A Real-Time Markov Chain Driver Model for Tracked Vehicles and its Validation: Its Adaptability via Stochastic Dynamic Programming

Zou, Yuan; Kong, Zehui; Li, Teng; Liu, Dexing

doi:10.1109/tvt.2016.2605449

Cited by 40 publications

(22 citation statements)

References 30 publications

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“…For example, in [38], [69], [94] where a Markov chain was used to establish the future distribution of velocity and acceleration in order to predict the future velocity ((V,A)-V distribution). In addition, future vehicle power demand sequence was generated by Markov chain automatically, where input variables are current power demand [27], [69] (P-P distribution) or current power demand and velocity [51], [70] ( (P,V)-P distribution).…”

Section: Markov Based Methodsmentioning

confidence: 99%

“…Expand one-order Markov Model to High-order Markov Model [23], [67], [69] Integrating Route-based information into Prediction framework [10], [11], [33]- [35], [56]- [61], [71], [74], [75], [77]- [79], [87], [97], [98], [112], [113], [116] Self-learning/Adaptive Mechanism Adaptive/Self-learning Markov approaches [51], [52], [64], [72], [73] Moving window Approaches [93] Variable prediction horizon…”

Section: Reduce Future Uncertaintymentioning

confidence: 99%

“…Therefore, such model distortion caused by the discrepancy between original database and real-time changing environments becomes another prediction accuracy-affecting factor. However, if the original database or the prediction model can be updated by online updating algorithm[51], the prediction model can "learn" new characteristics from external environments. Consequently, the adaptation for prediction models will be improved. Prediction-assisting knowledge Three types of knowledge are utilized to explore their potential of accuracy improvements by researchers: a) Knowledge from historical database.…”

mentioning

confidence: 99%

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A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

202

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Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mispredictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

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Section: Markov Based Methodsmentioning

confidence: 99%

Section: Reduce Future Uncertaintymentioning

confidence: 99%

mentioning

confidence: 99%

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A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

202

View full text Add to dashboard Cite

show abstract

“…The biggest difference between a tracked vehicle and traditional vehicle is that a tracked vehicle is equipped with high-quality tracks [32]. Because the track is irregular and the absolute speed of each part when driving is different, it should be divided into four parts, including the upper, lower, front, and rear parts, when calculating.…”

Section: Equivalent Inertia Modeling Of Tracked Vehiclesmentioning

confidence: 99%

Research on the Modeling, Control, and Calibration Technology of a Tracked Vehicle Load Simulation Test Bench

Zhou

Yang

et al. 2019

Applied Sciences

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The load simulation test bench plays an important role in tracked vehicle development. The stability and accuracy of the system have a vital impact on the experimental results. To accurately reproduce the power performance of a tracked vehicle on the test platform, this paper aims to investigate the model, control, and calibration method of the test bench. Firstly, the dynamic model of a tracked vehicle under complex driving conditions is analyzed and established, which takes driving torque as the input and driving wheel speed as the output. Then, considering the uncertainties and disturbances in the system model, a 2-degree-of-freedom (2-DOF) control method combined with a disturbance observer is proposed to solve the stability problem of the system. Furthermore, in order to investigate the accuracy of the simulation on the test bed, a method of calibrating the system by a flywheel set with standard inertia is proposed. In the calibration process, the influence of the system resistance torque and the original mechanical inertia on the results is considered, and the response time of the inertia simulation is analyzed in both a steady and dynamic state. Finally, the load simulation test is carried out with the corrected system. The test results show that the system has a high load simulation accuracy under various load simulation tests.

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“…Some research focus on the energy management strategies of the hybrid electric powertrain to improve the fuel economy [18]. Algorithms such as Stochastic Dynamic Programming, Radau Pseudospectral Method and Reinforcement Learning are implemented for optimizing the energy management strategy [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Power Coupling System for Electric Tracked Vehicles during High-Speed Steering with Optimization-Based Torque Distribution Control

2018

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It is significant to improve the steering maneuverability of dual-motor drive tracked vehicles (2MDTVs), which have wide applications in the tracked vehicle industry. In this paper, we focus on the problem of insufficient propulsion motor power during high-speed steering. Some correction formulas are introduced to improve the accuracy of the mathematical model. A steering coupling system and an optimization-based torque distribution control strategy is adopted to improve the lateral stability of the vehicle. The 2MDTV model and the proposed control strategy are built in the multi-body software RecurDyn and the control software Matlab/Simulink, respectively. According to the real-time steering simulation by the hardware-in-the-loop (HIL) method, the 2MDTV with the coupling device outputs more power during high-speed steering. The results show the speed during steering is quite high though, the stability of the vehicle can be achieved due to using the torque distribution strategy, and the steering maneuverability of the vehicle is also improved.

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A Real-Time Markov Chain Driver Model for Tracked Vehicles and its Validation: Its Adaptability via Stochastic Dynamic Programming

Cited by 40 publications

References 30 publications

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Research on the Modeling, Control, and Calibration Technology of a Tracked Vehicle Load Simulation Test Bench

A Power Coupling System for Electric Tracked Vehicles during High-Speed Steering with Optimization-Based Torque Distribution Control

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