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Sundström, Christofer
Publikasjoner (10 av 17) Visa alla publikasjoner
Jung, D. & Sundström, C. (2019). A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation. IEEE Transactions on Control Systems Technology, 27(2), 616-630
Åpne denne publikasjonen i ny fane eller vindu >>A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation
2019 (engelsk)Inngår i: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 27, nr 2, s. 616-630Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.

sted, utgiver, år, opplag, sider
IEEE, 2019
Emneord
Automotive applications, change detection algorithms, fault detection, fault diagnosis, machine learning.
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-149582 (URN)10.1109/TCST.2017.2773514 (DOI)000457619300014 ()
Merknad

Funding agencies: Volvo Car Corporation Gothenburg Sweden

Tilgjengelig fra: 2018-07-08 Laget: 2018-07-08 Sist oppdatert: 2019-02-20bibliografisk kontrollert
Sundström, C., Jung, D. & Blom, A. (2016). Analysis of optimal energy management in smart homes using MPC. In: 2016 EUROPEAN CONTROL CONFERENCE (ECC) : . Paper presented at 15th European Control Conference; Ålborg; Denmark (pp. 2066-2071). Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Analysis of optimal energy management in smart homes using MPC
2016 (engelsk)Inngår i: 2016 EUROPEAN CONTROL CONFERENCE (ECC) , Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 2066-2071Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Advanced building management systems utilize future information, such as electricity spot prices, weather forecasts, and predicted electric loads and hot water consumption, to reduce the maximum electric power consumption and energy cost. A model predictive controller (MPC) is implemented for a household with one hour sample intervals, including hot water usage, charging of an electric vehicle, and domestic heating, but also an accumulator water tank to be used as an additional thermal energy storage. Both the maximum total power used in the house and the energy cost are included in the cost function to evaluate how these properties are affected by different system designs. The MPC solution is compared to the global optimal solution using dynamic programming indicating comparable performance. The robustness of the MPC is evaluated using a prediction of the future household electric consumption in the controller. Results also show that a significant part of the cost reduction is achieved for as small prediction horizons as five hours. Analysis shows that including an accumulator tank is useful for reducing the total energy cost, while reducing the peak power is mainly achieved by increasing the prediction horizon of the MPC.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2016
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-137769 (URN)10.1109/ECC.2016.7810596 (DOI)000392695300343 ()978-1-5090-2591-6 (ISBN)978-1-5090-2590-9 (ISBN)978-1-5090-2592-3 (ISBN)
Konferanse
15th European Control Conference; Ålborg; Denmark
Tilgjengelig fra: 2017-05-29 Laget: 2017-05-29 Sist oppdatert: 2018-03-28bibliografisk kontrollert
Sundström, C., Frisk, E. & Nielsen, L. (2016). Diagnostic Method Combining the Lookup Tables and Fault Models Applied on a Hybrid Electric Vehicle. IEEE Transactions on Control Systems Technology, 24(3), 1109-1117
Åpne denne publikasjonen i ny fane eller vindu >>Diagnostic Method Combining the Lookup Tables and Fault Models Applied on a Hybrid Electric Vehicle
2016 (engelsk)Inngår i: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 24, nr 3, s. 1109-1117Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A common situation in industry is to store measurements for different operating points in the lookup tables, often called maps. They are used in many tasks, e.g., in control and estimation, and therefore considerable investments in engineering time are spent in measuring them which usually make them accurate descriptions of the fault-free system. They are thus well suited for fault detection, but, however, such a model cannot give fault isolation since only the fault free behavior is modeled. One way to handle this situation would be also to map all fault cases but that would require measurements for all faulty cases, which would be costly if at all possible. Instead, the main contribution here is a method to combine the lookup model with analytical fault models. This makes good use of all modeling efforts of the lookup model for the fault-free case, and combines it with fault models with reasonable modeling and calibration efforts, thus decreasing the engineering effort in the diagnosis design. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. An extensive simulation study clearly shows that the approach achieves both good fault detectability and isolability performance. A main point is that this is achieved without the need for neither measurements of a faulty system nor detailed physical modeling, thus saving considerable amounts of development time.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016
Emneord
Electric machine; fault detection; fault diagnosis; fault isolation; hybrid electric vehicle (HEV); lookup table
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-128736 (URN)10.1109/TCST.2015.2480008 (DOI)000375273200032 ()
Tilgjengelig fra: 2016-05-31 Laget: 2016-05-30 Sist oppdatert: 2017-11-30
Sundström, C., Frisk, E. & Nielsen, L. (2015). A New Electric Machine Model and its Relevance for Vehicle Level Diagnosis. International Journal of Modelling, Identification and Control, 24(1), 1-9
Åpne denne publikasjonen i ny fane eller vindu >>A New Electric Machine Model and its Relevance for Vehicle Level Diagnosis
2015 (engelsk)Inngår i: International Journal of Modelling, Identification and Control, ISSN 1746-6172, Vol. 24, nr 1, s. 1-9Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

