scholarly journals Multi-Level Energy Management—Part II: Implementation and Validation

Vehicles ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 41-56 ◽  
Author(s):  
Vital van Reeven ◽  
Theo Hofman
Keyword(s):  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Fuel Efficiency ◽  
Real Life ◽  
Level Energy ◽  
Fuel Saving ◽  
Energy Management Systems ◽  
Multi Level ◽  
Hybrid Electric ◽  
Map Data

In hybrid electric vehicles, energy management systems (EMS) using optimization show superior fuel efficiency compared to rule-based strategies. However, little research shows its real-life applicability. In Part II of this work, the multi-level, model-predictive EMS from Part I is implemented on a heavy-duty parallel hybrid electric vehicle, using GPS and map data as preview. The power split, hybrid mode, and gear selection, including switching costs, are optimized in real time, thereby proving the feasibility of optimal control techniques for hybrid driveline control. Functional validation of the EMS on a test track confirm the fuel-saving mechanism as simulated in Part I. In addition to a fuel saving of 36%, the EMS also improves the drivability, by reducing the amount of open driveline events.

scholarly journals Multi-Level Energy Management for Hybrid Electric Vehicles—Part I

Vehicles ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 3-40 ◽  
Author(s):  
Vital van Reeven ◽  
Theo Hofman
Keyword(s):  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Level Energy ◽  
Conventional Vehicle ◽  
Decision Variables ◽  
Multi Level ◽  
Hybrid Electric ◽  
System States ◽  
Battery Energy

The fuel economy of a hybrid electric vehicle (HEV) is improved, by taking the energy relevant system states into account in the energy management system (EMS). With an increasing number of states and decision variables, energy optimizing algorithms in the EMS can be prohibitive for real-time implementation. In part I of this work, a model-based, multi-level approach is taken to subdivide the original (large) optimization problem into computational efficient sub-problems, based on optimal control techniques using a preview. The resulting EMS solves the problem of power-split between engine and motor/generator, mode and gear switching including switching costs, with battery energy constraints. The superior energy efficiency of the multi-level EMS is simulated on a representative heavy duty drive cycle, where it saves 7.0% fuel, compared to a conventional vehicle, where the baseline EMS for the HEV saves 5.8%. In part II, real-world validation of the EMS is performed.

scholarly journals A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5355
Author(s):  
Qicheng Xue ◽  
Xin Zhang ◽  
Teng Teng ◽  
Jibao Zhang ◽  
Zhiyuan Feng ◽  
...  
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Strategy ◽  
Energy Management Systems ◽  
Hybrid Electric ◽  
And Control

The energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control algorithm to satisfy a variety of development objectives while not reducing vehicle performance. In this research, a comprehensive, multi-level classification for HEVs is introduced in detail from the aspects of the degree of hybridization (DoH), the position of the motor, the components and configurations of the powertrain, and whether or not the HEV is charged by external power. The principle and research status of EMSs for each type of HEV are summarized and reviewed. Additionally, the EMSs and control algorithms of HEVs are compared and analyzed from the perspectives of characteristics, applications, real-time abilities, and historical development. Finally, some discussions about potential directions and challenges for future research on the energy management systems of HEVs are presented. This review is expected to bring contribution to the development of efficient, intelligent, and advanced EMSs for future HEV energy management systems.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

Sign in / Sign up

Export Citation Format

Share Document