| 1. |
- Roupé, Mattias, 1975, et al.
(författare)
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Virtuell produktionsplanering med BIM och visualisering
- 2014
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Denna rapport presenterar resultatet från en genomförd förstudie som undersöker möjligheten med en ny planeringsmetodik dvs. Virtuell Produktions Planering (VPP). Avsikten med planeringsmetodiken är att få olika delaktiga aktörer i projektet att samarbeta och utnyttja varandras kunskap och erfarenheter genom att gemensamt virtuellt bygga och planera produktionen av byggobjektet innan det verkställs i verklig produktion. VPP utnyttjar 3D-visualisering (interaktiv visualisering/Virtual Reality) och BIM-modellen under denna process. Arbetsordningen för denna planeringsprocess är följande:1.BIM-modell och dess komponenter från projektering grupperas efter yrkesgrupper dvs. vem som har ansvaret att installera/bygga dessa under byggproduktionen.2.De olika ansvariga yrkesgrupperna får sina tilldelade komponenter som de tid- och resurssätter. 3.Modellen och dess komponenter byggs sedan ihop i rätt ordning gemensamt av de olika involverade yrkesgrupperna och resultatet är en tidplan som visualiseras i 4D.Vad denna förstudie rapporterar så finns stora möjligheter till en effektivare planeringsprocess med VPP. De framtagna VPP-prototyperna under förstudien har visat att det är tekniskt möjligt att implementera ett VPP-system. Vidare visade observationsstudien från en strukturplaneringsworkshop på utvecklingspotential av den befintliga processen. Observationsstudien visade även att denna typ av workshop ger bättre: • granskning och identifiering av felkällor i projekteringen• testning och granskning av byggbarhet av projektet• sammanförande av kunskap och erfarenheter från projektering och produktion• laganda och Team Building för projektet• gemensam målbild och målplanI slutändan skulle VPP med största sannolikhet tillföra ökad tillförlitlighet och effektivitet i produktionen.
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| 2. |
- Viklund Tallgren, Mikael, 1983, et al.
(författare)
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A BIM-supported framework for enhancing joint planning in construction
- 2015
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Ingår i: Beetz, J., van Berlo, L., Hartmann, T., Amor, R., Proceedings of the 32nd CIB W78 Conference 2015, 27th-29th 2015, Eindhoven, The Netherlands. ; , s. 696-705
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Konferensbidrag (refereegranskat)abstract
- Modern construction planning philosophies, such as The Last Planner System (LPS), stipulate thatthose performing the work on site should participate in the planning. In this paper we describe andanalyze a variant of LPS that is currently in use in a Scandinavian construction company andexplore the possibilities to enhance the process by integrating the use of Building InformationModels (BIM). The study is based on the observations of three planning workshops together with aset of semi-structured interviews. Based on the analysis and findings from the workshops weoutline the theoretical design of a Computer-Supported Cooperative Work-System (CSCW) thatintegrates BIM more closely in the method.
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| 3. |
- Andersson, Bengt-Åke
(författare)
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Circulating Biomarkers in Patients with Head and Neck Cancer and the Influence of Cigarette Smoking
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Head and neck cancer (HNC) is a collective name for heterogeneous tumors located in the head and neck regions for which smoking, alcohol and human papillomavirus (HPV) are documented risk factors. The survival of HNC patients has only improved marginally during the last decade. The most important prognostic factors are tumor size, local spread and distant metastases, tumor node metastasis (TNM) staging. Prognostic biomarkers are needed as a complement to TNM staging.The aim for this thesis was to investigate rapid and low cost blood based biomarkers which could indicate the risk of HNC, recurrence of the disease or the survival of HNC patients. Furthermore, the aim was to examine how cigarette smoking influences the levels of biomarkers.In paper I, a possible role of plasma cytokines or proteins associated with immune response or inflammation, as biomarkers for the survival of HNC patients was investigated. Higher levels of C-reactive protein (CRP) and tumor necrosis factor alpha (TNF-α) were detected in plasma of the patients compared with the levels in the controls. The elevated levels of these two biomarkers detected in patients were associated with decreased survival.In paper II, the influence of 45 single nucleotide polymorphisms (SNPs) located in 41 genes associated with cell cycle progression, cell death, DNA repair or immune response on cancer risk, tumor recurrence and survival in HNC patients were investigated. SNPs in immune response genes were associated with risk for HNC, an elevated risk for recurrence and a decreased survival in HNC patients.In paper III, the influence of cigarette smoking on levels of inflammatory cells, proteins or cytokines/chemokines, microRNAs (miRNAs) and SNPs was analysed in healthy smokers and non-smokers. Higher levels of total white blood cells (WBCs), neutrophils, monocytes, lymphocytes, neutrophil to lymphocyte ratio (NLR), CRP, monocyte chemoattractant protein- 1 (MCP-1) and interferon gamma (IFN-γ) were detected in smokers compared to non-smokers and indicate an inflammatory response. Also, a lower level of oncomiRNA miR-21was detected in smokers. This alteration, in combination with the elevated levels of IFN-γ in smokers could be a protective response to cigarette smoke. The higher levels of IFN-γ in smokers compared to non-smokers were however only detected in individuals with SNP rs2069705 genotype AG/GG. This indicates a genetic association of the levels of IFN-γ.In paper IV, the separate effects of cigarette smoking and HNC on inflammatory or immune biomarkers and the impact of high risk human papillomavirus, age and gender were investigated. Comparisons of circulating levels of WBCs and its subpopulations, plasma proteins or cytokines/chemokines between smoking and non-smoking patients, smoking and non-smoking controls and between the patient and control groups were analysed. Smoking had highest impact on elevated levels of WBCs, IFN-γ and MCP-1, and HNC had highest impact on elevated levels of neutrophils, monocytes, NLR, CRP, macrophage inflammatory protein 1 beta and TNF-α.In conclusion, host immune response associated parameters could be suitable as biomarkers for the risk of HNC, risk of recurrence or in predicting survival of HNC patients. This thesis show that HNC are associated with systemic inflammatory response and upregulated CRP and TNF-α is related to shorter survival in HNC patients. Additionally, SNPs in immune response genes such as rs1800629 in the TNF-α gene indicates a risk for HNC or an elevated risk for recurrence and a decreased survival in HNC patients. These rapid and low cost blood based biomarkers could be used in combination or as a supplement to established biomarkers in the clinic for a more personalized treatment modality.
