Research on optimal power allocation strategy based on power demand prediction for electro-mechanical transmission

Zhao, Yongsheng; Wang, Weida; Chen, Xiang; Huang, Kun

doi:10.1109/chicc.2016.7554735

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Shi et al [20] have used a one-step Markov chain for an MPC model with a time horizon of 1 s. Positive driving torque has been discretized between the maximum and minimum values to create a transition probability map based on two real drive cycles and CTBCDC (China Transit Bus City Driving Cycle) profile. In [21], a fixed gain algorithm method has been developed for an online, multi-step and real-time prediction for the demanding power of an electro-mechanical transmission based on an autoregressive model with external inputs. Both desired power demand and actual power demand of the vehicle have been used as input.…”

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%