Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer. Issue 11 (4th October 2021)
- Record Type:
- Journal Article
- Title:
- Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer. Issue 11 (4th October 2021)
- Main Title:
- Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer
- Authors:
- Sakile, Rajakumar
Sinha, Umesh Kumar - Abstract:
- Abstract: Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium‐ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium‐ion battery. Here, a first‐order resistor‐capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test. Abstract : The lithium‐ion battery's state of charge and state of health are estimated accurately from the proposed adaptive nonlinear observer. The state equations are obtained from the first‐order resistor‐capacitor (RC) equivalent circuit model. Based on the state equations, the adaptiveAbstract: Estimation of accurate state of charge (SOC) and state of health (SOH) of lithium‐ion batteries has become more difficult in electric vehicles due to various uncertainties in the battery. The main objective of this paper is to estimate the accurate and robust SOC and SOH of the lithium‐ion battery. Here, a first‐order resistor‐capacitor (RC) electrical equivalent circuit model is considered for the analysis and modeling, an adaptive nonlinear observer (ANO) is proposed to convert nonlinear equations into linearized equations. The transfer function is obtained from the linearized equations to estimate the actual parameters and the internal states of the battery. When comparing the proposed ANO to the conventional method extended Kalman filter, the new ANO gives better dynamic results, less SOC error, and high convergence capability. An extra random variable noise is added to the system; the proposed model is suitable for the SOC at current noise factors. The convergence capability of the observer is analyzed with the linear criterion. The simulation results of the proposed ANO are validated through the MATLAB/Simulink platform under the hybrid pulse power characteristics test. Abstract : The lithium‐ion battery's state of charge and state of health are estimated accurately from the proposed adaptive nonlinear observer. The state equations are obtained from the first‐order resistor‐capacitor (RC) equivalent circuit model. Based on the state equations, the adaptive nonlinear observer determines the accurate state of charge and health of the lithium‐ion battery. … (more)
- Is Part Of:
- Advanced theory and simulations. Volume 4:Issue 11(2021)
- Journal:
- Advanced theory and simulations
- Issue:
- Volume 4:Issue 11(2021)
- Issue Display:
- Volume 4, Issue 11 (2021)
- Year:
- 2021
- Volume:
- 4
- Issue:
- 11
- Issue Sort Value:
- 2021-0004-0011-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2021-10-04
- Subjects:
- adaptive nonlinear observer -- electric vehicles -- state of charge -- state of health of lithium‐ion batteries
Science -- Simulation methods -- Periodicals
Science -- Methodology -- Periodicals
Engineering -- Simulation methods -- Periodicals
Engineering -- Methodology -- Periodicals
507.21 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/adts.202100258 ↗
- Languages:
- English
- ISSNs:
- 2513-0390
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0696.935575
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26821.xml