With the electrification of society, especially transportation, the control and supervision of electrical machines become more and more important due to its bearing on energy, environment, and safety. To optimise performance in control and supervision, appropriate modelling is crucial, and this regards both the ability to capture reality and the computational complexity to be useful in real-time. Here, a new low complexity model of the electric machine is proposed and developed. The new model treats the machine constants in a different way compared to a previous standard model, which results in a different expression for power losses. It is shown that this increases model expressiveness so when adapted to real data the result is significantly better. The significance of this modelling improvement is demonstrated using a task in vehicle diagnosis where it is shown that the separation between the non-faulty and faulty cases is better and the resulting performance is improved.

sted, utgiver, år, opplag, sider
InderScience Publishers, 2015
Emneord
electric machine models, permanent magnet synchronous machine, PMSM, power losses, hybrid electric vehicle, HEV, fault diagnosis, vehicle level diagnosis
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-105485 (URN)10.1504/IJMIC.2015.071704 (DOI)
Merknad

At the time for thesis presentation publication was in status: Manuscript

Tilgjengelig fra: 2014-03-25 Laget: 2014-03-25 Sist oppdatert: 2018-11-27bibliografisk kontrollert
Sundström, C. & Krysander, M. (2015). Smart Energy Usage for Vehicle Charging and House Heating. In: : . Paper presented at 4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM 2015 — Columbus, Ohio, USA, 23-26 August 2015 (pp. 224-229). , 48
Åpne denne publikasjonen i ny fane eller vindu >>Smart Energy Usage for Vehicle Charging and House Heating
2015 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In northern Europe the electricity price is set by hourly rates one day in advance. The price fluctuates due to supply and demand, and these fluctuations are expected to increase when solar and wind power are increased in the energy system. The potential in cost reduction for heating a house and charging of an electrified vehicle by using a smart energy management system in a household is investigated. Dynamic programming is used and a simulation study of a household in Sweden comparing this optimal control scheme with a heuristic controller is carried out. The time frame in the study is one year and a novel way of handling the fact that the vehicle is disconnected from the grid at some times is developed. A plug-in hybrid electric vehicle is considered, but the methodology is the same also for pure electric vehicles. It is found that the potential in energy cost reduction for house heating and vehicle charging is significant and that using a smart energy management system is a promising path of cost reduction, especially with the introduction of electrified vehicles. 

