A multi-objective algorithm for train driving energy reduction with multiple time targets

Fernández-Rodríguez, Adrián; Su, Shuai; Fernández-Cardador, Antonio; Cucala, Asunción P.; Cao, Yuan

doi:10.1080/0305215x.2020.1746782

Cited by 18 publications

(7 citation statements)

References 54 publications

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“…Also, route and rolling stock data, the electromechanical specification of vehicles, and the line's operational scheme are the main data for the simulation. Thus, its movement could be determined by employing Newton's second law and kinematics equations, as demonstrated in () 36,37 . In Equation (), the effective mass of the understudy train has been defined.…”

Section: System Modelingmentioning

confidence: 99%

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Ehteshami

Hashemi‐Dezaki

Javadi

2022

Energy Science & Engineering

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Several studies have been reported for optimal operation of electrical railway systems (ERSs). However, the stochastic energy management of ERSs, including renewable energy resources (RERs), has received less attention. The RERs’ uncertainties might affect the ERSs. On the other hand, the calculation time of the Monte Carlo simulation (MCS)‐based approaches is an essential challenge, which should be solved, particularly in real‐time decisions and recursive optimization problems. Thus, it is crucial to study the ERSs' stochastic behaviors and uncertainties, including RERs. This paper tries to overcome the discussed concerns and challenges by proposing a novel ERS’s optimal stochastic energy management using clustering algorithms. In this paper, the backward scenario reduction algorithm has been used. In addition, regenerative braking energy (RBE) and energy management systems (ESSs) have been studied. Studying the impacts of changes in the number of passengers on the optimized operation of ERSs and investigating the interaction between the utility grid and the ERS are other contributions of this research. The proposed method is applied to an actual test system of Tehran Urban and Suburban Railway Operation Company (TUSROC). Test results are validated by comparing with available MCS‐based methods. Simulation results illustrate the accuracy and computation time advantages of the proposed method. Simulation results illustrate that <0.6% inaccuracy appears in the proposed method, while it would be 500 times faster than MCS‐based ones. The comparative test results show the advantages of the introduced method.

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Section: System Modelingmentioning

confidence: 99%

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Ehteshami

Hashemi‐Dezaki

Javadi

2022

Energy Science & Engineering

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“…First, the error sum of the three outputs of the deep neural network is denoted as e, and the smaller the value e is, the better the network performs. Then the fitness f k of each agent k in the algorithm is defined as Equation (13).…”

Section: Mpcma Implementation 1) Multi-populationmentioning

confidence: 99%

“…In view of the actual situation of complex railway lines, a new multi‐objective search algorithm was proposed to obtain the most effective set of the train speed curves under each combination of the arrival time and intermediate time [12]. In addition, an efficient evolutionary multi‐objective algorithm was proposed in [13], and a diverse and well‐distributed approximation of the Pareto frontier solution set was established. The solutions were the tradeoff of two objectives between the train running time and energy‐consumption.…”

Section: Introductionmentioning

confidence: 99%

Intelligent train operation based on deep learning from excellent driver manipulation patterns

et al. 2022

IET Intelligent Trans Sys

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In the application of deep learning to realize intelligent train operation, there are some problems, such as the single learning task. Especially when using the gradient descent approach to optimize the structure, weight and threshold of a deep network, it is easy in this task to fall into a local optimum. This leads to excessive reliance on manual tuning experience. Aiming at the above issues, this paper proposes a new approach of train manipulation and prediction based on a long short-term memory (LSTM) deep network. From the perspective of automatic hyper-parameter optimization, the gradient-free intelligent search method is principally chosen to optimize the architecture and parameters of a LSTM deep network, so as to improve the manipulation accuracy based on learning from excellent drivers. This method first selects excellent driver data through the Pareto dominance principle and crowding distance calculation; on this basis, a step-by-step method is used to optimize the structure, weight and threshold of the LSTM network. Particularly, in the first step, we adopt a genetic algorithm to search for the optimal deep network structure, which overcomes the problem that the structure is difficult to determine. In the second step, we optimize the parameters of the deep network, a process that is divided into two stages of 'rough learning' and 'precise learning'. In the 'rough learning' stage, we use the multi-population chained multi-agent (MPCMA) algorithm to preliminarily optimize the LSTM network parameters. In the 'precise learning' stage, the Adam algorithm is applied to further finely optimize the network parameters. Finally, through simulation experiments, it is verified that the proposed method improves the accuracy of train manipulation and prediction, and shows strong robustness in situations of multiple manipulation sequences and different temporary speed limits.

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“…Therefore, it is essential to calculate energy usage over various operating times to assess a method's efficacy. This requirement is reflected in the acquisition of the Pareto frontier, which delineates the trade-off between travel time and energy consumption as documented in sources [22,33,35,36]. However, a comparative analysis of the Pareto frontiers for CC and CR remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

An improved energy‐efficient driving strategy for routes with various gradients and speed limits

Liu,

Tian,

Jiang

et al. 2024

IET Intelligent Trans Sys

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With the increasing concerns about railway energy efficiency, two typical driving strategies have been used in actual train operation. One includes a sequence of full power traction, cruising, coasting, and full braking (CC). The other uses coasting–remotoring (CR) to replace cruising in CC. However, energy‐saving performance by CC and CR, which can be affected by route parameters of gradients and speed limits, has not been fully compared and studied. This paper analyses the energy distribution of CC and CR considering various route parameters and proposes an improved strategy for different gradients and speed limits. The detailed energy flow of CC and CR is analysed by Cauchy–Bunyakovsky–Schwarz inequality and the generalised Hölder's inequality, and then, a novel driving strategy CC_CR is designed. To verify the theoretical results and the effectiveness of the proposed strategy, three simulators with CC, CR, and CC_CR driving modes have been developed and implemented into case studies of four scenarios as well as a real‐world metro line. Simulation results demonstrate that CR can only outperform CC on routes with steep downhill and CC_CR is always the best strategy. The energy savings of CC_CR can be as much as 15% more than CR and 42% greater than CC.

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A multi-objective algorithm for train driving energy reduction with multiple time targets

Cited by 18 publications

References 54 publications

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Optimal stochastic energy management of electrical railway systems considering renewable energy resources' uncertainties and interactions with utility grid

Intelligent train operation based on deep learning from excellent driver manipulation patterns

An improved energy‐efficient driving strategy for routes with various gradients and speed limits

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