Vehicle Velocity Prediction and Energy Management Strategy Part 1: Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning

Liu, Kuan; Asher, Zachary D.; Gong, Xun; Huang, Mike; Kolmanovsky, Ilya

doi:10.4271/2019-01-1051

Cited by 71 publications

(44 citation statements)

References 11 publications

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“…However, the unwarranted accuracy of prediction is an important factor affecting the effect of the scheme. Enabled by recent advances of V2V and V2I communication, more information could be provided to predict the vehicle velocity more accurately which further optimises EMS to improve fuel economy and reduce emissions [106]. Literature [107] utilises the downstream road condition provided by ITS to replace MPC.…”

Section: Emss For Hev/phev Under Itsmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

174

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Section: Emss For Hev/phev Under Itsmentioning

confidence: 99%

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Yang

Zha

Wang

et al. 2020

IET Intelligent Transport Systems

174

View full text Add to dashboard Cite

“…It should be noted that this synthesis procedure is not directly applicable in most applications, because the required future power demand profile and vehicle velocity are usually not available. However, there is a good potential for application to vehicles driving on predetermined and fixed routes, such as buses and delivery vehicles typically equipped with GPS/GPRS-based tracking devices, where the upcoming power demand profiles can be effectively predicted based on historical data [30]. Also, this method could be applied to personal vehicles by using the data provided by the GPS-based navigation service, including an option of sharing the traffic data between the vehicles, similarly as it is done within the PHEV energy management approach described in [14].…”

Section: Scenario 3: Variable Road Grade and No Lez Presencementioning

confidence: 99%

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

2019

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The powertrain efficiency for plug-in hybrid electric vehicles (PHEV) can be maximized by gradually discharging the battery in a blended regime, where the engine is regularly used all over the driving cycle. A key step in designing an optimal PHEV control strategy for the blended regime corresponds to synthesis of battery state-of-charge (SoC) reference trajectory. The paper first demonstrates that the optimal SoC trajectory can significantly differ from a typical linear-like shape in the case of varying road grade and presence of low-emission zones (LEZ). Next, dynamic programming (DP)-based optimizations of PHEV control variables are conducted for the purpose of extracting and analyzing optimal SoC trajectory patterns. It is shown that the optimality is closely related to the minimization of SoC trajectory length with respect to travelled distance. This finding is used for SoC reference trajectory synthesis in the presence of LEZ and varying road grades. Finally, the overall PHEV control strategy is applied to a PHEV-type city bus and verified by means of computer simulations in comparison with the DP optimization benchmark.

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“…The Markov chain has been commonly adopted for shortterm speed prediction in the optimal control of vehicle powertrains [10], [17]- [19]. The Markov chain is a stochastic datadriven model that uses a state transition matrix and the current states to predict future states.…”

Section: Introductionmentioning

confidence: 99%

“…This RNN model structure allows effective estimation of the future state profiles through historical traffic information measured by various sensors. For example, Liu et al proposed RNN models for prediction of short-term velocity [19]. These models focus on prediction times over 1 to 10 seconds using numerous inputs measured by on-board sensors, radar, and V2I communication.…”

Section: Introductionmentioning

confidence: 99%

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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Vehicle Velocity Prediction and Energy Management Strategy Part 1: Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning

Cited by 71 publications

References 11 publications

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Efficient energy management strategy for hybrid electric vehicles/plug‐in hybrid electric vehicles: review and recent advances under intelligent transportation system

Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades

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

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