scholarly journals Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

2019 ◽  
Author(s):  
Neeraj Rama ◽  
Huanqing Wang ◽  
Joshua Orlando ◽  
Darrell Robinette ◽  
Bo Chen
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Multi Mode

Hybrid optimization and its applications for multi-mode plug-in hybrid electric vehicle

2019 ◽  
Vol 234 (1) ◽  
pp. 228-244 ◽  
Author(s):  
Yiran Zhang ◽  
Han Zhao ◽  
Kang Huang ◽  
Mingming Qiu ◽  
Lizhen Geng
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Optimization ◽  
Energy Management Strategy ◽  
Network Controller ◽  
Hybrid Electric ◽  
Optimization Methodology ◽  
Multi Mode

This study provides an optimization methodology in powertrain configurations and energy management strategy for a multi-mode plug-in hybrid electric vehicle. The challenge in this study is that the energy management strategy is highly intertwined with the powertrain configurations; the problem-specific complexity of this powertrain makes it even more difficult. Based on the analysis of the powertrain architecture, a simulation model is developed. Two self-adaptive solutions, a fuzzy PID driver and an adaptive shifting schedule are established to accommodate the powertrain parameter variations during the particle swarm optimization process. In order to obtain optimal and applicable results of both the powertrain and the energy management strategy configurations, a two-layer optimization controller is proposed. In the first layer, sub-optimal results are obtained by ACOR, additionally, the second layer is designed to final confirm both mode changes and gear shifting. At last, two possible applications—modifications to the shifting schedule and the energy management strategy, as well as developing a neuro network controller utilizing the optimal results—are analyzed. The results suggest that the hybrid optimization presented in this paper provides a solution to squeeze the potential of a plug-in hybrid electric vehicle while maintaining its practicability.

Model Predictive Control as an Energy Management Strategy for Hybrid Electric Vehicles

2009 ◽  
Author(s):  
Balaji Sampathnarayanan ◽  
Lorenzo Serrao ◽  
Simona Onori ◽  
Giorgio Rizzoni ◽  
Steve Yurkovich
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
A Priori ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Energy Management Strategy ◽  
Hybrid Electric

The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.

Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle

2017 ◽  
Vol 204 ◽  
pp. 476-488 ◽  
Author(s):  
Weichao Zhuang ◽  
Xiaowu Zhang ◽  
Daofei Li ◽  
Liangmo Wang ◽  
Guodong Yin
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Management Control ◽  
Mode Shift ◽  
Shift Map ◽  
Map Design ◽  
Hybrid Electric ◽  
Multi Mode
Sign in / Sign up

Export Citation Format

Share Document