Hybrid powertrain control with dynamic traffic prediction based on real-world V2X information

Deng, Junpeng; Adelberger, Daniel; Re, Luigi del

doi:10.23919/acc50511.2021.9482996

Cited by 2 publications

(2 citation statements)

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“…Another emerging scenario that has been addressed in the literature is the vehicle-to-everything (V2X) context, where the vehicle is connected to the surrounding environment and it is able to exchange information with any entity that can affect its performance and energy consumption. For example, Deng et al [29,30] have proposed a model-free approach and a multi-layer architecture to predict long-distance traffic velocity in real-world V2X considering the future dynamics of V2X information. The prediction has been based on the most similar past trajectories to the given route and this information has been exploited for forecasting up to 20 min; then, traffic velocity predicted has been adopted for an MPC control approach.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Deng et al. [29, 30] have proposed a model‐free approach and a multi‐layer architecture to predict long‐distance traffic velocity in real‐world V2X considering the future dynamics of V2X information. The prediction has been based on the most similar past trajectories to the given route and this information has been exploited for forecasting up to 20 min; then, traffic velocity predicted has been adopted for an MPC control approach.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of driver torque demand prediction methods

Cavanini

Ciabattoni

Ferracuti

et al. 2022

IET Intelligent Trans Sys

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The performances of energy management systems or electric vehicles and hybrid electric vehicles are highly dependent on the forecast of future driver torque/power request sequence that affects vehicle efficiency and economy. Since the behaviour of the driver is challenging to model/predict by first‐principles models, modern artificial intelligence algorithms would represent feasible methods for approaching this problem in real‐world automotive systems. This work provides a comparative study and analysis of performances of different data‐driven torque prediction strategies. The studied and compared torque demand prediction techniques are exponentially varying model, linear regression, shallow and deep neural networks, and least square support vector machine‐based approaches. The prediction performance and computational cost of these techniques are evaluated and reported, and the possibility of exploiting these techniques in real‐world scenarios is also discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%