State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility
This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/6/550 |
| _version_ | 1827739080679489536 |
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| author | Min Young Yoo Jung Heon Lee Joo-Ho Choi Jae Sung Huh Woosuk Sung |
| author_facet | Min Young Yoo Jung Heon Lee Joo-Ho Choi Jae Sung Huh Woosuk Sung |
| author_sort | Min Young Yoo |
| collection | DOAJ |
| description | This paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM’s flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes. |
| first_indexed | 2024-03-11T02:53:03Z |
| format | Article |
| id | doaj.art-a561dbb9f95d437e9ba4379fd9f61c98 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T02:53:03Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-a561dbb9f95d437e9ba4379fd9f61c982023-11-18T08:50:10ZengMDPI AGAerospace2226-43102023-06-0110655010.3390/aerospace10060550State-of-Charge Estimation of Batteries for Hybrid Urban Air MobilityMin Young Yoo0Jung Heon Lee1Joo-Ho Choi2Jae Sung Huh3Woosuk Sung4School of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Gyeonggi-do, Republic of KoreaDepartment of Smart Air Mobility, Korea Aerospace University, Goyang-si 10540, Gyeonggi-do, Republic of KoreaSchool of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Gyeonggi-do, Republic of KoreaAerospace Propulsion Division, Korea Aerospace Research Institute, Yuseong-gu, Daejeon 34133, Republic of KoreaSchool of Mechanical System and Automotive Engineering, Chosun University, Gwangju 61452, Republic of KoreaThis paper proposes a framework for accurately estimating the state-of-charge (SOC) and current sensor bias, with the aim of integrating it into urban air mobility (UAM) with hybrid propulsion. Considering the heightened safety concerns in an airborne environment, more reliable state estimation is required, particularly for the UAM that uses a battery as its primary power source. To ensure the suitability of the framework for the UAM, a two-pronged approach is taken. First, realistic test profiles, reflecting actual operational scenarios for the UAM, are used to model the battery and validate its state estimator. These profiles incorporate variations in battery power flow, namely, charge-depleting and charge-sustaining modes, during the different phases of the UAM’s flight, including take-off, cruise, and landing. Moreover, the current sensor bias is estimated and corrected concurrently with the SOC. An extended Kalman filter-based bias estimator is developed and experimentally validated using actual current measurements from a Hall sensor, which is prone to noise. With this correction, a SOC estimation error is consistently maintained at 2% or lower, even during transitions between operational modes.https://www.mdpi.com/2226-4310/10/6/550lithium-ion batteryurban air mobilitycharge-sustainingequivalent circuit modelextended kalman filterstate-of-charge (SOC) |
| spellingShingle | Min Young Yoo Jung Heon Lee Joo-Ho Choi Jae Sung Huh Woosuk Sung State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility Aerospace lithium-ion battery urban air mobility charge-sustaining equivalent circuit model extended kalman filter state-of-charge (SOC) |
| title | State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility |
| title_full | State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility |
| title_fullStr | State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility |
| title_full_unstemmed | State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility |
| title_short | State-of-Charge Estimation of Batteries for Hybrid Urban Air Mobility |
| title_sort | state of charge estimation of batteries for hybrid urban air mobility |
| topic | lithium-ion battery urban air mobility charge-sustaining equivalent circuit model extended kalman filter state-of-charge (SOC) |
| url | https://www.mdpi.com/2226-4310/10/6/550 |
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