Electrochemical model of a lithium-ion battery implemented into an automotive battery management system

Sung, Woosuk; Shin, Chee Burm

doi:10.1016/j.compchemeng.2015.02.007

Cited by 43 publications

(30 citation statements)

References 20 publications

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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%

“…Then an electrochemical model is established to describe battery electrochemical behaviours by using particle swarm optimization (PSO) method to optimize critical model parameters. Sung and Shin [7] showed that the electrochemical model presented a highly accurate prediction performance but required significant computation effort in model simulation. Then a model implementation scheme was developed to embed electrochemical model into the BMS.…”

Section: Battery Electric Modelmentioning

confidence: 99%

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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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Section: Battery Electric Modelmentioning

confidence: 99%

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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“…In contrast to a traditional experimental or equivalent circuit model [5][6][7][8][9], a physics-based electrochemical model estimates the internal parameters of the battery. An advanced BMS based on a physics-justified electrochemical model is able to more precisely estimate the internal state of the battery and immeasurable parameters such as Li+ concentration and solid state diffusivity to better control and utilize the battery [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Lithium-Ion Anode Materials Using an Experimentally Verified Physics-Based Electrochemical Model

Fu¹,

Zhou

Fan

et al. 2017

Energies

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Abstract:Researchers are in search of parameters inside Li-ion batteries that can be utilized to control their external behavior. Physics-based electrochemical model could bridge the gap between Li+ transportation and distribution inside battery and battery performance outside. In this paper, two commercially available Li-ion anode materials: graphite and Lithium titanate (Li 4 Ti 5 O 12 or LTO) were selected and a physics-based electrochemical model was developed based on half-cell assembly and testing. It is found that LTO has a smaller diffusion coefficient (D s ) than graphite, which causes a larger overpotential, leading to a smaller capacity utilization and, correspondingly, a shorter duration of constant current charge or discharge. However, in large current applications, LTO performs better than graphite because its effective particle radius decreases with increasing current, leading to enhanced diffusion. In addition, LTO has a higher activation overpotential in its side reactions; its degradation rate is expected to be much smaller than graphite, indicating a longer life span.

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“…Vehicle sensor data is acquired and processed by the respective electronic control unit (ECU) and on-board-diagnostics (OBD) are performed for the sake of vehicle design validation and verification, to identify warranty relevant information and for the detection of system faults. Meeting the requirements for real-time processing, the ECU is an embedded system, which has very limited storage capabilities in an order of magnitude of kB to MB [1]. On the contrary, data loggers that allow for a recording and storage of sensor signals with a high frequency, are limited to the development phases, and therefore rarely represented in series vehicles [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Smart Data Selection and Reduction for Electric Vehicle Service Analytics

Schoch

Staudt

Setzer

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Battery electric vehicles (BEV) are increasingly used in mobility services such as car-sharing. A severe problem with BEV is battery degradation, leading to a reduction of the already very limited range of a BEV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parameterization and better predictive outcomes, in practical settings the amount of storage and transmission bandwidth is limited by technical and economical considerations. By means of a simulation-based analysis, dynamic user behavior is simulated based on real-world driving profiles parameterized by different driver characteristics and ambient conditions. We find that by using a shrinked subset of variables the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy.

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Electrochemical model of a lithium-ion battery implemented into an automotive battery management system

Cited by 43 publications

References 20 publications

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

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

Comparison of Lithium-Ion Anode Materials Using an Experimentally Verified Physics-Based Electrochemical Model

Smart Data Selection and Reduction for Electric Vehicle Service Analytics

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