Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain Process

Isaac, Nithin; Saha, Akshay Kumar

doi:10.1016/j.rser.2021.110761

Cited by 20 publications

(12 citation statements)

References 23 publications

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“…In addressing challenges associated with HFVs, predicting trip-related variables, such as arrival time, destination, and refuelling behaviour, plays a crucial role [9,10]. Limited studies focus on HFV-related refuelling behaviour [2,[11][12][13]. Most studies consider factors such as energy costs, consumer preferences, price sensitivity to fuel, fuel efficiency, and socio-economic and technological factors [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Isaac,

Saha

2024

Sustainability

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Efficiently predicting and understanding refuelling patterns in the context of HFVs is paramount for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation. This study evaluates several supervised machine learning methodologies for predicting the refuelling behaviour of HFVs. The LightGBM model emerged as the most effective predictive model due to its ability to handle time series and seasonal data. The selected model integrates various input variables, encompassing refuelling metrics, day of the week, and weather conditions (e.g., temperature, precipitation), to capture intricate patterns and relationships within the data set. Empirical testing and validation against real-world refuelling data underscore the efficacy of the LightGBM model, demonstrating a minimal deviation from actual data given limited data and thereby showcasing its potential to offer valuable insights to fuelling station operators, vehicle manufacturers, and policymakers. Overall, this study highlights the potential of sustainable predictive modelling for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation in the context of HFVs.

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Section: Introductionmentioning

confidence: 99%

“…• An HFV is assumed to be able to travel 320 km before requiring refuelling. This is based on the typical trip range taken from the data set to comply with the typical range a hydrogen vehicle would need to travel before requiring refuelling [11,18]. • An HFV driver refuels when the refuelling tank is at 30% capacity.…”

mentioning

confidence: 99%

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Isaac,

Saha

2024

Sustainability

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“…In the literature, there are numerous studies on modelling transport processes using Markov or semi-Markov processes [21,22]. Publications often deal with the evaluation of vehicle readiness and reliability [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic operation model for readiness assessment of small wind turbines based on Markov theory

Zalewska‐Lesiak,

Małachowski,

Szkutnik‐Rogoż

2023

IET Renewable Power Gen

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Wind energy is now one of the most important alternative sources of renewable energy. The development of wind power generation is a prerequisite to meet environmental and sustainable development requirements while achieving energy security goals. The condition for the effectiveness of the proper operation of a wind power plant is its ability to perform the intended functions, which can be expressed by the coefficient of technical readiness. This article presents an original analysis of the readiness of small wind turbines. As part of the research, an original stochastic operation model was developed based on the application of the Markov process theory. Based on the collected data, a 3‐state phase space of the studied process was identified. Various analyses were carried out to calculate the operating coefficients of wind power plants. The ergodic (boundary) probabilities were calculated using three methods. The conditional probabilities were then determined based on the initial distribution. The resulting readiness factors, not exceeding 30%, indicate the low operational efficiency of the small wind turbines in question. Furthermore, an analysis of the sensitivity of the performance factors was carried out for the analysed objects.

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“…As such, several studies have delved into understanding the transition to HFVs and the challenges in predicting refueling behavior with limited data [24]. Since the accurate modeling of refueling patterns is essential for optimizing the placement and capacity of refueling stations/infrastructure, studies such as those in [18,[24][25][26] have contributed by studying and designing models that predict the refueling patterns of HFV users. This proves especially valuable since the location of hydrogen refueling stations and their layout could contribute to improving the adoption of HFVs [27,28].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Isaac,

Saha

2023

Sustainability

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This paper presents an analysis of two methodologies that can be used to predict refueling behavior. Both models aim to provide insights into hydrogen-fuel vehicle users’ refueling patterns and behaviors. The first model leverages probabilistic transitions between refueling states to simulate and predict the refueling behavior of hydrogen vehicle users. In contrast, the GP-1 model employs Gaussian processes to capture the underlying patterns and uncertainties in hydrogen-fuel vehicle refueling behavior, taking into consideration additional factors such as weather conditions and the time of the year. The model demonstrates statistical significance and accuracy in predicting trips while identifying the insignificance of precipitation and high ambient temperatures. The methodologies, findings, strengths, and limitations of the two models were tested and compared to identify their relevant contributions. By contrasting their methodologies and evaluating their predictive performance, using performance metrics such as accuracy, precision, and recall values, this study provides valuable insights into the strengths and limitations of each approach. Limitations include assuming a stationary refueling process and excluding external factors and limitations related to data availability, as well as the absence of a specific focus on hydrogen-fuel vehicles. By understanding the differences and similarities between these two models, this paper aims to provide a unique perspective on gaps and further requirements for accurate prediction and modeling of refueling behavior to guide policymakers, infrastructure planners, and stakeholders in making informed decisions regarding the design and optimization of hydrogen refueling infrastructure.

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Analysis of refueling behavior of hydrogen fuel vehicles through a stochastic model using Markov Chain Process

Cited by 20 publications

References 23 publications

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Stochastic operation model for readiness assessment of small wind turbines based on Markov theory

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

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