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Integrated Optimization of Charging Infrastructure, Renewable Energy and Battery Electric Bus Scheduling Subject to Power Distribution Network Constraints

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SSRN

SSRN 2024年

作者： Najafi, Arsalan Gao, Kun Parishwad, Omkar Tsaousoglou, Georgios Jin, Sheng Yi, Wen Department of Architecture and Civil Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Department of Building and Real Estate The Hong Kong Polytechnic University Hung Hom Hong Kong

The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. However, it also poses challenges to the coupled electric transport and energy systems, particularly concerning the safety and efficiency of Power Distribution Networks (PDN). This study introduces a comprehensive BEB-PDN framework designed for the joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources, with a specific focus on their impacts on PDN. The complexity of the proposed model is primarily due to its extensive decision variables and nonlinear constraints. To address this, a decomposition approach is employed, linearizing the problem and dividing it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that without renewable energy integration, high charging demands from BEBs can lead to PDN safety violations, such as significant voltage drops at certain nodes. The incorporation of renewable energy sources with battery energy storage in the BEB transit system is shown to enhance the PDN's stability and safety while catering to the system's spatiotemporal charging demands. This study offers an optimization framework for the integrated planning of charging infrastructure, BEB systems, and renewable energy, providing empirical insights into their potential benefits. © 2024, The Authors. All rights reserved.

关键词： Electric buses

On the Experiences of Adopting Automated Data Validation in an Industrial Machine Learning Project

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On the Experiences of Adopting Automated Data Validation in ...

IEEE/ACM International Conference on Software engineering: Software engineering in Practice Track (ICSE-SEIP)

作者： Lucy Ellen Lwakatare Ellinor Rånge Ivica Crnkovic Jan Bosch Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Ericsson Gothenburg Sweden

Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training MLmodels using bad data, research and industrial practice suggest incorporating a data validation process and tool in the ML system development process. Aim: The study investigates the adoption of a data validation process and tool in industrial ML projects. The data validation process demands significant engineering resources for tool development and maintenance. Thus, it is important to identify the best practices for their adoption especially by development teams that are in the early phases of deploying ML-enabled software systems. Method: Action research was conducted at a large-software intensive organization in telecommunications, specifically within the analytics R&D organization for an ML use case of classifying faults from returned hardware telecommunication devices. Results: Based on the evaluation results and learning from our action research, we identified three best practices, three benefits, and two barriers to adopting the data validation process and tool in ML projects. We also propose a data validation framework(DVF) for systematizing the adoption of a data validation process. Conclusions: The results show that adopting a data validation process and tool in ML projects is an effective approach to testing ML-enabled software systems. It requires having an overview of the level of data (feature, dataset, cross-dataset, data stream) at which certain data quality tests can be applied.

关键词： Organizations Machine learning Tools Software systems Telecommunications Object recognition Software engineering

Epitaxial growth and characterization of (001) [NiFe/M]20(M = Cu, CuPt and Pt) superlattices

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arXiv 2023年

作者： Kateb, Movaffaq Gudmundsson, Jon Tomas Ingvarsson, Snorri Science Institute University of Iceland Dunhaga 3 ReykjavikIS-107 Iceland Condensed Matter and Materials Theory Division Department of Physics Chalmers University of Technology GothenburgSE-412 96 Sweden Space and Plasma Physics School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44 Sweden

We present optimization of [(15Å) Ni80Fe20/(5Å) M]20single crystal multilayers on (001) MgO, with M being Cu, Cu50Pt50and Pt. These superlattices were characterized by high resolution X-ray reflectivity (XRR) and diffraction (XRD) as well as polar mapping of important crystal planes. It is shown that cube on cube epitaxial relationship can be obtained when depositing at substrate temperature of 100 °C regardless of the lattice mismatch (5% and 14% for Cu and Pt, respectively). At lower substrate temperatures poly-crystalline multilayers were obtained while at higher substrate temperatures {111} planes appear at ∼10° off normal to the film plane. It is also shown that as the epitaxial strain increases, the easy magnetization axis rotates towards the direction that previously was assumed to be harder, i.e. from [110] to [100], and eventually further increase in the strain makes the magnetic hysteresis loops isotropic in the film plane. Higher epitaxial strain is also accompanied with increased coercivity values. Thus, the effect of epitaxial strain on the magnetocrystalline anisotropy is much larger than what was observed previously in similar, but polycrystalline samples with uniaxial anisotropy (Kateb et al. 2021). © 2023, CC BY.

