A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control With Road Grade Preview

Zeng, Xiangrui; Wang, Junmin

doi:10.1109/tcst.2015.2409235

Cited by 224 publications

(91 citation statements)

References 18 publications

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“…Furthermore, the vehicle speed shows a declining behavior (Figure 15g). On the other hand, when the control strategy is applied, the MG2 torque decreases during mode shift (Figure 16c), and the MG2 torque is determined by Equation (33). It is seen that the vehicle speed increases steadily (Figure 16g).…”

Section: Simulation Results and Discussionmentioning

confidence: 97%

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Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Huang

Chen

et al. 2017

Energies

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Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor-generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor-generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ.

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Section: Simulation Results and Discussionmentioning

confidence: 97%

“…The battery pack, as an additional power source, is an essential energy storage unit of the hybrid HDV, and the power to or from the battery pack is related to the power required by the MG and the other accessories [32,33]. In addition, the battery pack can be charged and discharged in real time, based on the vehicle's driving conditions.…”

Section: Battery Modelmentioning

confidence: 99%

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Huang

Chen

et al. 2017

Energies

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“…In (22), u m,0|k−1 indicates whether the subsystem is currently on or off and σ(x m,0|k ) is a (state-dependent) penalty parameter. This penalty parameter is used to achieve a desired switching frequency.…”

Section: Modified Lagrange Dual Function For On/off Controlmentioning

confidence: 99%

“…This penalty parameter is used to achieve a desired switching frequency. The optimization problem (22) can now be solved by solving the two LCQPs (22b) and (22c) and the optimal decision at the first time instant (n = 0) is given by:…”

Section: Modified Lagrange Dual Function For On/off Controlmentioning

confidence: 99%

“…The MPC strategies are interesting as they naturally take prediction information into account. The prediction information can be stochastic as in [21,22] or deterministic as in [18][19][20][23][24][25]. The future power demand for the deterministic MPC strategies can be predicted as a function of the current power demand, e.g., the power demand is exponentially decaying in [23,24] and held constant in [25].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

et al. 2017

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Abstract:In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

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Power balance control based on full state feedback for a fuel‐cell hybrid electric vehicle

Song

2020

Asian Journal of Control

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A fuel‐cell vehicle (FCV) mainly depends on the fuel‐cell system (FCS) to provide the power to drive the vehicle. A battery is also integrated as the second power source, which has the advantage of high density and quick dynamic response. Since two power sources co‐exist in the vehicle, the control balance of how to distribute the transient power requirement between the FCS and the battery has a great impact on the performance of the FCV. In this paper, the dynamic model of FCS, the battery, the electric motor (EM), and its control unit are first established. Then the parameters in these models are determined by using the least squares method to achieve an analytical description of the power‐train system. According to the FCV model, the power balance control based on the best state feedback is realized. This optimized control is verified over three different typical driving cycles through simulation and a bench experiment. The results show that the balance control has a practical application for FCV in improving the fuel economy and the dynamic performance.

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A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control With Road Grade Preview

Cited by 224 publications

References 18 publications

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Power balance control based on full state feedback for a fuel‐cell hybrid electric vehicle

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