Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries
The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely...
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Published in | Journal of power sources Vol. 253; pp. 412 - 418 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.05.2014
Elsevier |
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Abstract | The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely limited due to the need for a long rest time for full relaxation. In this paper, a rapid OCV prediction method is proposed to predict the final static OCV in a few minutes using linear regression techniques, based on a new mathematical model developed from an improvement on a second-order resistance–capacitance (RC) model. As the improvement, an important discovery is demonstrated by experimental investigation and data analysis: the relaxation time (i.e., time constant) of the diffusion circuit of the second-order RC model is not a fixed constant, unlike an intrinsic value for a given material, but an apparent linear function of the open-circuit time. This improvement enables the new model to track the actual relaxation process very well. The accuracy and the rapidity of the new model and proposed method are validated with working-condition experimental data on battery cells with different cathodes, and the results of OCV prediction are very accurate (errors below 1 mV in 20 min).
•Discover that diffusion time constant is a linear function of open-circuit time.•Established a new voltage relaxation model with good curve-fitting performance.•Presented a rapid, adaptive and online OCV prediction method.•Shorten the waiting time from traditional 20 h to 20 min with the new method. |
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AbstractList | The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely limited due to the need for a long rest time for full relaxation. In this paper, a rapid OCV prediction method is proposed to predict the final static OCV in a few minutes using linear regression techniques, based on a new mathematical model developed from an improvement on a second-order resistance–capacitance (RC) model. As the improvement, an important discovery is demonstrated by experimental investigation and data analysis: the relaxation time (i.e., time constant) of the diffusion circuit of the second-order RC model is not a fixed constant, unlike an intrinsic value for a given material, but an apparent linear function of the open-circuit time. This improvement enables the new model to track the actual relaxation process very well. The accuracy and the rapidity of the new model and proposed method are validated with working-condition experimental data on battery cells with different cathodes, and the results of OCV prediction are very accurate (errors below 1 mV in 20 min).
•Discover that diffusion time constant is a linear function of open-circuit time.•Established a new voltage relaxation model with good curve-fitting performance.•Presented a rapid, adaptive and online OCV prediction method.•Shorten the waiting time from traditional 20 h to 20 min with the new method. The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely limited due to the need for a long rest time for full relaxation. In this paper, a rapid OCV prediction method is proposed to predict the final static OCV in a few minutes using linear regression techniques, based on a new mathematical model developed from an improvement on a second-order resistance-capacitance (RC) model. As the improvement, an important discovery is demonstrated by experimental investigation and data analysis: the relaxation time (i.e., time constant) of the diffusion circuit of the second-order RC model is not a fixed constant, unlike an intrinsic value for a given material, but an apparent linear function of the open-circuit time. This improvement enables the new model to track the actual relaxation process very well. The accuracy and the rapidity of the new model and proposed method are validated with working-condition experimental data on battery cells with different cathodes, and the results of OCV prediction are very accurate (errors below 1 mV in 20 min). The open-circuit voltage (OCV) of a battery, as a crucial characteristic parameter, is widely used in many aspects of battery technology, such as electrode material mechanism analysis, battery performance/state estimation and working process management. However, the applications of OCV are severely limited due to the need for a long rest time for full relaxation. In this paper, a rapid OCV prediction method is proposed to predict the final static OCV in a few minutes using linear regression techniques, based on a new mathematical model developed from an improvement on a second-order resistance-capacitance (RC) model. As the improvement, an important discovery is demonstrated by experimental investigation and data analysis: the relaxation time (i.e., time constant) of the diffusion circuit of the second-order RC model is not a fixed constant, unlike an intrinsic value for a given material, but an apparent linear function of the open-circuit time. This improvement enables the new model to track the actual relaxation process very well. The accuracy and the rapidity of the new model and proposed method are validated with working-condition experimental data on battery cells with different cathodes, and the results of OCV prediction are very accurate (errors below 1 mV in 20 mm). |
Author | Lu, Rengui Wang, Tiansi Pei, Lei Zhu, Chunbo |
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Keywords | Lithium-ion battery Open-circuit voltage Relaxation model Time constant Rapid prediction Lithium ion batteries Relaxation Prediction Voltage Secondary cell Models Open circuit voltage |
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SubjectTerms | Applied sciences Direct energy conversion and energy accumulation Electric batteries Electric potential Electrical engineering. Electrical power engineering Electrical power engineering Electrochemical conversion: primary and secondary batteries, fuel cells Exact sciences and technology Glass transition temperature Lithium batteries Lithium-ion batteries Lithium-ion battery Mathematical analysis Mathematical models Open-circuit voltage Rapid prediction Relaxation model Relaxation time Time constant Voltage |
Title | Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries |
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