Assessment of the worthwhileness of efficient driving in railway systems with high-receptivity power supplies

Eco-driving is one of the most important strategies for significantly reducing the energy consumption of railways with low investments. It consists of designing a way of driving a train to fulfil a target running time, consuming the minimum amount of energy. Most eco-driving energy savings come from the substitution of some braking periods with coasting periods. Nowadays, modern trains can use regenerative braking to recover the kinetic energy during deceleration phases. Therefore, if the receptivity of the railway system to regenerate energy is high, a question arises: is it worth designing eco-driving speed profiles? This paper assesses the energy benefits that eco-driving can provide in different scenarios to answer this question. Eco-driving is obtained by means of a multi-objective particle swarm optimization algorithm, combined with a detailed train simulator, to obtain realistic results. Eco-driving speed profiles are compared with a standard driving that performs the same running time. Real data from Spanish high-speed lines have been used to analyze the results in two case studies. Stretches fed by 1 × 25 kV and 2 × 25 kV AC power supply systems have been considered, as they present high receptivity to regenerate energy. Furthermore, the variations of the two most important factors that affect the regenerative energy usage have been studied: train motors efficiency ratio and catenary resistance. Results indicate that the greater the catenary resistance, the more advantageous eco-driving is. Similarly, the lower the motor efficiency, the greater the energy savings provided by efficient driving. Despite the differences observed in energy savings, the main conclusion is that eco-driving always provides significant energy savings, even in the case of the most receptive power supply network. Therefore, this paper has demonstrated that efforts in improving regenerated energy usage must not neglect the role of eco-driving in railway efficiency

Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems

Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have bypassed the air gaps and aim at causing safety incidents and service disruptions. In this paper, we study false data injection (FDI) attacks against railways' traction power systems (TPSes). Specifically, we analyze two types of FDI attacks on the train-borne voltage, current, and position sensor measurements - which we call efficiency attack and safety attack -- that (i) maximize the system's total power consumption and (ii) mislead trains' local voltages to exceed given safety-critical thresholds, respectively. To counteract, we develop a global attack detection (GAD) system that serializes a bad data detector and a novel secondary attack detector designed based on unique TPS characteristics. With intact position data of trains, our detection system can effectively detect the FDI attacks on trains' voltage and current measurements even if the attacker has full and accurate knowledge of the TPS, attack detection, and real-time system state. In particular, the GAD system features an adaptive mechanism that ensures low false positive and negative rates in detecting the attacks under noisy system measurements. Extensive simulations driven by realistic running profiles of trains verify that a TPS setup is vulnerable to the FDI attacks, but these attacks can be detected effectively by the proposed GAD while ensuring a low false positive rate.Comment: IEEE/IFIP DSN-2016 and ACM Trans. on Cyber-Physical System

Flywheel Energy Storage Systems for Rail

In current non-electrified rail systems there is a significant loss of energy during vehicle braking. The aim of this research has been to investigate the potential benefits of introducing onboard regenerative braking systems to rail vehicles. An overview of energy saving measures proposed within the rail industry is presented along with a review of different energy storage devices and systems developed for both rail and automotive applications. Advanced flywheels have been identified as a candidate energy storage device for rail applications, combining high specific power and energy. In order to assess the potential benefits of energy storage systems in rail vehicles, a computational model of a conventional regional diesel train has been developed. This has been used to define a base level of vehicle performance, and to investigate the effects of energy efficient control strategies focussing on the application of coasting prior to braking. The impact of these measures on both the requirements of an energy storage system and the potential benefits of a hybrid train have been assessed. A detailed study of a range of existing and novel mechanical flywheel transmissions has been performed. The interaction between the flywheel, transmission and vehicle is investigated using a novel application-independent analysis method which has been developed to characterise and compare the performance of different systems. The results of this analysis produce general ‘design tools’ for each flywheel transmission configuration, allowing appropriate system configurations and parameters to be identified for a particular application. More detailed computational models of the best performing systems have been developed and integrated with the conventional regional diesel train model. The performance of proposed flywheel hybrid regional trains has been assessed using realistic component losses and journey profiles, and the fuel saving relative to a conventional train quantified for a range of energy storage capacities and power-train control strategies

Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm

A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train

An approximate dynamic programming approach for designing train timetables

Traditional approaches to solving the train timetabling problem—the optimal allocation of when each train arrives and departs each station—have relied on Mixed-Integer Programming (MIP) approaches. We propose an alternative formulation for this problem based on the modeling and algorithmic framework of approximate dynamic programming. We present a Q-learning algorithm in order to tractably solve the high-dimensional problem. We compare the performance of several variants of this approach, including discretizing the state and the action spaces, and continuous function approximation with global basis functions. We demonstrate the algorithms on two railway system cases, one minimizing energy consumption subject to punctuality constraints, and one maximizing capacity subject to safety constraints. We demonstrate that the ADP algorithm converges rapidly to an optimal solution, and that the number of iterations required increases linearly in the size of the rail system, in contrast with MIP approaches whose computation time grows exponentially. We also show that an additional benefit to the ADP approach is the intuition gained from visualizing the Q-factor functions, which graphically capture the intuitive tradeoffs between efficiency and constraints in both examples

A review of modelling and optimisation methods applied to railways energy consumption

[EN] Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms.Martínez Fernández, P.; Villalba Sanchis, I.; Yepes, V.; Insa Franco, R. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production. 222:153-162. https://doi.org/10.1016/j.jclepro.2019.03.037S15316222

Cost-minimization predictive energy management of a postal-delivery fuel cell electric vehicle with intelligent battery State-of-Charge Planner

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is constructed to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43% and 7.30% in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123ms, less than the sampling time interval 1s, proving its potential of real-time applications