با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Energy management of hybrid vehicles with state constraints: A penalty and implicit Hamiltonian minimization approach
مدیریت انرژی وسایل نقلیه هیبریدی با محدودیت های دولت: رویکردحداقل سازی همیلتونی ضمنی و مجازاتی -2020 When designing hybrid vehicles, the energy management is formulated as an optimal control problem. The
Pontryagin’s minimum principle represents a powerful methodology capable of solving the energy management
offline. Moreover, the Pontryagin’s minimum principle has been proved useful in the derivation of online energy
management algorithms, such as the equivalent consumption minimization strategy. Nevertheless, difficulties on
the application of the Pontryagin’s minimum principle arise when state constraints are included in the definition
of the problem. A possible solution is to combine the Pontryagin’s minimum principle with a penalty function
approach. This is done by adding functions to the Hamiltonian, which increase the value of the Hamiltonian
whenever the optimal trajectory violates its constraints. However, the addition of penalty functions to the
Hamiltonian makes it harder to compute its minimum. This work proposes an effective penalty approach
through an implicit Hamiltonian minimization. The proposed method is applied to solve the energy management
for a hybrid electric vehicle modeled as a mixed input-state constrained optimal control problem with two states:
the battery temperature and state-of-energy. It is demonstrated to be up to 46 times faster than the dynamic
programming method while taking benefits of state-of-the-art boundary value problem solvers and avoiding any
issue related to state quantization. Keywords: Energy management | Hybrid electric vehicles | Pontryagin’s minimum principle | Mixed input-state constraints | Penalty function approach |
مقاله انگلیسی |
2 |
Energy management strategy to reduce pollutant emissions during the catalyst light-off of parallel hybrid vehicles
استراتژی مدیریت انرژی برای کاهش انتشار آلاینده ها در هنگام خاموش شدن کاتالیزور وسایل نقلیه هیبریدی موازی-2020 The transportation sector is a major contributor to both air pollution and greenhouse gas emissions. Hybrid
electric vehicles can reduce fuel consumption and CO2 emissions by optimizing the energy management of the
powertrain. The purpose of this study is to examine the trade-off between regulated pollutant emissions and
hybrid powertrain efficiency. The thermal dynamics of the three-way catalyst are taken into account in order to
optimize the light-off. Experimental campaigns are conducted on a spark-ignition engine to introduce simplified
models for emissions, exhaust gas temperature, catalyst heat transfers and efficiency. These models are used to
determine the optimal distribution of a power request between the thermal engine and the electric motor with
three-dimensional dynamic programming and a weighted objective function. A pollution-centered scenario is
compared with a consumption-centered scenario for various driving cycles. The optimal torque distribution for
the emissions-centered scenario on the world harmonized light-duty vehicles test cycle shows an 8–33% decrease
in pollutant emissions while the consumption remains stable (0.1% increase). The consistency of the results is
analyzed with respect to the discretization parameters, driving cycle, electric motor and battery sizing, as well as
emission and catalyst models. The control strategies are promising but will have to be adapted to online engine
control where the driving cycle and the catalyst efficiency are uncertain.. Keywords: Hybrid electric vehicle | Energy management strategy | Dynamic programming | Catalyst thermal behavior | Fuel consumption | Pollutant emissions |
مقاله انگلیسی |
3 |
Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles
مدیریت انرژی اقتصادی مبتنی بر کنترل پیش بینی سلسله مراتبی برای ساخت و ساز وسایل نقلیه هیبریدی سلول سوختی-2020 Fuel cell hybrid construction vehicles are attractive for future fuel cell applications. Model predictive
control as an energy management strategy has been applied to various hybrid electric vehicles. In this
paper, a hierarchical model predictive control-based energy management strategy for fuel cell hybrid
construction vehicles is explored. The hierarchical model predictive control framework consists of economic
and control levels. The economic level considers economic costs, including of hydrogen consumption
and components use cost. Real-time optimization is implemented at the economic level to
identify the optimal reference trajectory. The control layer is a model predictive control that controls the
system to follow the reference trajectory. A prediction methodology-based on wavelet transformation
and Levenberg-Marquardt optimised neural network is proposed for the complex load prediction of fuel
cell hybrid construction vehicle. The simulations performed with cyclic loading show that the accuracy of
the proposed prediction methodology is satisfactory, and demonstrate the superiority of the proposed
energy management strategy. The proposed hierarchical model predictive control-based energy management
strategy considering economy and controllability is important for commercial application in
hybrid electric vehicles. Keywords: Model predictive control | Energy management | Fuel cell | Construction vehicle | Wavelet | Neural network |
مقاله انگلیسی |
4 |
Rule-corrected energy management strategy for hybrid electric vehicles based on operation-mode prediction
استراتژی مدیریت انرژی تصحیح شده توسط قانون برای وسایل نقلیه الکتریکی هیبریدی بر اساس پیش بینی عملکرد حالت-2018 The energy crisis and exhaust emissions are serious problems that are largely related to road traffic. One
solution to these threats is to switch from traditional gasoline-based vehicles to hybrid electric vehicles
(HEVs) or electric vehicles (EVs), which also has the benefit of promoting a more sustainable economy.
Energy management strategies (EMS) for HEVs or EVs play an important role in improving fuel economy.
As a stochastic prediction method, a Markov chain, has been widely used in the prediction of driving
conditions, but the application of a Markov chain in the prediction of HEV-operating modes in a rule
based EMS has rarely been presented in the literature. In addition, the threshold selection of rule
based EMS is usually based on experience and it is difficult to achieve optimal performance. In this
paper, the impact of operation-mode prediction on rule-based EMS fuel economy has been explored to
achieve real-time on-line corrections to motor and engine torque and to enhance their capacity for on
line optimization. Thus, a new EMS for HEV has been proposed based on operation-mode prediction
using a Markov chain, which determines the on-line correction of torque distribution between the en
gine and the electric motor. The Markov decision processes and transition matrix are introduced first,
and then, the transition probability matrix and torque correction model are established using the
MATLAB/Simulink platform. The results of the simulation show that the proposed approach provides a
13.1% and 9.6% improvement in real fuel consumption under the New European Driving Cycle and the
Urban Dynamometer Driving Schedule respectively, in comparison with the conventional rule-based
control strategies without operation-mode prediction. The results also show that the proposed control
strategy can significantly enhance the real-time optimization control performance of the EMS while
maintaining the state of charge within a reasonable range.
Keywords: Hybrid electric vehicles ، Markov chain ، Operation-mode prediction ، Energy management strategy |
مقاله انگلیسی |