scholarly journals Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle

2018 ◽  
Vol 10 (10) ◽  
pp. 3758 ◽  
Author(s):  
Shaobo Xie ◽  
Xiaosong Hu ◽  
Kun Lang ◽  
Shanwei Qi ◽  
Tong Liu
Keyword(s):  
Dynamic Programming ◽  
Power Unit ◽  
Energy Management ◽  
Control Strategy ◽  
Minimum Principle ◽  
Mechanical Transmission ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Pontryagin's Minimum Principle ◽  
Hybrid Electric

Pontryagin’s Minimum Principle (PMP) has a significant computational advantage over dynamic programming for energy management issues of hybrid electric vehicles. However, minimizing the total energy consumption for a plug-in hybrid electric vehicle based on PMP is not always a two-point boundary value problem (TPBVP), as the optimal solution of a powering mode will be either a pure-electric driving mode or a hybrid discharging mode, depending on the trip distance. In this paper, based on a plug-in hybrid electric truck (PHET) equipped with an automatic mechanical transmission (AMT), we propose an integrated control strategy to flexibly identify the optimal powering mode in accordance with different trip lengths, where an electric-only-mode decision module is incorporated into the TPBVP by judging the auxiliary power unit state and the final battery state-of-charge (SOC) level. For the hybrid mode, the PMP-based energy management problem is converted to a normal TPBVP and solved by using a shooting method. Moreover, the energy management for the plug-in hybrid electric truck with an AMT involves simultaneously optimizing the power distribution between the auxiliary power unit (APU) and the battery, as well as the gear-shifting choice. The simulation results with long- and short-distance scenarios indicate the flexibility of the PMP-based strategy. Furthermore, the proposed control strategy is compared with dynamic programming (DP) and a rule-based charge-depleting and charge-sustaining (CD-CS) strategy to evaluate its performance in terms of computational accuracy and time efficiency.

Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles

2019 ◽  
Vol 234 (6) ◽  
pp. 1544-1562
Author(s):  
Wissam Bou Nader ◽  
Yuan Cheng ◽  
Emmanuel Nault ◽  
Alexandre Reine ◽  
Samer Wakim ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Unit ◽  
Hybrid Electric Vehicle ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Technological Analysis

Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine–system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems’ efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine–system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine–auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

scholarly journals Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2954
Author(s):  
Loïc Joud ◽  
Rui Da Silva ◽  
Daniela Chrenko ◽  
Alan Kéromnès ◽  
Luis Le Moyne
Keyword(s):  
Experimental Study ◽  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Control Strategy ◽  
Management Strategy ◽  
Energetic Efficiency ◽  
Energy Management Strategy ◽  
Common Cycles ◽  
Hybrid Electric

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.

Sign in / Sign up

Export Citation Format

Share Document