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Original Article
Rui XIONG
Beijing institute of technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4608-7597
License:
This work is licensed under a CC BY 4.0 License. Read Full License
State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.
Keywords
Electric vehicle, lithium ion battery, fractional order model, state of charge
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CURRENT STATUS: Published
View the published article at Chinese Journal of Mechanical Engineering.
Version 2
Posted 25 Jun, 2020
No community comments so far
Editorial decision: Accept
On 28 Jun, 2020
Editor assigned
On 25 Jun, 2020
Submission checks complete
On 24 Jun, 2020
Editor invited
On 24 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 17 Jun, 2020
Review #2 received
Received 13 Jun, 2020
Reviewer #3 agreed
On 10 Jun, 2020
Review #1 received
Received 17 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Reviewers invited
Invitations sent on 24 Apr, 2020
Reviewer #1 agreed
On 24 Apr, 2020
Editor assigned
On 19 Apr, 2020
Submission checks complete
On 18 Apr, 2020
Editor invited
On 18 Apr, 2020
First submitted
On 17 Apr, 2020
Metrics
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Subject Areas
Mechanical Engineering
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A new preprint design is coming!
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See the published version of this preprint at Chinese Journal of Mechanical Engineering.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original Article
Rui XIONG
Beijing institute of technology
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4608-7597
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Chinese Journal of Mechanical Engineering.
Version 2
Posted 25 Jun, 2020
No community comments so far
Editorial decision: Accept
On 28 Jun, 2020
Editor assigned
On 25 Jun, 2020
Submission checks complete
On 24 Jun, 2020
Editor invited
On 24 Jun, 2020
No comments provided
Editorial decision: Minor revision
On 17 Jun, 2020
Review #2 received
Received 13 Jun, 2020
Reviewer #3 agreed
On 10 Jun, 2020
Review #1 received
Received 17 May, 2020
Reviewer #2 agreed
On 27 Apr, 2020
Reviewers invited
Invitations sent on 24 Apr, 2020
Reviewer #1 agreed
On 24 Apr, 2020
Editor assigned
On 19 Apr, 2020
Submission checks complete
On 18 Apr, 2020
Editor invited
On 18 Apr, 2020
First submitted
On 17 Apr, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Mechanical Engineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Chinese Journal of Mechanical Engineering.
State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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Figure 14
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Figure 16
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