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-122460 (URN)10.1016/j.ifacol.2015.10.032 (DOI)
Konferanse
4th IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM 2015 — Columbus, Ohio, USA, 23-26 August 2015
Tilgjengelig fra: 2015-11-03 Laget: 2015-11-03 Sist oppdatert: 2015-11-10
Sundström, C., Frisk, E. & Nielsen, L. (2014). Diagnostic Method Combining Map and Fault Models Applied on a Hybrid Electric Vehicle.
Åpne denne publikasjonen i ny fane eller vindu >>Diagnostic Method Combining Map and Fault Models Applied on a Hybrid Electric Vehicle
2014 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

A common situation in the automotive industry is that map based models are available. In general these models accurately describe the fault free system, and are therefore suited for fault detectability in a diagnosis system. However, one drawback using such a model is that fault isolation then requires that measurements of the faulty system is done, which is costly. Another approach is to use a model of the system where the faults are explicitly included. To directly achieve good diagnostic performance such a model needs to be accurate, which also is costly. Therefore, in the new approach taken here, two models are used in combination to achieve both good fault detectability and isolability in a diagnosis system; one is a map based model, and one is describing how the faults affect the system. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. In an extensive simulation study it is shown that the approach works well and is a promising path to achieve both good fault detectability and isolability performance, without the need for neither measurements of a faulty system nor detailed physical modeling. In the designed diagnosis system all faults are fully isolated, and the size of the faults are accurately estimated.

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-105486 (URN)
Tilgjengelig fra: 2014-03-25 Laget: 2014-03-25 Sist oppdatert: 2014-03-25bibliografisk kontrollert
Sundström, C. (2014). Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Åpne denne publikasjonen i ny fane eller vindu >>Model Based Vehicle Level Diagnosis for Hybrid Electric Vehicles
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

When hybridizing a vehicle, new components are added that need to be monitored due to safety and legislative demands. Diagnostic aspects due to powertrain hybridization are investigated, such as that there are more mode switches in the hybrid powertrain compared to a conventional powertrain, and that there is a freedom in choosing operating points of the components in the powertrain via the overall energy management and still fulfill the driver torque request. A model of a long haulage truck is developed, and a contribution is a new electric machine model. The machine model is of low complexity, and treats the machine constants in a different way compared to a standard model. It is shown that this model describes the power losses significantly better when adopted to real data, and that this modeling improvement leads to better signal separation between the non-faulty and faulty cases compared to the standard model.

To investigate the influence of the energy management design and sensor configuration on the diagnostic performance, two vehicle level diagnosis systems based on different sensor configurations are designed and implemented. It is found that there is a connection between the operating modes of the vehicle and the diagnostic performance, and that this interplay is of special relevance in the system based on few sensors.

In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, i.e. there are more equations than unknown variables. The selection of sets of equations to be included in the diagnosis system and in what order to compute the unknown variables in the used equations affect the diagnostic performance. A systematic method that finds properties and constructs residual generator candidates based on a model has been developed. Methods are also devised for utilization of the residual generators, such as initialization of dynamic residual generators, and for consideration of the fault excitation in the residuals using the internal form of the residual generators. For demonstration, the model of the hybridized truck is used in a simulation study, and it is shown that the methods significantly increase the diagnostic performance.

The models used in a diagnosis system need to be accurate for fault detection. Map based models describe the fault free behavior accurately, but fault isolability is often difficult to achieve using this kind of model. To achieve also good fault isolability performance without extensive modeling, a new diagnostic approach is presented. A map based model describes the nominal behavior, and another model, that is less accurate but in which the faults are explicitly included, is used to model how the faults affect the output signals. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid vehicle, and simulations show that the approach works well.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2014. s. 10
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1589
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-105487 (URN)10.3384/diss.diva-105487 (DOI)978-91-7519-356-4 (ISBN)
Disputas
2014-04-25, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (engelsk)
Veileder
Tilgjengelig fra: 2014-03-25 Laget: 2014-03-25 Sist oppdatert: 2014-04-08bibliografisk kontrollert
Nilsson, T., Nyberg, P., Sundström, C., Frisk, E. & Krysander, M. (2014). Robust Driving Pattern Detection and Identification with a Wheel Loader Application. International journal of vehicle systems modelling and testing, 9(1), 56-76
Åpne denne publikasjonen i ny fane eller vindu >>Robust Driving Pattern Detection and Identification with a Wheel Loader Application
Vise andre…
2014 (engelsk)Inngår i: International journal of vehicle systems modelling and testing, ISSN 1745-6436, Vol. 9, nr 1, s. 56-76Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Information about wheel loader usage can be used in several ways to optimize customer adaption. First, optimizing the configuration and component sizing of a wheel loader to customer needs can lead to a significant improvement in e.g. fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, on-line usage identification opens up for the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective here is to develop an on-line algorithm that automatically, using production sensors only, can extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to berobust when evaluated on measured data of wheel loaders loading gravel and shot rock.

