Electrothermal Dynamics-Conscious Many-Objective Modular Design for Power-Split Plug-in Hybrid Electric Vehicles

Li, Ji; Liu, Kailong; Zhou, Quan; Meng, Jinhao; Ge, Yunshan; Xu, Hongming

doi:10.1109/tmech.2022.3156535

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…The model setup has been introduced in the previous sections, while here we present the method used to calculate energy consumption as the benchmark of our work based on the introduction in our previous work in [55]. The energy consumption of the studied EV is all generated from its battery pack.…”

Section: Energy Consumption Calculationmentioning

confidence: 99%

Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles Using Federated Learning

Yan,

Fang,

et al. 2024

IEEE Trans. Transp. Electrific.

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Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.

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Section: Energy Consumption Calculationmentioning

confidence: 99%

Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles Using Federated Learning

Yan,

Fang,

et al. 2024

IEEE Trans. Transp. Electrific.

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“…With the rapid development of artificial intelligence, machine learning and computational platforms [8], machine-learning-based methods have become a powerful solution to management issues in batteries [9,10]. Numerous machine-learning-based solutions have been developed to estimate batteries' internal states [11][12][13][14], forecast batteries' future ageing dynamics [15][16][17] and remaining useful life (RUL) [18,19], diagnose battery faults [20][21][22] and optimize battery charging [23][24][25][26] and energy management [27][28][29][30]. In summary, based on a well-developed machine learning method, an effective management solution can be obtained to improve the operational performance of batteries.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Dai,

Xiang,

Zhou

et al. 2024

WEVJ

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Solid-state batteries are currently developing into one of the most promising battery types for both the electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour. The performance of solid-state batteries is largely determined by the manufacturing process, particularly in the production of electrodes. However, efficiently analysing the effects of key manufacturing features and predicting the mass loading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effectively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key features during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machine-learning-based regression methods, including decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in the mixing and coating stages. The comparative results show that the developed machine-learning-based approach is able to provide a satisfactory prediction of the electrode mass loading of a solid-state battery with 0.995 R2 while successfully quantifying the importance of four key features in the early manufacturing stages. Due to the advantages of its data-driven nature, the developed machine-learning-based approach can efficiently assist engineers in monitoring/predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batteries for further energy storage applications.

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“…When the ambient temperature is low or high, the vehicle often has a heat demand for heating or cooling, and the heat demand can significantly reduce the pure electric driving range [1,2]. For the REEV, when the ambient tempera-ture is low, the waste heat from the engine can be used to meet the heat demand of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of Range Extender Electric Vehicle considering Temperature Effect under Different Heat Demands

Li³

2023

International Journal of Energy Research

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In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment.

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Electrothermal Dynamics-Conscious Many-Objective Modular Design for Power-Split Plug-in Hybrid Electric Vehicles

Cited by 11 publications

References 34 publications

Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles Using Federated Learning

Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles Using Federated Learning

A Machine-Learning-Based Approach to Analyse the Feature Importance and Predict the Electrode Mass Loading of a Solid-State Battery

Research on Energy Management Strategy of Range Extender Electric Vehicle considering Temperature Effect under Different Heat Demands

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