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1 |
Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
استراتژی مدیریت انرژی آنلاین از وسایل نقلیه برقی هیبریدی سلول سوختی بر اساس یادگیری قانون-2020 In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy
consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of
fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived
offline by the Pontryagin’s minimum principle. Next, the K-means algorithm is employed to hierarchically
cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning
to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and
classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the
abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can
achieve more than 95% savings of energy consumption economy, solved by Pontryagin’s minimum
principle, with less calculation intensity and without dependence on prior driving conditions, thereby
manifesting the feasibility of online application. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hierarchical clustering | Rule learning |
مقاله انگلیسی |
2 |
Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles
استراتژی مدیریت سلسله مراتبی مبتنی بر یادگیری تقویتی مبتنی بر داده برای وسایل نقلیه الکتریکی ترکیبی سلول سوختی / باتری / فوق خازن-2020 A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy
system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power
splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the
reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed
for tackling high-dimensional state-action space, and finding a trade-off between global learning and realtime
implementation. The power splitting policy based on experimental data is obtained by using reinforcement
learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy
management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption
economy. Simulation results confirm that, compared with existing learning algorithms and optimization
methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high
computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Reinforcement learning | Data driven | Hierarchical power splitting |
مقاله انگلیسی |
3 |
Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm
مدیریت انرژی وسایل نقلیه الکتریکی هیبریدی: مروری بر بهینه سازی انرژی سیستم قدرت هیبریدی سلول سوختی بر اساس الگوریتم ژنتیکی-2020 Under the background of current environmental pollution and serious shortage of fossil energy, the development
of electric vehicles driven by clean new energy is the key to solve this problem, especially the hybrid electric
vehicle driven by fuel cell is the most effective solution. Many scholars have found that the output performance
of hybrid system is an important reason to determine the life of fuel cell. Unreasonable output will affect the
control characteristics of the drive system, resulting in a series of serious consequences such as the reduction of
the life of fuel cell hybrid power system. Therefore, the energy management strategy and performance optimization
of hybrid system is the key to ensure the normal operation of the system. At present, many excellent
researchers have carried out relevant research in this field. Genetic algorithm is a heuristic algorithm, which has
better optimization performance. It can easily choose satisfactory solutions according to the optimization objectives,
and make up for these shortcomings by using its own characteristics. These characteristics make genetic
algorithm have outstanding advantages in the iterative optimization of energy management strategy. This paper
analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies,
aiming to analyze and select the optimization rules and parameters, optimization objects and optimization
objectives. This paper hopes to provide guidance for the optimal control strategy and structural design of the fuel
cell hybrid power system, contribute to the research on improving the energy utilization efficiency of the hybrid
power system and extending the life of the fuel cell, and provide more ideas for the optimization of energy
management in the future. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hybrid power system | Genetic algorithm |Optimization parameters and objectives |
مقاله انگلیسی |
4 |
Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer
مدیریت انرژی پیش گویانه چند حالته برای وسایل نقلیه الکتریکی هیبریدی سلول سوختی با استفاده از شناسه الگوی رانندگی مارکوف-2020 Considering the changeable driving conditions in reality, energy management strategies for fuel cell hybrid
electric vehicles should be able to effectively distribute power demands under multiple driving patterns. In this
paper, the development of an adaptive energy management strategy is presented, including a driving pattern
recognizer and a multi-mode model predictive controller. In the supervisory level, the Markov pattern recognizer
can classify the real-time driving segment into one of three predefined patterns. Based on the periodically updated
pattern identification results, one set of pre-optimized control parameters is selected to formulate the
multi-objective cost function. Afterwards, the desirable control policies can be obtained by solving a constrained
optimization problem within each prediction horizon. Validation results demonstrate the effectiveness of the
Markov pattern recognizer, where at least 94.94% identification accuracy can be reached. Additionally, compared
to a single-mode benchmark strategy, the proposed multi-mode strategy can reduce the average fuel cell
power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen
consumption, indicating the improved fuel cell system durability and the enhanced fuel economy. Keywords: Energy management strategy | Driving pattern recognition | Model predictive control | Fuel cell hybrid electric vehicle |
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 |
Data-driven reinforcement-learning-based hierarchical energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles
استراتژی مدیریت انرژی سلسله مراتبی مبتنی بر تقویت یادگیری مبتنی بر داده ها برای وسایل نقلیه برقی هیبریدی سلول / باتری / ultracapacitor-2020 A reinforcement-learning-based energy management strategy is proposed in this paper for managing energy
system of Fuel Cell Hybrid Electric Vehicles (FCHEV) equipped with three power sources. A hierarchical power
splitting structure is employed to shrink large state-action space based on an adaptive fuzzy filter. Then, the
reinforcement-learning-based algorithm using Equivalent Consumption Minimization Strategy (ECMS) is proposed
for tackling high-dimensional state-action space, and finding a trade-off between global learning and realtime
implementation. The power splitting policy based on experimental data is obtained by using reinforcement
learning algorithm, which allows for many different driving cycles and traffic conditions. The proposed energy
management strategy can achieve low computation cost, optimal fuel cell efficiency and energy consumption
economy. Simulation results confirm that, compared with existing learning algorithms and optimization
methods, the proposed reinforcement-learning-based energy management strategy using ECMS can achieve high
computation efficiency, lower power fluctuation of fuel cell and optimal fuel economy of FCHEV. Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Reinforcement learning | Data driven | Hierarchical power splitting |
مقاله انگلیسی |