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  • 1.
    De Guglielmo, Domenico
    et al.
    University of Pisa, Italy.
    Al Nahas, Beshr
    Chalmers University of Technology, Sweden.
    Duquennoy, Simon
    RISE - Research Institutes of Sweden, ICT, SICS.
    Voigt, Thiemo
    RISE - Research Institutes of Sweden, ICT, SICS.
    Anastasi, Guiseppe
    University of Pisa, Italy.
    Analysis and Experimental Evaluation of IEEE 802.15.4e TSCH CSMA-CA Algorithm2017In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 66, no 2, p. 1573-1588, article id 7451274Article in journal (Refereed)
    Abstract [en]

    Time-slotted channel hopping (TSCH) is one of the medium access control (MAC) behavior modes defined in the IEEE 802.15.4e standard. It combines time-slotted access and channel hopping, thus providing predictable latency, energy efficiency, communication reliability, and high network capacity. TSCH provides both dedicated and shared links. The latter is special slots assigned to more than one transmitter, whose concurrent access is regulated by a carrier-sense multiple access with collision avoidance (CSMA-CA) algorithm. In this paper, we develop an analytical model of the TSCH CSMA-CA algorithm to predict the performance experienced by nodes when using shared links. The model allows for deriving a number of metrics, such as delivery probability, packet latency, and energy consumption of nodes. Moreover, it considers the capture effect (CE) that typically occurs in real wireless networks. We validate the model through simulation experiments and measurements in a real testbed. Our results show that the model is very accurate. Furthermore, we found that the CE plays a fundamental role as it can significantly improve the performance experienced by nodes.

  • 2.
    Larsson, Viktor
    et al.
    Chalmers University of Technology, Sweden.
    Johannesson, Lars
    RISE, Swedish ICT, Viktoria.
    Egardt, Bo
    Chalmers University of Technology, Sweden.
    Analytic Solutions to the Dynamic Programming sub-problem in Hybrid Vehicle Energy Management2014In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 64, no 4, p. 1458-Article in journal (Refereed)
    Abstract [en]

    The computationally demanding dynamic programming (DP) algorithm is frequently used in academic research to solve the energy management problem of a hybrid electric vehicle (HEV). This paper is exclusively focused on how the computational demand of such a computation can be reduced. The main idea is to use a local approximation of the gridded cost-to-go and derive an analytic solution for the optimal torque split decision at each point in the time and state grid. Thereby, it is not necessary to quantize the torque split and identify the optimal decision by interpolating in the cost-to-go. Two different approximations of the cost-to-go are considered in this paper: 1) a local linear approximation and 2) a quadratic spline approximation. The results indicate that computation time can be reduced by orders of magnitude with only a slight degradation in simulated fuel economy. Furthermore, with a spline approximated cost-to-go, it is also possible to significantly reduce the memory storage requirements. A parallel plug-in HEV is considered in this paper, but the method is also applicable to an HEV.

  • 3. Murgovski, N
    et al.
    Johannesson, L
    RISE, Swedish ICT, Viktoria.
    Egardt, B
    Optimal battery dimensioning control of a CVT PHEV powertrain2013In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 63, no 5, p. 2141-Article in journal (Refereed)
    Abstract [en]

    This paper presents convex modeling steps for the problem of optimal battery dimensioning control of a plug-in hybrid electric vehicle with a continuous variable transmission. The power limits of the internal combustion engine the electric machine are approximated as convex/concave functions in kinetic energy, while their losses are approximated as convex in both kinetic energy power. An example is presented of minimizing total cost of ownership of a city bus including battery wear model. The proposed method is also used to obtain optimal charging power from an infrastructure that is to be designed at the same time the bus is dimensioned._x000D_

