal State-of-Charge Value for Charge-Sustaining Mode of Plug-In Hybrid Electric Vehicles

Zhang

2020

IEEE Access

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For plug-in hybrid electric vehicles or extended range electric vehicles, battery stored energy often cannot fully meet the travel needs, and the battery needs to stay in a charge-sustaining mode to allow for backup sources such as gasoline or diesel to power the vehicle. It is crucial to identify the optimal stateof-charge (SOC) value for the battery to maintain this charge-sustaining mode since this SOC has significant impact to battery degradation. In existing studies, Just-in-Time control proposes that this SOC should be maintained at 55% but without theoretical justification. With the help of a battery degradation model based on solid electrolyte interphase growth, this paper develops a method to decide the optimal SOC value for charge-sustaining mode. Following the principle of superposition, degradation during the battery discharging process is divided as a fixed degradation caused by the drop of SOC from maximum to minimum values during charge-depleting mode, and a dynamic degradation caused by the oscillation at the charge-sustaining SOC value. Then this oscillation-caused degradation is further modeled and minimized through the investigation of the side reaction current density. The optimal SOC value obtained will be the SOC at which the side reaction current density has the slowest changing rate. This SOC value indeed relies on battery parameters, charging/discharging current and ambient temperature, and a case study shows that the best range of SOC value is 36%~38% for a 1.8Ah SONY 18650 cell. An average SOC of 37% is therefore recommended for charge-sustaining mode considering the possible errors in SOC estimation. INDEX TERMS Plug-in hybrid electric vehicle, state-of-charge, charge-sustaining, solid electrolyte interphase

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Section: Case Studymentioning

confidence: 99%

Optimal State-of-Charge Value for Charge-Sustaining Mode of Plug-In Hybrid Electric Vehicles

Zhang

2020

IEEE Access

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“…Smith et al [12] and Wei et al [13]used partial differential equation to establish electrochemical model to complete SOC estimation of LIBs. Deng Z et al [14] applied the dual adaptive extended Kalman filter algorithms based ECM to estimate SOC; Wang et al [15] employed PF model to estimate SOC, which can eliminate the uncertainty of noise, but ignored the influence of temperature on SOC estimation. Among several models, the ECM is considered to be the most suitable model for SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

Min

Wei

et al. 2020

IEEE Access

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Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation，this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model. INDEX TERMS Lithium-ion batteries(LIBs); electric vehicles(EVs); state-of-charge (SOC); sliding window method; least-squares-support-vector-machine (LSSVM); grey wolf optimizer(GWO)

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“…Battery electric models mainly include electrochemical model [6][7][8][9], reduced-order model [10][11][12][13], equivalent circuit model [14][15][16][17] and data-driven model [18][19][20]. For electrochemical model, Rahman et al [6] claimed that the battery electrochemical model should have the abilities to capture the spatiotemporal dynamics of battery concentration, the electrode potential for each phase and the BulterVolmer kinetic to control intercalation reaction.…”

Section: Battery Electric Modelmentioning

confidence: 99%

“…Data-driven models try to capture the relation between input and output signals of batteries. Various datadriven models such as neural networks [18,19] and support vector machine (SVM) [20] have been adopted to describe battery electric behaviours without the prior knowledge. The performance of battery data-driven model is highly dependent on the test data as well as training approaches.…”

Section: Battery Electric Modelmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

Cited by 133 publications

References 27 publications

Optimal State-of-Charge Value for Charge-Sustaining Mode of Plug-In Hybrid Electric Vehicles

Optimal State-of-Charge Value for Charge-Sustaining Mode of Plug-In Hybrid Electric Vehicles

A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

A brief review on key technologies in the battery management system of electric vehicles

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