scholarly journals Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

2020 ◽  
Vol 10 (3) ◽  
pp. 745
Author(s):  
Zhong Wang ◽  
Xiaohong Jiao
Keyword(s):  
Energy Management ◽  
Predictive Control ◽  
Fuel Economy ◽  
Optimization Problem ◽  
Lower Layer ◽  
Hybrid Powertrain ◽  
Construction Machinery ◽  
Stochastic Optimization Problem ◽  
Driving Conditions ◽  
Disturbance Estimator

Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.

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.

Nonlinear model predictive energy management of hydrostatic drive transmissions

2018 ◽  
Vol 233 (3) ◽  
pp. 335-347
Author(s):  
Joni Backas ◽  
Reza Ghabcheloo
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fuel Economy ◽  
High Energy ◽  
Nonlinear Model Predictive Control ◽  
Detailed Model ◽  
Control Framework ◽  
Velocity Tracking

In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

Modeling and Control of New Multi-Mode Plug-in Hybrid Electric Vehicle

2019 ◽  
Author(s):  
Abhinandan Raut ◽  
Suryaji Phalke ◽  
Diane Peters
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Powertrain ◽  
Power Sources ◽  
Full Size ◽  
Wide Range ◽  
Hybrid Electric ◽  
Multi Mode

Abstract Fuel economy and emission standards for internal combustion engine (ICE) vehicles lead to emergence of hybrid powertrain mechanisms. Hybrid powertrains can enable significant fuel economy improvements without sacrificing vehicle performance or utility. This requires optimization of engine operation, regenerative braking, and use of a wide range of possible combinations of engine and battery usage. The multi-mode hybrid powertrain in this paper combines many options to meet a complex driving requirement while maintaining the desired fuel economy. In this paper, a systematic design methodology is used to design a full-size hybrid vehicle with multiple components. This involves the modeling, simulation and development of optimal energy management strategy. This vehicle (full size car) has dual battery, dual fuel V6 engine with cylinder deactivation and bi-directional power flow in and from dual motor/generator. The design includes multiple gearboxes to connect these pieces. The vehicle model allows many degrees of freedom including various modes of operation depending upon the combination of degree of driver involvement, vehicle power requirement and optimized fuel economy resulting in automatic switching between modes. This model is tested for different Environmental Protection Agency (EPA) driving cycles. By integrating all components of this hybrid electric vehicle (HEV) and the highly coordinated energy management control system that performs optimum blending of torque, speed, and power from multiple power sources, the benefit from this hybridization is maximized.

Robustification Through Minimax Dynamic Programming and Its Implication for Hybrid Vehicle Energy Management Strategies

2021 ◽  
Author(s):  
Kevin R. Mallon ◽  
Francis Assadian
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Fuel Economy ◽  
Driver Behavior ◽  
Tuning Parameter ◽  
Stochastic Dynamic ◽  
Energy Management Strategy ◽  
Worst Case ◽  
Hybrid Electric ◽  
Driving Conditions

Abstract Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programming (SDP) controller with minimax dynamic programming (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a trade-off of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

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 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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