Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

Zheng, Fangdan; Xing, Yinjiao; Jiang, Jiuchun; Sun, Bingxiang; Kim, Jong-Hoon; Pecht, Michael

doi:10.1016/j.apenergy.2016.09.010

Cited by 423 publications

(161 citation statements)

References 49 publications

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“…It would result in a large error in this case. Consequently, most of the literatures use only the 20%-80% of SOC information [9,40]. It can be seen from Figure 8(b) that LPSO and ARDPSO have relatively small errors as the value of SOC is in between 20% and 80%.…”

Section: Effectiveness Verificationmentioning

confidence: 99%

Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Huang

Guo

et al. 2018

Mathematical Problems in Engineering

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The performance behavior of the lithium-ion battery can be simulated by the battery model and thus applied to a variety of practical situations. Although the particle swarm optimization (PSO) algorithm has been used for the battery model development, it is usually unable to find an optimal solution during the iteration process. To resolve this problem, an adaptive random disturbance PSO algorithm is proposed. The optimal solution can be updated continuously by obtaining a new random location around the particle's historical optimal location. There are two conditions considered to perform the model process. Initially, the test operating condition is used to validate the model effectiveness. Secondly, the verification operating condition is used to validate the model generality. The performance results show that the proposed model can achieve higher precision in the lithium-ion battery behavior, and it is feasible for wide applications in industry.

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Section: Effectiveness Verificationmentioning

confidence: 99%

Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Huang

Guo

et al. 2018

Mathematical Problems in Engineering

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“…It is important to set a proper value of R k and Q k to let the algorithm work properly. In the present case, the model input and output are presented in Equations (26) and (27):…”

Section: Extended Kalman Filter Algorithmmentioning

confidence: 99%

“…The combined use of these two devices allows for applying different user defined charging/discharging profiles to meet the need to study the battery behaviour in specific working conditions. Battery testing system manufacturers provide equipment with integrated multi-channel charging/discharging units, managed with dedicated proprietary software [27].…”

Section: Testing Equipmentmentioning

confidence: 99%

Battery Modelling and Simulation Using a Programmable Testing Equipment

2018

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Abstract:In this paper, the study and modelling of a lithium-ion battery cell is presented. To test the considered cell, a battery testing system was built using two programmable power units: an electronic load and a power supply. To communicate with them, a software/hardware interface was implemented within the National Instruments (NI) LabVIEW environment. This dedicated laboratory equipment can be used to apply charging/discharging cycles according to user defined load profiles. The battery modelling and the parameters identification procedure are described. The model was used to estimate the State Of Charge (SOC) under dynamic loading conditions. The most spread techniques used in the field of battery modelling and SOC estimation are implemented and compared.

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“…UKF is an updated version of EKF that applies a discrete-time filtering algorithm and unscented transform to solve filtering problems. UKF based on unscented transformation was proposed to avoid the weakness of Taylor series expansion compared to EKF [45,92,101]. The UKF approach for SOC estimation is shown in Table 6.…”

Section: (5) Unscented Kalman Filteringmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

Cited by 423 publications

References 49 publications

Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Development of Accurate Lithium-Ion Battery Model Based on Adaptive Random Disturbance PSO Algorithm

Battery Modelling and Simulation Using a Programmable Testing Equipment

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

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