Comparison of driving cycles obtained by the Micro-trips, Markov-chains and MWD-CP methods

Huertas, José Ignacio; Quirama, Luis F.; Giraldo, Michael; Diaz, Jenny L.

doi:10.5278/ijsepm.2554

Cited by 4 publications

(4 citation statements)

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“…It highlights research community efforts to address these challenges, classifying them as technical, economic, and sustainable. This journal has published a series of studies on electric vehicles, though typically from a more techno-economic perspective [41][42][43] or from an energy system perspective [44], thus the present article compliments the existing body well. In addition, the journal has a long track-record on studies on India.…”

Section: This Issuementioning

confidence: 71%

Ten years of sustainable energy planning and management

Østergaard,

Lund,

Johannsen

et al. 2024

IJSEPM

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With this volume 40 the International Journal of Sustainable Energy Planning and Management can celebrate its 10 years anniversary. In this editorial we take a closer look at the journals impact over its first 10 years of existence and highlight the articles that have made the largest impacts in the scientific community from a citation perspective. The topic of the journal – sustainable energy planning and management – has not decreased in importance over the first ten years of the journal’s existence – and fortunately the results published in the journal has garnered interest and the impact of the journal has significantly increased. In terms of citation, the journals 275 published articles, has received 1484 citations as per March 2024 and nine articles have 40 or more citations in Scopus. CiteScore ranks the journal in the top 10% out of 700+ journals within the topic of Geography, Planning and Development.

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Section: This Issuementioning

confidence: 71%

Ten years of sustainable energy planning and management

Østergaard,

Lund,

Johannsen

et al. 2024

IJSEPM

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“…The best method for building DCs is the micro trip (MT) method [2,27]. We created a DC utilizing the MT approach using the same data used for the NN prediction method to demonstrate the significance of the methodology provided in this study.…”

Section: Micro Trip Methods Of DC Constructionmentioning

confidence: 99%

“…Finally, we established that a DC represents a driving pattern when the characteristic parameters of the candidate DC are approximately similar to the target parameters. Thus, the degree of representativeness of a candidate DC was evaluated as the relative difference between paired CPs according to Equation (4) [27].…”

Section: Micro Trip Methods Of DC Constructionmentioning

confidence: 99%

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

et al. 2022

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This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC’s development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method.

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“…A MCM describes the possible transitions between a finite set of discrete states using a probability matrix. MCMs have been widely used in representative or surrogate vehicle cycle generation [29]- [31]. In this work they are used to extract transition probabilities from the original raw drive cycle data and in reconstructing cycles, this information is then used to connect the selected micro-trajectories.…”

Section: H Cdr Step 7: Connecting Selected Micro-trajectories Using A...mentioning

confidence: 99%

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

Yang,

Mason,

Winward

et al. 2024

IEEE Access

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Transient test cycles are an essential part of the development of both on and off-highway vehicles and machines. These cycles are used to test and validate the dynamic operation and performance of systems, and they also form part of regulations such as Real-world Driving Emissions (RDE). A common approach to generating a transient cycle is to replicate recorded real-world transient operation. However, the dynamics of transient operation can vary significantly based on factors such as the operating environment, the use case, the user, and the type of vehicle or machine. The feasible duration of a transient test cycle is also often limited by development time and cost. Therefore, there is a need for a method to synthesize transient test cycles from real-world data in order to replicate a wide range of feasible dynamic operation in the shortest possible time. This paper proposes an innovative transient Cycle Decomposition and Reduction (CDR) method that generates a shortened representative driving cycle in speed-torque two-dimensional space. The steps include identifying micro-trajectories using critical points (e.g. minimum torque), identifying features of micro-trajectories, grouping similar micro-trajectories using k-means clustering and Gaussian process models, and using Markov chain models to efficiently splice together the micro-transients to form a new reduced cycle. The method is flexible and can be used to generate reduced transient cycles for both on-road and off-road applications. The proposed CDR method is demonstrated by using it to perturb a system and collect data for the identification of nonlinear dynamic system models. A 30-minute transient cycle is reduced to a 200-second representative cycle, which is used to train a Neuro-Fuzzy NOx emission model. The results show that this model can accurately predict NOx emissions behaviour over the original transient cycle. The proposed CDR method is easy to expand to higher dimensions and can be applied in a wide range of applications. Additionally, the information extracted during the process has useful physical meaning and can be further utilised, for example in driving pattern recognition, vehicle diagnosis, and autonomous vehicles. INDEX TERMS Clustering, Cycle decomposition, Markov chain model, Nonlinear dynamic modelling.

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Comparison of driving cycles obtained by the Micro-trips, Markov-chains and MWD-CP methods

Cited by 4 publications

References 0 publications

Ten years of sustainable energy planning and management

Ten years of sustainable energy planning and management

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling

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