Energy Saving Speed and Charge/Discharge Control of a Railway Vehicle with On‐board Energy Storage by Means of an Optimization Model

Miyatake, Masafumi; Matsuda, K.

doi:10.1002/tee.20479

Cited by 83 publications

(44 citation statements)

References 11 publications

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“…p is a variable that represents the power supply mode in phase r, which is defined as Equation (27). C bt are equivalent energy consumption of the catenary, the SCs and the BTs, respectively.…”

Section: Multiple Phases Integrated Optimization Modelmentioning

confidence: 99%

Integrated Optimization of Speed Profiles and Power Split for a Tram with Hybrid Energy Storage Systems on a Signalized Route

et al. 2018

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Abstract:A tram with on-board hybrid energy storage systems based on batteries and supercapacitors is a new option for the urban traffic system. This configuration enables the tram to operate in both catenary zones and catenary-free zones, and the storage of regenerative braking energy for later usage. This paper presents a multiple phases integrated optimization (MPIO) method for the coordination of speed profiles and power split considering the signal control strategy. The objective is to minimize the equivalent total energy consumption of all the power sources, which includes both the energy from the traction substation and energy storage systems. The constraints contain running time, variable gradients and curves, speed limits, power balance and signal time at some intersections. The integrated optimization problem is formulated as a multiple phases model based on the characters of the signalized route. An integrated calculation framework, using hp-adaptive pseudospectral method, is proposed for the integrated optimization problem. The effectiveness of the method is verified under fixed time signal (FTS) control strategy and tram priority signal (TPS) control strategy. Illustrative results show that this method can be successfully applied for trams with hybrid energy storage systems to improve their energy efficiency.

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Section: Multiple Phases Integrated Optimization Modelmentioning

confidence: 99%

Integrated Optimization of Speed Profiles and Power Split for a Tram with Hybrid Energy Storage Systems on a Signalized Route

et al. 2018

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“…In addition, Miyatake et al [14,15], Ho et al [16], and Sheu et al [17] performed productive research on mathematical approaches and optimal theory. Mathematical methods are based on accurate models, which are sometimes difficult to obtain.…”

Section: Mathematical Methods and Optimal Theorymentioning

confidence: 99%

“…Step 1: According to the given distance, deduce the minimal running time and its corresponding speed profile, which follows Equation (14). This means that the train runs with full power and then switches from acceleration mode directly to coasting mode, as demonstrated by the red curve in Figure 8.…”

Section: Influence Of Running Time On Energy Consumptionmentioning

confidence: 99%

“…Here, the CBSP should be determined according to Equation (14). Regarding the online optimization, however, flexible CBSP adjustments can be made within the optimal switching region determined by Equations (19) and (20) to fully utilize the running time and to stop precisely at train stations without a huge increase in computational time.…”

Section: Coasting and Braking Modesmentioning

confidence: 99%

“…In the following simulation, on a 20 km level track, the different running times and corresponding optimal speed profiles are shown in Figure 8. Note that the relationship between the value of the holding speed and the CBSP is derived based on Equation (14).…”

Section: Influence Of Running Time On Energy Consumptionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Yang

Jia

et al. 2016

Energies

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Speed profile optimization plays an important role in optimal train control. Considering the characteristics of an electrical locomotive with regenerative braking, this paper proposes a new algorithm for target speed profile approximation. This paper makes the following three contributions: First, it proves that under a certain calculation precision, there is an optimal coast-brake switching region-not just a point where the train should be switched from coasting mode to braking mode. This is very useful in engineering applications. Second, the paper analyzes the influence of regenerative braking on the optimal coast-brake switching region and proposes an approximate linear relationship between the optimal switching region and the regeneration efficiency. Third, the paper presents an average speed equivalent algorithm for local speed profile optimization in steep sections. In addition, this paper simplifies the proof of the optimality of smooth running on a non-steep track in two steps. The effects on energy consumption from two important factors (optimal coast and running time) are systematically analyzed. Extensive simulations verify these points of view and demonstrate that the proposed approximation approach is computationally efficient and achieves sufficient engineering accuracy.

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Charge/discharge control of wayside batteries via reinforcement learning for energy‐conservation in electrified railway systems

Yoshida

Arai

Kobayashi

et al. 2021

Electrical Engineering Japan

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The effective utilization of regenerative power generated by trains has attracted the attention of engineers due to its promising potential in energy conservation for electrified railways. Charge control by wayside battery batteries is an effective method of utilizing this regenerative power. Wayside batteries requires saving energy by utilizing the minimum storage capacity of energy storage devices. However, because current control policies are rule‐based, based on human empirical knowledge, it is difficult to decide the rules appropriately considering the battery's state of charge. Therefore, in this paper, we introduce reinforcement learning with an actor‐critic algorithm to acquire an effective control policy, which had been previously difficult to derive as rules using experts’ knowledge. The proposed algorithm, which can autonomously learn the control policy, stabilizes the balance of power supply and demand. Through several computational simulations, we demonstrate that the proposed method exhibits a superior performance compared to existing ones.

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Energy Saving Speed and Charge/Discharge Control of a Railway Vehicle with On‐board Energy Storage by Means of an Optimization Model

Cited by 83 publications

References 11 publications

Integrated Optimization of Speed Profiles and Power Split for a Tram with Hybrid Energy Storage Systems on a Signalized Route

Integrated Optimization of Speed Profiles and Power Split for a Tram with Hybrid Energy Storage Systems on a Signalized Route

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Charge/discharge control of wayside batteries via reinforcement learning for energy‐conservation in electrified railway systems

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