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| 4. |
- Andersson, Malin, et al.
(författare)
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A Continuous-time LPV model for battery state-of-health estimation using real vehicle data
- 2020
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Ingår i: CCTA 2020 - 4th IEEE Conference on Control Technology and Applications. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 692-698
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Konferensbidrag (refereegranskat)abstract
- One approach for State-of-health estimation onboard electric vehicles is to train a data-driven virtual battery on operational data and use this model, rather than the actual battery, for performance tests. A temperature-dependent continuous-time output-error (OE) model is proposed as virtual battery and identified and validated on real operational data from electric buses. The proposed model is compared to discrete-time and parameter-invariant models and shows better performance on all data sets. In addition, the OE model structure is shown to be superior to a conventional Auto Regressive eXogenous (ARX) model for the purpose of modeling the battery voltage response. Finally, challenges regarding vehicle log data are identified and improvements to the model are suggested in order to capture observed un-modeled phenomena.
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| 5. |
- Andersson, Malin
(författare)
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Aging sensitive battery control
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies.1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters.2) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts. 3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal control problem with charging speed and information content as objectives. To also take battery degradation into account, constraints on polarization was included. The results showed that parameter information can be increased without significant increase in charge time nor aging related stress.
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| 6. |
- Andersson, Malin, et al.
(författare)
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Electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model’s accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization.
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| 7. |
- Andersson, Malin, et al.
(författare)
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Electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells
- 2024
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Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 372
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Tidskriftsartikel (refereegranskat)abstract
- Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model's accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization.
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| 8. |
- Andersson, Malin, et al.
(författare)
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Informative battery charging : integrating fast charging and optimal experiments
- 2023
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Konferensbidrag (refereegranskat)abstract
- This paper presents informative battery charging, a novel approach for battery model parameter estimation during fast charge. Our solution comprises three distinct contributions: first, we develop a semi-explicit solution to an optimal fast charging problem for equivalent circuit models with health-conscious voltage constraints; second, we design optimal experiments for battery model parameter estimation; and third, we suggest a strategy for how the fast charging and experimentation currents can be combined while still satisfying constraints and maintaining acceptable charging times. Numerical results show that model parameters can be identified with lower variance if an optimal experiment is added to the charging procedure.
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| 9. |
- Andersson, Malin
(författare)
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Modelling, parameter identification and aging-sensitive management of lithium-ion batteries in heavy-duty electric vehicles
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging-sensitive battery management is necessary. This refers to a management strategy that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery since it reflects the changing properties of an aging battery. This thesis investigates identification of electrochemical and empirical battery models from operational EV data. In addition, it studies model-based strategies for optimal and adaptive fast charging. The work is divided into four main studies.1) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts.2) A global sensitivity analysis was performed on the parameters of a high-order electrochemical model. Measured current profiles from real EVs were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters.3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal experiment design problem in frequency domain. An aging-sensitive fast-charge procedure was optimized based on equivalent circuit model (ECM) states. Finally, different methods for combining the optimal fast charge and the optimal experiment were evaluated with regard to the resulting charging time and model performance. 4) Finally, aging-adaptive fast charging of automotive lithium-ion cells was studied. An electrochemical model was identified at the beginning of life and an electrochemically constrained fast charge was designed. The model parameters were then periodically re-evaluated during a cycling study and the charging procedure was updated to account for cell degradation. The study showed that adaptation of charge protocols increased the cell utilization compared to static protocols, but that heterogeneous degradation reduced the validity of the model and the adherence to electrochemical constraints.
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| 10. |
- Andersson, Malin, et al.
(författare)
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p Parametrization of physics-based battery models from input-output data : A review of methodology and current research
- 2022
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Ingår i: Journal of Power Sources. - : Elsevier BV. - 0378-7753 .- 1873-2755. ; 521, s. 230859-
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Forskningsöversikt (refereegranskat)abstract
- Physics-based battery models are important tools in battery research, development, and control. To obtain useful information from the models, accurate parametrization is essential. A complex model structure and many unknown and hard-to-measure parameters make parametrization challenging. Furthermore, numerous applications require non-invasive parametrization relying on parameter estimation from measurements of current and voltage. Parametrization of physics-based battery models from input-output data is a growing research area with many recent publications. This paper aims to bridge the gap between researchers from different fields that work with battery model parametrization, since successful parametrization requires both knowledge of the underlying physical system as well as understanding of theory and concepts behind parameter estimation. The review encompasses sensitivity analyses, methods for parameter optimization, structural and practical identifiability analyses, design of experiments and methods for validation as well as the use of machine learning in parametrization. We highlight that not all model parameters can accurately be identified nor are all relevant for model performance. Nonetheless, no consensus on parameter importance could be shown. Local methods are commonly chosen because of their computational advantages. However, we find that the implications of local methods for analysis of non-linear models are often not sufficiently considered in reviewed literature.
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