sted, utgiver, år, opplag, sider
InderScience Publishers, 2014
Emneord
Driving cycle; Driving cycle identification; Driving pattern; Pattern identification; Robust detection; State automaton; Usage classification; Usage detection; Wheel loader
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-92222 (URN)10.1504/IJVSMT.2014.059156 (DOI)2-s2.0-84893958574 (Scopus ID)
Tilgjengelig fra: 2013-05-08 Laget: 2013-05-08 Sist oppdatert: 2015-04-01bibliografisk kontrollert
Sundström, C., Frisk, E. & Nielsen, L. (2014). Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 44(2), 172-185
Åpne denne publikasjonen i ny fane eller vindu >>Selecting and Utilizing Sequential Residual Generators in FDI Applied to Hybrid Vehicles
2014 (engelsk)Inngår i: IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, ISSN 2168-2216, Vol. 44, nr 2, s. 172-185Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In order to obtain a realistic model of a complex system, thousands of possible residual generators need to be used for diagnosis. Based on engineering insights of the system to be monitored, certain algebraic and dynamic properties of the residual generators may be preferred, and therefore, a method for finding sequential residual generators is developed that accounts for these properties of the residual generator candidates. It is shown that only a small fraction of all residual generator candidates fulfill fundamental requirements, and thereby, proves the value of systematic methods. Furthermore, methods are devised for utilization of the residual generators, such as initialization of dynamic residual generators. A proposed method, considering the fault excitation in the residuals using the internal form of the residuals, significantly increases the diagnosis performance. A hybrid electric vehicle is used in a simulation study for demonstration, but the methods used are general in character and provides a basis when designing diagnosis systems for other complex systems.

sted, utgiver, år, opplag, sider
IEEE, 2014
Emneord
Fault diagnosis; hybrid electric vehicle; model based diagnosis; residual generation
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-104283 (URN)10.1109/TSMC.2013.2248147 (DOI)000330131800004 ()
Tilgjengelig fra: 2014-02-17 Laget: 2014-02-14 Sist oppdatert: 2014-03-25
Eriksson, D. & Sundström, C. (2014). Sequential Residual Generator Selection for Fault Detection. In: 2014 EUROPEAN CONTROL CONFERENCE (ECC): . Paper presented at 13th European Control Conference (ECC) (pp. 932-937). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Sequential Residual Generator Selection for Fault Detection
2014 (engelsk)Inngår i: 2014 EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2014, s. 932-937Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Structural methods in model-based fault diagnosis applications are simple and efficient tools for finding candidates for residual generation. However, the structural methods do not take model uncertainties and information about fault behavior into consideration. This may result in selecting residual generators with bad performance to be included in the diagnosis system. By using the Kullback-Leibler divergence, the performance of different residual generators can be compared to find the best one. With the ability to quantify diagnostic performance, the design of residual generators can be optimized by, for example, combining several residual generators such that the diagnostic performance is maximized. The proposed method for residual generation selection is applied to a water tank system to show that the achieved residual performance is improved compared to only use a structural method.

sted, utgiver, år, opplag, sider
IEEE, 2014
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-116847 (URN)10.1109/ECC.2014.6862195 (DOI)000349955701039 ()978-3-9524269-1-3 (ISBN)
Konferanse
13th European Control Conference (ECC)
Tilgjengelig fra: 2015-04-07 Laget: 2015-04-07 Sist oppdatert: 2015-04-07
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