Reduced-Coupling Coestimation of SOC and SOH for Lithium-Ion Batteries Based on Convex Optimization
| Autor: | Ali Emadi, Sheng Liu, Shaoyi Yuan, Ryan Ahmed, Saeid Habibi, Gaoliang Fang, Dianxun Xiao |
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| Rok vydání: | 2020 |
| Předmět: |
Battery (electricity)
State variable Observer (quantum physics) Computer science 020208 electrical & electronic engineering Estimator Lithium polymer battery chemistry.chemical_element 02 engineering and technology Decoupling (cosmology) Convexity Nonlinear system chemistry Control theory Convex optimization 0202 electrical engineering electronic engineering information engineering Lithium Electrical and Electronic Engineering Decoupling (electronics) |
| Zdroj: | IEEE Transactions on Power Electronics. 35:12332-12346 |
| ISSN: | 1941-0107 0885-8993 |
| DOI: | 10.1109/tpel.2020.2984248 |
| Popis: | Model-based state-of-charge (SOC) and state-of-health (SOH) estimation for lithium-ion batteries has been widely applied in electrified vehicles, while the SOC and SOH estimators are highly coupled and nonlinear in conventional techniques. This leads to a bulky design of observer network and complicates the stability analyses. In this article, a new reduced-decoupling SOC and SOH coestimation algorithm based on convex optimization is proposed. This scheme estimates the battery SOC from the battery model and does not require the classic Coulomb-counting method. Therefore, it can decouple the capacity estimation from the SOC estimator and reduce the strong interaction existing in conventional coestimation methods. Besides, all state variables can be solved together by one estimator, which is straightforward and avoids the complicated observer network. Owing to the decoupling design, the stability of the proposed method becomes more intuitive and can be always guaranteed according to the convexity analysis without using other stabilizing approaches. In consequence, a weak-interaction and robust coestimation algorithm of SOC and SOH can be realized by the proposed technique. The experiments on a 5.4-Ah lithium polymer battery are implemented to validate the feasibility of the algorithm. |
| Databáze: | OpenAIRE |
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