  • 4.
    Murgovski, N.
    et al.
    Chalmers University of Technology, Sweden.
    Johannesson, L
    RISE, Swedish ICT, Viktoria. Chalmers University of Technology, Sweden.
    Egardt, B.
    Chalmers University of Technology, Sweden.
    Optimal Battery Dimensioning Control of a CVT PHEV Powertrain2014In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 63, no 5, p. 2151-2161Article in journal (Refereed)
    Abstract [en]

    This paper presents convex modeling steps for the problem of optimal battery dimensioning control of a plug-in hybrid electric vehicle with a continuous variable transmission. The power limits of the internal combustion engine the electric machine are approximated as convex/concave functions in kinetic energy, whereas their losses are approximated as convex in both kinetic energy power. An example of minimizing the total cost of ownership of a city bus including a battery wear model is presented. The proposed method is also used to obtain optimal charging power from an infrastructure that is to be designed at the same time the bus is dimensioned.

  • 5. Murgovski, N
    et al.
    Johannesson Mårdh, L
    RISE, Swedish ICT, Viktoria.
    Sjöberg, J
    Engine on/off control for dimensioning hybrid electric powertrains via convex optimization2013In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 62, no 7, p. 2949-Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel heuristic method for_x000D_ optimal control of mixed-integer problems which for given_x000D_ feasible values of the integer variables are convex in the rest of_x000D_ the variables. The method is based on the Pontryagin€™s maximum_x000D_ principle allows the problem to be solved using convex_x000D_ optimization techniques. The advantage of this approach is the_x000D_ short computation time for obtaining a solution near the global_x000D_ optimum, which may otherwise need very long computation time_x000D_ when solved by algorithms guaranteeing global optimum, such_x000D_ as Dynamic Programming._x000D_ In this study the method is applied to the problem of battery_x000D_ dimensioning power split control of a plug-in hybrid electric_x000D_ vehicle where the only integer variable is the engine on/off_x000D_ control, but the method can be extended to problems with more_x000D_ integer variables. The studied vehicle is a city bus which is_x000D_ driven along a perfectly known bus line with a fixed charging_x000D_ infrastructure. The bus can charge either at standstill, or while_x000D_ driving along a tram line (slide-in)._x000D_ The problem is approached in two different scenarios: first,_x000D_ only the optimal power split control is obtained for several fixed_x000D_ battery sizes, second, both battery size power split control_x000D_ are optimized simultaneously. Optimizations are performed over_x000D_ four different bus lines two different battery types, giving_x000D_ solutions that are very close to the global optimum obtained by_x000D_ Dynamic Programming._x000D_

  • 6.
    Peng, B.
    et al.
    Chalmers University of Technology, Sweden.
    Seco-Granados, G.
    Universitat Autonoma de Barcelona, Spain.
    Steinmetz, Erik
    RISE - Research Institutes of Sweden, Safety and Transport, Measurement Science and Technology. Chalmers University of Technology, Sweden.
    Fröhle, M.
    Chalmers University of Technology, Sweden.
    Wymeersch, H. W.
    Chalmers University of Technology, Sweden.
    Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning2019In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 5, p. 4295-4305, article id 8701533Article in journal (Refereed)
    Abstract [en]

    Cooperative localization is a promising solution to the vehicular high-accuracy localization problem. Despite its high potential, exhaustive measurement and information exchange between all adjacent vehicles are expensive and impractical for applications with limited resources. Greedy policies or hand-engineering heuristics may not be able to meet the requirement of complicated use cases. In this paper, we formulate a scheduling problem to improve the localization accuracy (measured through the Cramér-Rao lower bound) of every vehicle up to a given threshold using the minimum number of measurements. The problem is cast as a partially observable Markov decision process and solved using decentralized scheduling algorithms with deep reinforcement learning, which allow vehicles to optimize the scheduling (i.e., the instants to execute measurement and information exchange with each adjacent vehicle) in a distributed manner without a central controlling unit. Simulation results show that the proposed algorithms have a significant advantage over random and greedy policies in terms of both required numbers of measurements to localize all nodes and achievable localization precision with limited numbers of measurements.

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