scholarly journals Communication‐based predictive energy management strategy for a hybrid powertrain

2021 ◽  
Author(s):  
Junpeng Deng ◽  
Daniel Adelberger ◽  
Luigi del Re
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Energy Management Strategy ◽  
Hybrid Powertrain

Power Optimization of Series Hydraulic Hybrid Powertrain for Compact Wheel Loader

2019 ◽  
Author(s):  
Qunya Wen ◽  
Feng Wang ◽  
Bing Xu ◽  
Zongxuan Sun
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Power Optimization ◽  
Parameter Study ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Wheel Loader ◽  
Hydraulic Hybrid ◽  
Equivalent Consumption Minimization Strategy

Abstract As an effective approach to improving the fuel economy of heavy duty vehicles, hydraulic hybrid has shown great potentials in off-road applications. Although the fuel economy improvement is achieved through different hybrid architectures (parallel, series and power split), the energy management strategy is still the key to hydraulic hybrid powertrain. Different optimization methods provide powerful tools for energy management strategy of hybrid powertrain. In this paper a power optimization method based on equivalent consumption minimization strategy has been proposed for a series hydraulic hybrid wheel loader. To show the fuel saving potential of the proposed strategy, the fuel consumption of the hydraulic hybrid wheel loader with equivalent consumption minimization strategy was investigated and compared with the system with a rule-based strategy. The parameter study of the equivalent consumption minimization strategy has also been conducted.

scholarly journals Analysis of the integration of the three-way catalyst thermal management in the on-line supervisory control strategy of a gasoline full hybrid vehicle

2020 ◽  
Vol 197 ◽  
pp. 06008
Author(s):  
Marco Benegiamo ◽  
Paolo Carlucci ◽  
Vincenzo Mulone ◽  
Andrea Valletta
Keyword(s):  
Thermal Management ◽  
Energy Management ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Supervisory Control ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Three Way Catalyst

Full hybrid electric vehicles have proven to be a midterm viable solution to fulfil stricter regulations, such as those regarding carbon dioxide abatement. Although fuel economy directly benefits from hybridization, the use of the electric machine for propulsion may hinder an appropriate warming of the aftertreatment system, whose temperature is directly related to the emissions conversion efficiency. The present work evaluates the efficacy of a supervisory energy management strategy based on Equivalent Minimization Consumption Strategy (ECMS) which incorporates a temperature-based control for the thermal management of the Three-Way Catalyst (TWC). The impact of using only the midspan temperature of TWC is compared against the case where temperature at three different sampling points along the TWC length are used. Moreover, a penalty term based on TWC temperature has been introduced in the cost functional of the ECMS to allow the control of the TWC temperature operating window. In fact, beyond a certain threshold, the increase of the engine load, requested to speed up TWC warming, does not translate into a better catalyst efficiency, because the TWC gets close to its highest conversion rate. A gasoline P2 parallel full hybrid powertrain has been considered as test case. Results show that the effects of the different calibrations strategies are negligible on the TWC thermal management, as they do not provide any improvements in the fuel economy nor in the emissions abatement of the hybrid powertrain. This effect can be explained by the fact that the charge sustaining condition has a greater weight on the energy management strategy than the effects deriving from the addition of the soft constraints to control the TWC thermal management. These results hence encourage the use of simple setups to deal with the control of the TWC in supervisory control strategies for full hybrid electric vehicles.

scholarly journals Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

2021 ◽  
Vol 12 (4) ◽  
pp. 175
Author(s):  
Ying Huang ◽  
Fachao Jiang ◽  
Haiming Xie
Keyword(s):  
Machine Learning ◽  
Optimal Control ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Management Strategy ◽  
General Purpose ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
New Energy

The new energy of concrete truck mixers is of great significance to achieve energy conservation and emission reduction. Unlike general-purpose vehicles, in addition to driving conditions, upper-mixing system conditions, operation scenarios, and variable loads are the key factors to be considered during the new energy of concrete truck mixers. This study focuses on the machine-learning-based approximate optimal energy management design for a concrete truck mixer equipped with a novel extended-range powertrain from two aspects: trip information and energy management strategy. Firstly, an optimal control database is constructed, which benefits from a global optimization algorithm with dimension reduction for the constrained time-varying two-point boundary value problems with two control variables, and the driving data analysis through machine learning and data-driven methods. Then, different machine-learning-based driving condition identifiers are constructed and compared. Finally, a vehicle mass and power demand of an upper-part system based novel neural network energy management strategy is designed based on a constructed optimal control database. Simulation results show that the intelligent optimization algorithm based on the ML of trip information and energy management is an appropriate way to solve the online energy management problem of the concrete truck mixer equipped with the proposed novel powertrain.

Modeling and Design of a Hydraulic Hybrid Powertrain for Passenger Vehicle

2017 ◽  
Author(s):  
Haoxiang Zhang ◽  
Feng Wang ◽  
Kim A. Stelson
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Controller Design ◽  
Mechanical Transmission ◽  
Passenger Vehicle ◽  
Energy Management Strategy ◽  
Hybrid Powertrain ◽  
Hydraulic Hybrid ◽  
Hydraulic Accumulator ◽  
Pressure Controlled

A hydraulic hybrid powertrain for passenger vehicle is studied in this paper. The hydraulic hybrid powertrain consists of a hydro-mechanical transmission and a hydraulic accumulator. The key component of this hydro-mechanical transmission is a pressure-controlled hydraulic transmission. It combines pumping and motoring function in one unit and is potentially more competitive in terms of both energy efficiency and cost effectiveness than a conventional hydrostatic transmission. By feeding the output flow of the pressure-controlled hydraulic transmission to a variable displacement motor coupled to the transmission output shaft, a more compact and simpler hydro-mechanical transmission is constituted. In this paper the systematic approach of applying the hydraulic hybrid powertrain to a passenger vehicle is studied. A dynamic simulation model is developed in Simulink and the U.S. EPA’s urban cycle is used as the test driving cycle. A rule-based energy management strategy (EMS) for the hydraulic hybrid powertrain has also been developed. The system parameter design, controller design and the energy management strategy are evaluated through simulation.

scholarly journals Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

2021 ◽  
Vol 12 (3) ◽  
pp. 159
Author(s):  
Enrico Landolfi ◽  
Francesco Junior Minervini ◽  
Nicola Minervini ◽  
Vincenzo De De Bellis ◽  
Enrica Malfi ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Transport Sector ◽  
Energy Management Strategy ◽  
Steering Angle ◽  
Hybrid Powertrain ◽  
The Road

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

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