Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles
Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/9/2144 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849312914878496768 |
|---|---|
| author | Hongzhao Li Hongsheng Jia Ping Xiao Haojie Jiang Yang Chen |
| author_facet | Hongzhao Li Hongsheng Jia Ping Xiao Haojie Jiang Yang Chen |
| author_sort | Hongzhao Li |
| collection | DOAJ |
| description | Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field. |
| format | Article |
| id | doaj-art-b4a64e8fe7cf478993398a3ed6bb1a82 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b4a64e8fe7cf478993398a3ed6bb1a822025-08-20T03:52:56ZengMDPI AGEnergies1996-10732025-04-01189214410.3390/en18092144Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected VehiclesHongzhao Li0Hongsheng Jia1Ping Xiao2Haojie Jiang3Yang Chen4School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaSchool of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaSchool of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaSchool of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, ChinaNational Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaAccurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field.https://www.mdpi.com/1996-1073/18/9/2144lithium-ion batteriesbattery management systemstate of chargebattery modelsdata-drivenstate estimation |
| spellingShingle | Hongzhao Li Hongsheng Jia Ping Xiao Haojie Jiang Yang Chen Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles Energies lithium-ion batteries battery management system state of charge battery models data-driven state estimation |
| title | Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles |
| title_full | Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles |
| title_fullStr | Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles |
| title_full_unstemmed | Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles |
| title_short | Research Progress on State of Charge Estimation Methods for Power Batteries in New Energy Intelligent Connected Vehicles |
| title_sort | research progress on state of charge estimation methods for power batteries in new energy intelligent connected vehicles |
| topic | lithium-ion batteries battery management system state of charge battery models data-driven state estimation |
| url | https://www.mdpi.com/1996-1073/18/9/2144 |
| work_keys_str_mv | AT hongzhaoli researchprogressonstateofchargeestimationmethodsforpowerbatteriesinnewenergyintelligentconnectedvehicles AT hongshengjia researchprogressonstateofchargeestimationmethodsforpowerbatteriesinnewenergyintelligentconnectedvehicles AT pingxiao researchprogressonstateofchargeestimationmethodsforpowerbatteriesinnewenergyintelligentconnectedvehicles AT haojiejiang researchprogressonstateofchargeestimationmethodsforpowerbatteriesinnewenergyintelligentconnectedvehicles AT yangchen researchprogressonstateofchargeestimationmethodsforpowerbatteriesinnewenergyintelligentconnectedvehicles |