Sparse Approximation of LS-SVM for LPV-ARX Model Identification: Application to a Powertrain Subsystem

Cavanini, Luca; Ferracuti, Francesco; Longhi, Sauro; Marchegiani, Enrico; Monteriù, Andrea

doi:10.23919/aeitautomotive50086.2020.9307401

Cited by 7 publications

(1 citation statement)

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“…In this work, a comparative study of different strategies for torque prediction requested by the driver for optimal H/EVs energy management is presented. Exponentially varying torque predictor, linear regressor, shallow and deep NN and LS-SVMbased strategies have been considered [32,33]. The choice fell on the above techniques since they allow developing multipleinput and multiple-output (MIMO) models and direct models for accurate long-term prediction.…”

Section: Introductionmentioning

confidence: 99%

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%