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Catenary-free trams powered by on-board supercapacitor systems require high charging power from tram stations along the line. Since a shared electric grid is suffering from power superimposition when several trams charge at the same time, we propose to install stationary energy storage systems (SESSs) for power supply network to downsize charging equipment and reduce operational cost of the electric grid. To evaluate the trade-off between component cost and operational cost, an optimisation problem, which integrates type selection, sizing, energy management and different installation configurations of the SESSs, is introduced and formulated in terms of an annual cost. Disciplined convex modelling is applied to obtain a computationally tractable solution for a case study on an existing line in China. Results show that the optimal solution may reduce tramline cost by 11.48%.

To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability.

This paper introduces an optimal sizing method for a catenary-free tram, in which both on-board energy storage systems and charging infrastructures are considered. To quantitatively analyze the trade-off between available charging time and economic operation, a daily cost function containing a whole life-time cost of energy storage and an expense of energy supplies is formulated for the optimal sizing problem. A mixed particle swarm optimization algorithm is utilized to find optimal solutions for three schemes: (1) ultracapacitors storage systems with fast-charging at each station, (2) battery storage systems with slow-charging at starting and final stations, (3) battery storage systems with fast-swapping at swapping station . A case study on an existing catenary-free tramline in China is applied to verify the effectiveness of the proposed method. Results show that a daily-cost reduction over 30% and a weight reduction over 40% can be achieved by scheme 2, and a cost saving of 34.23% and a weight reduction of 32.46% can be obtained by scheme 3.

This paper introduces the concept of fast-swap charging (FSC), an operation mode designed to improve the operating cost-efficiency relation in on-board-powered light rail (LR) networks. The main concept is to swap the depleted energy storage units for fresh, pre-charged units in every cycle after the LR vehicle arrives at its terminus. The proposed operation mode allows the empty energy storage components to be charged at low electricity-demand periods, such as at night, so that a cheaper kWh tariff is accessed. Energy efficiency models capable of assessing the energy efficiency are developed as a framework for practical case studies. Finally, a case study illustrating the effects of different operation modes on the selection and optimal sizing of energy storage components, and the energy efficiency of the Zhuhai route, China, is included. The results show that the proposed FSC outperform the counterparts in term of average operating cost per year while obtaining a similar efficiency.

In order to reduce the energy consumption and operating costs, energy storage technologies have become a hot research in the field of rail transit. The energy storage system (ESS), which acts as a power source can output energy during the vehicle traction and absorb energy when the vehicle is braking. This study investigates a hybrid energy storage system (HESS) experimental platform based on dSPACE for preliminary studies and post-verification of the ESS. The topology uses two bi-directional dc-dc converters parallel and another bi-directional dc-dc to simulate the vehicle traction system. The HESS has four operating modes. The experiment result proves the feasibility of the control strategies used for power distribution between the battery bank and the ultracapacitor bank.

Modern tramway is widely used in metropolises all over the world for the economic and environmental advantages in recent years. The tram vehicles with onboard energy storage system is one of the most popular tramway mode, due to running without the catenary and safety. However, the charging power and distribution capacity demand have been a great challenge to the distribution network, especially in the heavy load area. This paper focused on the tram line with on-board surpercapacitors vehicles, presented an optimal charging strategy based on the characteristic analysis of the charging load and vehicles of a realistic tram line. And then, a model of stationary energy storage system was proposed to decrease the demand of distribution capacity further. A case study of an operated line was carried out, and the results show that the distribution capacity demand was decreased by 90.26% than former. The cost of basic electricity was substantially reduced at the same time.

For energy storage based urban rail vehicles, development of representative driving cycles is of great significance in the performance test and optimal sizing of energy storage systems. This paper proposes station-based microtrip analysis method to synthesize driving cycles for urban rail vehicles. Firstly, the data are segmented into microtrips by stations and then categorized depending on running time. Subsequntly, seventeen kinematics-related characteristic parameters are picked to describe each microtrip followed by the calculation of square Euclidean distances (referred as distance hereinafter) between microtrips with two methods. The first method employs the selected characteristic parameters to compute the distances while the second method adopts the components extracted by principal component analysis for calculation. On the principle of minimizing the sum of distance to the other microtrips, the candidate microtrip of each station which has the closest match to the real-world driving conditions is selected. Later the candidate of each station is spliced station by station and representative driving cycles can be achieved. Finally, a comparative analysis is made between the generated cycles and raw data to evaluate the validity of synthesized driving cycles. The results demonstrate that the driving cycles synthesized by these two methods are highly consistent with the original data in terms of statistical parameters and velocity probability distribution. Furthermore, with respect to energy economy, the relative error of driving cycles generated by PCA is within 2%, less than that obtained by characteristic parameters which is within 9%.

