Hybrid Electric Vehicle Downshifting Strategy Based on Stochastic Dynamic Programming During Regenerative Braking Process

Liu, Binhao; Li, Liang; Wang, Xiangyu; Cheng, Shuo

doi:10.1109/tvt.2018.2815518

Cited by 87 publications

(24 citation statements)

References 33 publications

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“…Since the expressions for T demand and T motor have both been obtained, the engine torque T e can be calculated and a detailed expression is presented in the following equation: (see (16)) . Either equality or inequality constraints of each distance step are considered in this paper.…”

Section: First Stage: Pre-trip Soc and Speed Optimisationmentioning

confidence: 99%

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Zhang

Lim

et al. 2019

IET Electrical Systems in Transportation

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Hybrid electric vehicles (HEVs) provide a promising alternative to conventional engine-powered vehicles, with less emission and better fuel economy. This study proposes a hierarchical control for a power-split HEV over a driving cycle, featuring pre-trip optimisation and en-route speed adaption. Constraints including vehicle powertrain boundaries, road gradients, and speed limits, are taken into consideration. In the first stage, the HEV operating conditions, including the optimal vehicle SOC, speed profiles, and total driving time, are generated for the entire trip before departure. Based on the pre-trip results, the second stage adapts the vehicle speed for a short horizon when driving, while taking the safety spacing to the preceding vehicle into consideration, which acts as an indicator of actual traffic conditions and guarantees safe driving. Both optimisations are conducted under the distance domain for realising localisation in the optimal speed profile due to frequent changes in traffic conditions. An estimation of distribution algorithm is used to run the simulation so that the feasibility, robustness, and effectiveness of the proposed hierarchical control can be demonstrated.

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Section: First Stage: Pre-trip Soc and Speed Optimisationmentioning

confidence: 99%

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Zhang

Lim

et al. 2019

IET Electrical Systems in Transportation

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“…The mathematical equations of the mechanical model in Figure 5 are as follows: (6) where Mv is the passenger load; g is the acceleration of gravity; fr is the rolling resistance coefficient, related to the type and shape of the vehicle; a is the road inclination angle; Af is the area of the vehicle face; kair is the aerodynamic coefficient; V is the linear velocity of the trolleybus; me is the equivalent vehicle mass; δ is the vehicle mass increase resulting from the individual moments of inertia of all its rotating masses (in this case, the rotating masses are considered to be the wheels and the electric motor); Jw is the inertia of the wheels; Jm is the motor inertia; mw is the mass of the wheels; mm is the motor mass; rw is the radius of the wheels; Jew is the equivalent vehicle moment of inertia; me is the equivalent total vehicle mass; and Je,drive is the total vehicle (equivalent) inertia reduced to the motor During accelerations and constant speed vehicle courses, the energy flow is directed from the power supply to the machine, which operates as a motor.…”

Section: Mechanical Modeling Of the Trolleybusmentioning

confidence: 99%

“…Although regenerative braking energy recovery has been widely used in hybrid electric vehicles [2][3][4][5][6], as well as in some public means of electrical transportation, such as metros and trains [7,8], it has not been studied and incorporated in the case of trolleybuses, in any east-European country and other countries world-wide. The reason for this is some technical restrictions in the electric system of those vehicles, which are being discussed in this study.…”

Section: Introductionmentioning

confidence: 99%

Energy Saving Estimation of Athens Trolleybuses Considering Regenerative Braking and Improved Control Scheme

Apostolidou

Papanikolaou

2018

Resources

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Abstract:In this work, the electromechanical system of the 8000-series of Athens trolleybuses, based on data provided by OSY S.A., is analyzed. Those data were used to develop a valid model in order to estimate the total energy consumption of the vehicle under any possible operating conditions. In addition, an effort is made to estimate the energy saving potential if the wasted energy-in the form of heat-during braking or downhill courses is recovered (regenerative braking) and retrofitted during normal operation. This process requires the installation of appropriate electrical apparatus to recover and temporarily store this energy amount. Moreover, due to the fact that the main engine of the system is an asynchronous electric machine, its driving scheme is also of interest. This study assumes the current driving scheme, that is the direct vector control (DVC), and proposes an alternative control method, the direct torque control (DTC). Energy consumption/saving calculations highlight the effectiveness of incorporating regenerative braking infrastructure in trolleybuses transportation systems. Finally, a sustainable hybrid energy storage unit that supports regenerative braking is proposed.

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“…However, considering the intercity transportation demands with various driving patterns, the online instantaneous optimization method has attracted more and more attention. Stochastic optimal control algorithms are widely used to tackle such online problems and have been proved to be effective, such as stochastic dynamic programming (SDP) [16] and stochastic model predictive control (SMPC) [17,18]. With high robustness and flexibility, the conventional MPC methods are very prevalent in handling industrial and engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Control for Dual-Motor Battery Electric Bus Based on Signed Markov Chain Monte Carlo Method

et al. 2020

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With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control. INDEX TERMS Energy management strategy, dual-motor coupling powertrain, driving cycle recognition, signed Markov chain Monte Carlo method, stochastic model predictive control.

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Hybrid Electric Vehicle Downshifting Strategy Based on Stochastic Dynamic Programming During Regenerative Braking Process

Cited by 87 publications

References 33 publications

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Energy Saving Estimation of Athens Trolleybuses Considering Regenerative Braking and Improved Control Scheme

Stochastic Model Predictive Control for Dual-Motor Battery Electric Bus Based on Signed Markov Chain Monte Carlo Method

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