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Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

机译：机器学习应用于电气化的车辆电池的充电状态和健康状态：最先进的

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Carlos Vidal; Pawel Malysz; Phillip Kollmeyer; Ali Emadi;
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1. State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries [J] . Pawel Malysz, Ran Gu, Jin Ye, Electrical Systems in Transportation, IET . 2016,第2期

机译：带有状态约束的充电状态和健康状态估算以及电流传感器偏置校正，用于电动动力总成汽车电池
2. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles [J] . Zahid Taimoor, Xu Kun, Li Weimin, Energy . 2018,第NOVa1期

机译：在多种行驶周期下使用高级机器学习算法估算电动汽车动力电池的充电状态
3. Comparison of the First Order and the Second Order Equivalent Circuit Model Applied in State of Charge Estimation for Battery Used in Electric Vehicles [J] . Guoliang Wu, Chunbo Zhu, C. C. Chan Journal of Asian Electric Vehicles . 2010,第1期

机译：电动汽车电池充电状态估算中一阶和二阶等效电路模型的比较
4. Study of machine learning algorithms to state of health estimation of iron phosphate lithium-ion battery used in fully electric vehicles [C] . Sender Rocha dos Santos, Juliana C. M. S. Aranha, Thiago Chiachio do Nascimento, SAE BRASIL International Mobility Technology Congress and Exhibition . 2018

机译：全电动汽车磷酸铁锂离子电池健康估算机器学习算法研究
5. Dual benefits of adding damper bars in PMSMs for electrified vehicles: Improved machine dynamics and simplified integrated charging. [D] . Lu, Xiaomin. 2014

机译：在电动汽车的PMSM中增加阻尼杆的双重好处：改善了机器的动力学性能并简化了集成充电。
6. Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks [O] . José Manuel Lozano Domínguez, Faroq Al-Tam, Tomás de J. Mateo Sanguino, 2020

机译：应用于智能人行横道车辆的机器学习技术分析
7. State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms [O] . Venkatesan Chandran, Chandrashekhar K. Patil, Alagar Karthick, 2021

机译：使用机器学习算法电动汽车锂离子电池的充电状态

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

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