Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles
Abstract Battery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuse...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-02-01
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| Series: | ChemElectroChem |
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| Online Access: | https://doi.org/10.1002/celc.202300497 |
| _version_ | 1797334595203497984 |
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| author | Assist. Prof. Yeonsoo Kim |
| author_facet | Assist. Prof. Yeonsoo Kim |
| author_sort | Assist. Prof. Yeonsoo Kim |
| collection | DOAJ |
| description | Abstract Battery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuses on optimal controllers for charging, thermal control, and cell balancing of electric vehicles. A potential approach for practical applications is the direct optimal control method, particularly model predictive control (MPC). The objective function, prediction model types, and manipulated variables are summarized, along with the computational performance. Typical nonlinear MPC, linear MPC, explicit MPC, and hierarchical MPC are the main formulations for the optimal control of EVs. The AI‐based approach learns the optimal control law as a function from the optimal control result data. Although few studies have applied the reinforcement approach to battery systems, additional safety considerations for constraints must be considered for real applications. Cell variations, aging factors, and uncertainty considerations have been analyzed for improving the controller design. Addressing the computational issue with a reliable optimizer is critical to the implementation of an optimal controller for EVs. |
| first_indexed | 2024-03-08T08:23:21Z |
| format | Article |
| id | doaj.art-46a22d24903f49db9b78370d049c5db3 |
| institution | Directory Open Access Journal |
| issn | 2196-0216 |
| language | English |
| last_indexed | 2024-03-08T08:23:21Z |
| publishDate | 2024-02-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | ChemElectroChem |
| spelling | doaj.art-46a22d24903f49db9b78370d049c5db32024-02-02T05:12:53ZengWiley-VCHChemElectroChem2196-02162024-02-01113n/an/a10.1002/celc.202300497Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric VehiclesAssist. Prof. Yeonsoo Kim0Department of Chemical Engineering Kwangwoon University 20 Kwangwoon-ro, Nowon-gu Seoul 01897 Republic of KoreaAbstract Battery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuses on optimal controllers for charging, thermal control, and cell balancing of electric vehicles. A potential approach for practical applications is the direct optimal control method, particularly model predictive control (MPC). The objective function, prediction model types, and manipulated variables are summarized, along with the computational performance. Typical nonlinear MPC, linear MPC, explicit MPC, and hierarchical MPC are the main formulations for the optimal control of EVs. The AI‐based approach learns the optimal control law as a function from the optimal control result data. Although few studies have applied the reinforcement approach to battery systems, additional safety considerations for constraints must be considered for real applications. Cell variations, aging factors, and uncertainty considerations have been analyzed for improving the controller design. Addressing the computational issue with a reliable optimizer is critical to the implementation of an optimal controller for EVs.https://doi.org/10.1002/celc.202300497battery managementcell balancingmodel predictive controloptimal chargingthermal management |
| spellingShingle | Assist. Prof. Yeonsoo Kim Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles ChemElectroChem battery management cell balancing model predictive control optimal charging thermal management |
| title | Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles |
| title_full | Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles |
| title_fullStr | Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles |
| title_full_unstemmed | Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles |
| title_short | Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles |
| title_sort | critical review of optimal control methods for li ion batteries in electric vehicles |
| topic | battery management cell balancing model predictive control optimal charging thermal management |
| url | https://doi.org/10.1002/celc.202300497 |
| work_keys_str_mv | AT assistprofyeonsookim criticalreviewofoptimalcontrolmethodsforliionbatteriesinelectricvehicles |