Energy management strategy to optimise regenerative braking in a hybrid dual‐mode locomotive

Mendoza, Diana Sofía; Solano, Javier; Boulon, Loïc

doi:10.1049/iet-est.2020.0070

Cited by 8 publications

(4 citation statements)

References 54 publications

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“…Yan et al [26] optimised the speed trajectory to minimise energy consumption and determined a hybrid system control strategy based on minimum hydrogen consumption. Another study [27] proposed a rule-based energy management strategy to maximise regenerative braking energy recovery. A different method [28] used the motor characteristic curve, supercapacitor capacity, maximum acceleration, and other information to obtain the braking process speed trajectory, ensuring that the supercapacitor captures more regenerative braking energy.…”

Section: Optimisation Problem Identificationmentioning

confidence: 99%

“…The f ′ (x ′ i, j ) from Equation ( 24) returns the optimised energy management-related variables and passes them to the optimisation function in Equation (27). This optimisation function denoted as [ G ′ best (x ′ i j ), f ′ best (x ′ i j )] minimises the instantaneous performance value at each iteration of the time and station counter.…”

Section: Optimisation Function (Non-convex Constraints)mentioning

confidence: 99%

See 1 more Smart Citation

Hybrid railway vehicle trajectory optimisation using a non‐convex function and evolutionary hybrid forecast algorithm

Din

Tian

Bukhari

et al. 2023

IET Intelligent Trans Sys

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This paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non‐linear programming solver with the highly efficient “Mayfly Algorithm” to address a non‐convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.

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Section: Optimisation Problem Identificationmentioning

confidence: 99%

Section: Optimisation Function (Non-convex Constraints)mentioning

confidence: 99%

Hybrid railway vehicle trajectory optimisation using a non‐convex function and evolutionary hybrid forecast algorithm

Din

Tian

Bukhari

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…In [23], Yan et al optimized the speed trajectory with the aim of achieving the minimum energy consumption, then determined the hybrid system control strategy based on minimum hydrogen consumption. In [24], a rule-based energy management strategy was proposed to maximize regenerative braking energy recovery. In [25], the braking process speed trajectory was obtained based on motor characteristic curve, supercapacitor capacity, maximum acceleration, and other information to ensure that the supercapacitor can obtain more regenerative braking energy, but this method does not consider the efficiency of the fuel cell.…”

Section: Introductionmentioning

confidence: 99%

Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model

Meng

Zhang

et al. 2022

Energies

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With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD.

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“…Given that such vehicles operate on fixed schedules, their duty cycle can be derived from vehicle and route data. [5][6][7] However, switcher locomotives do not operate on fixed schedules, and therefore require the synthesis of a driving cycle for power source sizing, [8][9][10][11][12] and energy management. 8,10 To that end, dieselelectric locomotives would typically be instrumented to record engine-generator power which would be used for power source selection and sizing.…”

Section: Introductionmentioning

confidence: 99%

The development of a representative multidimensional transient duty cycle for in-service switcher locomotives

Hegazi

Hoffrichter²,

Andrews

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part F:

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Switcher locomotives operate in railway yards where they shunt railcars and assemble trains. Shunting railcars requires frequent aggressive acceleration and deceleration events in order for the locomotive to push or pull railcars onto specific tracks. As a result, switcher locomotives rarely sustain tractive power for any significant period of time. Given that all switchers in North America rely on diesel-electric propulsion; the result is rapid and frequent transitions in engine power leading to a very low engine efficiency and high levels of emissions. Any attempt to quantify or remedy these issues will face a lack of a representative profile or test cycle. A locomotive duty cycle is a breakdown of time spent at each power level of the locomotive’s engine. A major drawback of current duty cycles is that they only account for steady power. Such cycles are not representative of real switcher locomotive operation. This paper presents a real-world transient duty cycle for switcher locomotives that accounts for the rapid power transitions and is argued to be more statistically representative of actual operations. The methodology adopted relies on real-time data collection, microtrip based trip segmentation, and a finite mixture model-based clustering algorithm. Measurements were collected on a EMD 16-645 GP9 locomotive. The duty cycle developed herein is representative of switching operations in Southern Railway of British Columbia’s New Westminster Yard as an example of the methodology which can be repeated in other cases as well.

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Energy management strategy to optimise regenerative braking in a hybrid dual‐mode locomotive

Cited by 8 publications

References 54 publications

Hybrid railway vehicle trajectory optimisation using a non‐convex function and evolutionary hybrid forecast algorithm

Hybrid railway vehicle trajectory optimisation using a non‐convex function and evolutionary hybrid forecast algorithm

Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model

The development of a representative multidimensional transient duty cycle for in-service switcher locomotives

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