To realize economical operation of a catenary-free tramline, we propose installing a stationary energy storage system (SESS) to assist the electric grid for trams charging. As the tram operation may not be fully aligned with a predetermined timetable, an economical coordination of the electric grid and the SESS under uncertain charging demands is investigated. To this end, a chance-constrained program is formulated where demanded charging of the tramline is satisfied in a probabilistic sense. The introduced chance-constrained program is translated into a robust and deterministic mixed-integer second-order cone program (MISOCP) by first saturating charging power to a stochastic upper limit and then prolonging charging periods until entire energy is delivered for all charging scenarios that are being investigated. A case study for the Haizhu line in Guangzhou, China, shows that a cost–benefit of 13.70% can be obtained by installing an SESS when charging power is fully delivered for all scenarios, while a 28.47% cost-saving can be achieved when charging power is delivered 99% of the time.

© 1986-2012 IEEE. The implementation of multistep direct model predictive control (MPC) for DC-DC boost converters overcomes the well-known issue of nonminimum phase behavior. However, it can lead to a high computational burden depending on the prediction horizon length. In this paper, a simple and computationally efficient MPC method for DC-DC boost converters is proposed. The key novelty of the presented control strategy lies in the way dynamic references are handled. The control strategy is capable of providing suitable references for the inductor current and the output voltage, without requiring additional control loops. Moreover, this reference design allows the predictive controller to be implemented with a single-step prediction horizon. Thus, a significant reduction in the required real-time calculations executed in the control hardware is achieved. To obtain constant switching frequency, the power switch commutation instants within a sampling period are considered as control inputs. Therefore, the predictive controller is formulated as a continuous control set MPC. Additionally, the proposed formulation is able to deal with different operation modes of the converter without changing the controller structure. Finally, an observer is used to dynamically modify the reference to provide robustness to system parameter uncertainties. Simulation and experimental results show an accurate tracking of dynamic inductor current and output voltage references, while respecting the restrictions on maximum inductor current levels of the converter.

A single-objective optimization energy management strategy (EMS) for an onboard hybrid energy storage system (HESS) for light rail (LR) vehicles is proposed. The HESS uses batteries and supercapacitors (SCs). The main objective of the proposed optimization is to reduce the battery and SC losses while maintaining the SC state of charge (SOC) within specific limits based on the distance between consecutive LR stations. To do this, a series of optimized SOC limits is used to prevent the SC from becoming exhausted prematurely instead of the standard SC SOC penalty term in the cost function. Meanwhile, a rule-based EMS (RB-EMS) is used to give the SCs charging priority over the batteries when the vehicle is braking. Moreover, a simplified method for the optimization is proposed to reduce the computational burden. Simulation and experimental results for the proposed EMS and a standard SC SOC penalty-based cost function optimization are provided to evaluate losses. As a result, it is shown that the proposed EMS, compared with standard SC SOC penalty-based cost function optimization, decreases losses and prevents the SOC from reach the discharging limits.

For the estimation problem of vehicle state and road adhesion coefficient in driving, the estimation algorithm of vehicle state and road adhesion coefficient were studied in this paper based on double cubature Kalman filter. The 3-DOF nonlinear vehicle estimation model with Dugoff tire model was established. The vehicle driving state estimator and tire-road friction coefficient estimator based on double cubature Kalman filter were designed. The estimators contact with each other in the process of estimation and forms a closed loop feedback to estimate the vehicle state and road adhesion coefficient timely and accurately. Selecting the typical working condition, the algorithm was verified by driving simulator experiments in the loop. The results showed that estimation algorithm based on double cubature Kalman filter can more accurately estimate the vehicle state and road adhesion coefficient than estimation algorithm based on extend Kalman filter.

Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical data collected in the experiments are used to train the BP network model, which reveals a test error of 10−4. With the input of continuous SOC regions and discharge currents, continuous-time efficiency can be estimated by the trained BP network model. The estimated and simulated result is proven to be consistent with the experimental results.

The rapid development of rail transit brings a great increment of electricity, in order to reduce the energy consumption and the operating cost, energy storage technology has become a hot focus in the field of rail transit. In this paper, the power loss model of hybrid energy storage system (HESS) is established from the point of efficiency improving, which includes the loss of lithium-ion battery, ultracapacitor and bi-directional DC/DC converter. In order to make the best use of HESS, an optimal equation with two objectives including the minimization of power loss and the stable operation of energy storage elements, is derived. Moreover, an efficiency optimization method with adaptive adjustment of weight coefficients is proposed after fuzzy processing. Simulation results carried out on Matlab show that compared with the threshold power management strategy, the total power loss of the HESS can be effectively reduced and the energy efficiency in a single scale tramway can be increased by the proposed control strategy.

Fossil Fuel is one of the main pillar of resources in current society. Seventy-four percent of the total global consumption depends on it, including coal, petroleum etc. Nevertheless, a great number of countries start to focus more on renewable energy due to the current severe contamination and low efficiency. Development is restrained by many factors, such as low efficiency of light conservation and large cost of system establishment. Moreover, the general public usually expects much more from solar energy applications. Applying to this market demand, the invention of energy-saving cars, which utilise solar energy has become a key resource direction. Through design of optimal angle modelling of silicon photocells, Drag Coefficient and MPPT (Maximum Power Point Tracking), a new solution of utilising highly efficient solar energy automobiles is obtained.