关键词： Binary alloys

SHIFT OF PAIRWISE SIMILARITIES FOR DATA CLUSTERING

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arXiv 2021年

作者： Chehreghani, Morteza Haghir Department of Computer Science and Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden

Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (e.g., the size or the degree of the clusters), in order to yield a more balanced partitioning. We, instead, investigate adding such regularizations to the original cost function. We first consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities. This leads to shifting (adaptively) the pairwise similarities which might make some of them negative. We then study the connection of this method to Correlation Clustering and then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem. In the following, we investigate the shift of pairwise similarities on some common clustering methods, and finally, we demonstrate the superior performance of the method by extensive experiments on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Cost functions

Lost options commitment: how short-term policies affect long-term scope of action

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Oxford Open Climate Change 2024年第1期4卷

作者： Montero, Marina Martínez Brede, Nuria Couplet, Victor Crucifix, Michel Botta, Nicola Wieners, Claudia Earth and Life Institute UCLouvain Louvain-la-Neuve 1348 Belgium Department of Computer Science University of Potsdam Potsdam 14476 Germany Potsdam Institute for Climate Impact Research Potsdam 14473 Germany Department of Computer Science and Engineering Chalmers University of Technology Gothenburg 41296 Sweden Institute for Marine and Atmospheric Research Utrecht University Utrecht 3584 Netherlands

We propose to explore the sustainability of climate policies based on a novel commitment metric. This metric allows to quantify how future generations’ scope of action is affected by short-term climate policy. In an example application, we show that following a moderate emission scenario like SSP2-4.5 will commit future generations to heavily rely on carbon dioxide removal or/and solar radiation modification to avoid unmanageable sea level rise. © 2024 Oxford university Press. All rights reserved.

关键词： commitment generational fairness lost options sustainability vulnerability

An architecture for enabling A/B experiments in automotive embedded software

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arXiv 2021年

作者： Liu, Yuchu Bosch, Jan Olsson, Helena Holmström Lantz, Jonn Volvo Cars Gothenburg Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Computer Science and Media Technology Malmö University Malmö Sweden

A/B experimentation is a known technique for data-driven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique is not without challenge. In this paper, we present an architecture to enable A/B testing in automotive embedded software. The design addresses challenges that are unique to the automotive industry in a systematic fashion. Going from hypothesis to practice, our architecture was also applied in practice for running online experiments on a considerable scale. Furthermore, a case study approach was used to compare our proposal with state-of-practice in the automotive industry. We found our architecture design to be relevant and applicable in the efforts of adopting continuous A/B experiments in automotive embedded software. © 2021, CC BY.

关键词： Software testing

Bayesian propensity score matching in automotive embedded software engineering

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Bayesian propensity score matching in automotive embedded so...

Asia-Pacific Conference on Software engineering

作者： Yuchu Liu David Issa Mattos Jan Bosch Helena Holmström Olsson Jonn Lantz Volvo Cars Gothenburg Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Computer Science and Media Technology Malmö University Malmö Sweden

ISBN: (纸本)9781665437851

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.

关键词： Ethics Companies Sensor phenomena and characterization Software Bayes methods Safety Automotive engineering

Bayesian propensity score matching in automotive embedded software engineering

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arXiv 2021年

作者： Liu, Yuchu Mattos, David Issa Bosch, Jan Olsson, Helena Holmström Lantz, Jonn Volvo Cars Gothenburg Sweden Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Computer Science and Media Technology Malmö University Malmö Sweden

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc= 1100) fleet of cars using the current software and a small treatment fleet (Nt= 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples. © 2021, CC BY.

关键词： Software design

Size matters? or not: A/B testing with limited sample in automotive embedded software

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arXiv 2021年

A/B testing is gaining attention in the automotive sector as a promising tool to measure causal effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we present a method for designing balanced control and treatment groups so that sound conclusions can be drawn from experiments with considerably small sample sizes. While the Balance Match Weighted method has been used in other domains such as medicine, this is the first paper to apply and evaluate it in the context of software development. Furthermore, we describe the Balance Match Weighted method in detail and we conduct a case study together with an automotive manufacturer to apply the group design method in a fleet of vehicles. Finally, we present our case study in the automotive software engineering domain, as well as a discussion on the benefits and limitations of the A/B group design method. © 2021, CC BY.

关键词： Software design