Design of a fuel economy oriented vehicle longitudinal speed controller with optimal gear sequence

Jing, Junbo; Ozatay, Engin; Kurt, Arda; Michelini, John; Filev, Dimitar; Özgüner, Ü.

doi:10.1109/cdc.2016.7798493

Cited by 13 publications

(1 citation statement)

References 9 publications

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“…14 Other MPC approaches try approximating fuel consumption of an internal combustion engine (ICE) using polynomials. [15][16][17] While these approaches deliver accurate approximations for conventional vehicles, they cannot be transfered easily to reflect the behavior of electric powertrains and especially hybrid powertrains. Other methods consider a more detailed system behavior including nonlinear characteristics of powertrain components, driving resistances, and powertrain states by using dynamic programming to calculate an optimized velocity profile.…”

Section: Introductionmentioning

confidence: 99%

Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios

Wegener

Plum

Andert

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this article, we analyze the interaction between powertrain technology, predictive driving functionalities, and inner-city traffic conditions. A model predictive velocity control algorithm is developed that utilizes dynamic traffic data as well as static route information to optimize the future trajectory of the considered ego-vehicle. This controller is then integrated into a state-of-the-art simulation environment for automated driving functionalities to calculate energy saving potentials for vehicles with a conventional gasoline engine powertrain and a P3-hybrid powertrain configuration as well as for a battery electric vehicle based on real driving measurements. The comparison of these powertrains under various traffic conditions shows that all three technologies profit from predictive driving functionalities. The determined reduction in energy demand ranges from 15% to more than 40%, but it is highly dependent on the boundary conditions and the selected powertrain technology. Specifically, it is shown that electrified powertrains can profit the most when the time-gap to the preceding vehicle is maintained at a high level. For a conventional powertrain, this effect is less pronounced and can be attributed to the efficiency characteristics of gasoline engines. It can be concluded that the development of advanced predictive driving functionalities requires microscopic simulation of inner-city traffic to achieve optimum results with regard to energy consumption.

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Section: Introductionmentioning

confidence: 99%

Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios

Wegener

Plum

Andert

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

et al. 2021

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Short-term prediction models for an ego-vehicle's speed contributes to the improvement of vehicle safety, driveability, and fuel economy. To achieve these desired outcomes, an accurate forward speed prediction model and its successful implementation in a real system is a prerequisite. This paper compares six velocity prediction models based on two types of data-driven models, a Markov chain and a Recurrent Neural Network (RNN), by implementing them in an embedded system to evaluate their prediction accuracy and execution time. The inputs to each model are the driving information acquired on a specific route, such as internal vehicle information, relative speed and distance to the vehicle in the front of the egovehicle, and ego-vehicle's location estimated by the GPS signal along with the B-spline roadway model. The proposed prediction models predict the velocity profile of the ego-vehicle up to the prediction horizon of 150 m. The parameters of the proposed models have been optimized using Hyper-parameter Optimization via Radial basis function and Dynamic coordinate search. By applying real driving data, the Markov chainbased models show slightly lower prediction accuracy but shorter execution time than those of the RNNbased models. INDEX TERMS Embedded system, execution time, gated recurrent unit (GRU), long short-term momory (LSTM), Markov chain, prediction accuracy, prediction algorithm, recurrent neural network (RNN)

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Ego-Vehicle Speed Prediction with Walk-Ahead

Matesanz

Tempelmeier

Nolting

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Design of a fuel economy oriented vehicle longitudinal speed controller with optimal gear sequence

Cited by 13 publications

References 9 publications

Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios

Energy saving potentials of modern powertrains utilizing predictive driving algorithms in different traffic scenarios

Comparative Study of Markov Chain With Recurrent Neural Network for Short Term Velocity Prediction Implemented on an Embedded System

Ego-Vehicle Speed Prediction with Walk-Ahead

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