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ردیف | عنوان | نوع |
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1 |
A Non-Convex Control Allocation Strategy as Energy-Efficient Torque Distributors for On-Road and Off-Road Vehicles
یک استراتژی تخصیص کنترل غیر محدب به عنوان توزیع کننده گشتاور مقرون به صرفه برای وسایل نقلیه جاده ای و خارج از جاده-2020 A Vehicle with multiple drivetrains, like a hybrid electric one, is an over-actuated system that means there is
an infinite number of combinations of torques that individual drivetrains can supply to provide a given total
torque demand. Energy efficiency is considered as the secondary objective to determine the optimum solution
among these feasible combinations. The resulting optimisation problem, which is nonlinear due to the multimodal
operation of electric machines, must be solved quickly to comply with the stability requirements of the
vehicle dynamics. A theorem is developed for the first time to formulate and parametrically solve the energyefficient
torque distribution problem of a vehicle with multiple different drivetrains. The parametric solution is
deployable on an ordinary electronic control unit (ECU) as a small-size lookup table that makes it significantly
fast in operation. The fuel-economy of combustion engines, load transformations due to longitudinal and lateral
accelerations, and traction efficiency of the off-road conditions are integrated into the developed theorem.
Simulation results illustrate the effectiveness of the provided optimal strategy as torque distributors of on-road
and off-road electrified vehicles with multiple different drivetrains. Keywords: Traction efficiency | Control allocation | Energy management strategies | Hybrid electric vehicles | Power loss | Multiple drivetrains |
مقاله انگلیسی |
2 |
Optimization & validation of Intelligent Energy Management System for pseudo dynamic predictive regulation of plug-in hybrid electric vehicle as donor clients
بهینه سازی و اعتبار سنجی سیستم مدیریت انرژی هوشمند برای تنظیم پیش بینی شبه دینامیکی پلاگین در خودروهای برقی هیبریدی به عنوان مشتری دهنده-2020 In developing countries, policies for discarding the existing Internal Combustion (IC) Engine vehicles for
faster adoption of Electric Vehicles’ not only creates burden on the existing power grid but also is
impractical. The conversion of Conventional IC Engine based Online Taxis or public transport vehicles
into Plug-in Hybrid Electric Vehicles donor clients, to participate in Vehicle to Grid & Vehicle to Vehicle
power transfer model, is the solution. These vehicles would not only have emissions within compliance
standards but would also reduces the load on the power grid meanwhile making an income through
power transfer. The Intelligent Energy Management System (IEMS) developed makes use of a Non
Dominated Sorting Genetic Algorithm (NSGA-II) based Pseudo dynamic predictive regulation approach
on the powertrain to optimize the emissions, fuel cost and traction battery SoC. If the vehicle intends to
participate in power transfer, the IEMS would predetermine the amount of SoC that would be used for an
upcoming journey using Global Positioning System(GPS) data interconnected with a server unit which
enables the IEMS to optimize the operating conditions of the vehicle. The modelled IEMS performance is
tested for a given driving cycle with varying traffic levels on a virtual simulation environment using the
IPG CarMaker software. A prototype with a 150 cc, 7.5 kW IC engine integrated to a 3 kW BLDC traction
motor is developed and the response to the predictive model is evaluated and found to provide 27.66%,
13.73% and 7.72% equivalent energy to micro grid for low, medium and high criticality conditions for the
user. Keywords: Vehicle to grid | NSGA-II optimization | State of charge | Emission | GPS | Real-time validation |
مقاله انگلیسی |
3 |
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 |
4 |
Improvement of ultracapacitors-energy usage in fuel cell based hybrid electric vehicle
بهبود استفاده از انرژی ماورا بنفش در خودروی برقی هیبریدی مبتنی بر سلول سوختی-2020 Hybrid electric power systems based on fuel cell stack and energy storage sources like
batteries and ultracapacitors are a plausible solution to vehicle electrification due to their
balance between acceleration performance and range. Having a high degree of hybridization
can be advantageous, considering the different characteristics of the power sources.
Some parameters to be considered are: specific power and energy, energy and power
density, lifetime, cost among others. Ultracapacitors (UC) are of particular interest in
electric vehicle applications due to its high-power capability, which is commonly required
during acceleration. UCs are commonly used without a power electronics interface due to
the high-power processing requirement. Although connecting UCs directly to the DC bus,
without using a power converter, presents considerable advantages, the main disadvantage
is related to the UC energy-usage capability, which is limited by constant DC bus
control. This paper proposes a novel energy-management strategy based on a fuzzy
inference system, for fuel-cell/battery/ultracapacitor hybrid electric vehicles. The proposed
strategy is able to control the charge and discharge of the UC bank in order to take
advantage of its energy storage capability. Experimental results show that the proposed
strategy reduces the waste of energy due to dynamic brake in 14%. This represents a
reduction in energy consumption from 218 Wh/km to 192 Wh/km for the same driving
conditions. By using the proposed energy management strategy, the estimated fuel efficiency
in miles per gallon equivalent was also increase from 96 mpge to 109 mpge. Keywords: PEMFC | Hybrid electric vehicles | Energy management | Ultracapacitors |
مقاله انگلیسی |
5 |
A robust online energy management strategy for fuel cell/battery hybrid electric vehicles
یک استراتژی مدیریت انرژی آنلاین قوی برای خودروهای برقی هیبریدی سلول / باتری-2020 Traditional optimization-based energy management strategies (EMSs) do not consider the
uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes
a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the
uncertain driving cycles. The energy consumption model of the FCHEV is built by
considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear
programming-based method is proposed to produce the benchmark solution. The ROEMS
instantaneously minimizes the equivalent power of fuel cell and battery, where an
equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming
to battery energy. To control the state of charge of battery, two control coefficients
are introduced to adjust the power of battery in objective function. Another penalty coefficient
is used to amend the power of fuel cell, which reduces the load change of fuel cell
so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has
good performance in both fuel economy and load change control of fuel cell. The most
important advantage of ROEMS is its robustness and adaptivity, because it almost produces
the optimal solution without changing the control parameters when driving cycles are
changed. Keywords: Fuel cell | Hybrid electric vehicles | Online energy management strategy | Robustness | Uncertaint |
مقاله انگلیسی |
6 |
Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control
استراتژی مدیریت انرژی برای پلاگین در خودروهای برقی هیبریدی یکپارچه با کنترل همکاری خودرو-محیط-2020 Energy management strategies have been proven to be instrumental in fully realizing the potential of
plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved adaptive equivalent consumption
minimization strategy (IA-ECMS). In an IA-ECMS, the equivalence factor (EF) can be tuned in
real time due to integration with the results of the vehicle-environment cooperative control. This study’s
main contributions are twofold. First, a novel A-ECMS is developed, in which the EF tuning method is
carefully designed based on the results of a correlation study. The study results reveal that EF is determined
by the future driving behaviour and the current component status. To ascertain the future driving
behaviour, a method based on participatory sensing data (PSD) is used to implement the vehicleenvironment
cooperative control. Second, a comparative study of the IA-ECMS and the energy management
strategy based on the existing model of predictive control (MPC) is performed. The comparison
results show that the application process of the IA-ECMS is similar to that of the MPC-based method
except for two main differences. The simulation results demonstrate that the presented IA-ECMS
approach could outperform in fuel economy the conventional A-ECMS (CA-ECMS) method. Keywords: Plug-in hybrid electric vehicles | Adaptive equivalent consumption | minimization strategy | Equivalence factor | Model predictive control | Vehicle-environment cooperative control |
مقاله انگلیسی |