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Träfflista för sökning "hsv:(NATURVETENSKAP) hsv:(Matematik) hsv:(Beräkningsmatematik) ;pers:(Murgovski Nikolce 1980)"

Sökning: hsv:(NATURVETENSKAP) hsv:(Matematik) hsv:(Beräkningsmatematik) > Murgovski Nikolce 1980

  • Resultat 1-10 av 41
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1.
  • Murgovski, Nikolce, 1980, et al. (författare)
  • Convex relaxations in the optimal control of electrified vehicles
  • 2015
  • Ingår i: American Control Conference. - 0743-1619. - 9781479986842 ; 2015-July, s. 2292-2298
  • Konferensbidrag (refereegranskat)abstract
    • When controlling the energy flow in electrified powertrains by means of convex optimization, the typically non-convex set of the original formulation needs to be relaxed to a convex super-set. In this paper we show that when using the backward simulation approach, where vehicle velocity is equal to the reference velocity, the global optimum of the original non-convex problem can be obtained by solving the relaxed convex problem. When vehicle velocity is kept as a state in the problem, in the so called forward simulation approach, we provide a condition for which, when satisfied, an agreement will be achieved between the solutions of the relaxed and the original problem.
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2.
  • Du, Wei, et al. (författare)
  • Stochastic Model Predictive Energy Management of Electric Trucks in Connected Traffic
  • 2023
  • Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 72:4, s. 4294-4307
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper proposes a cost-effective power management strategy utilizing the data provided by V2I communication techniques for dual electric machine coupling propulsion trucks. We formulate a bilevel program where the high-level optimizes operation mode implicitly, while the low-level computes an explicit policy for power distribution of two electric machines. Stochastic model predictive control (SMPC) strategy is employed at the high-level, the performance of which highly depends on the prediction accuracy of future driving information. To establish a position-dependent stochastic velocity predictor using limited amount of historical data, two improved approaches are developed: 1) Predictor using multiple features; 2) Predictor combining data and model. Simulations are performed to validate the performance of the proposed predictors compared with a benchmark. The results show that the controllers using the proposed predictors can reduce driving cost by 3.36 % and 4.26 %, respectively.
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3.
  • Elbert, Philipp, et al. (författare)
  • Engine On/Off Control for the Energy Management of a Serial Hybrid Electric Bus via Convex Optimization
  • 2014
  • Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 63:8, s. 3549-3559
  • Tidskriftsartikel (refereegranskat)abstract
    • Convex optimization has recently been suggested for solving the optimal energy management problem of hybrid electric vehicles. Compared to dynamic programming, this approach can significantly reduce the computational time, but the price to pay are additional model approximations and heuristics for discrete decision variables such as engine on/off control. In this paper, the globally optimal engine on/off conditions are derived analytically. It is demonstrated that the optimal engine on/off strategy is to switch the engine on if and only if the requested power exceeds a certain non-constant threshold. By iteratively computing the threshold and the power split using convex optimization, the optimal solution to the energy management problem is found. The effectiveness of the presented approach is demonstrated in two sizing case studies. The first case study deals with high energy capacity batteries, while the second case study deals with supercapacitors that have much lower energy capacity. In both cases, the proposed algorithm yields optimal results much faster than the dynamic programming algorithm.
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4.
  • Feru, Emanuel, et al. (författare)
  • Supervisory control of a heavy-duty diesel engine with an electrified waste heat recovery system
  • 2016
  • Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661. ; 54, s. 190-201
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents an integrated energy and emission management strategy, called Integrated Powertrain Control (IPC), for an Euro-VI diesel engine with an electrified waste heat recovery system. This strategy optimizes the CO2-NOx trade-off by minimizing the operational costs associated with fuel consumption, AdBlue dosage, and active particulate filter regeneration, while satisfying the tailpipe emission constraints. For comparison purposes, the proposed control strategy is applied to different powertrain configurations: with and without waste heat recovery (WHR) system and a WHR system equipped with a battery for energy storage. The potential of each studied configuration is evaluated over the World Harmonized Transient Cycle for cold-start and hot-start conditions. In comparison to the existing Euro VI engine without WHR system, it is shown in simulations that the optimal IPC strategy with an electrified WHR system and battery provides an additional 3.5% CO2 emission reduction and 19% particulate matter reduction, while satisfying the NOx emission constraint.
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5.
  • Hamednia, Ahad, 1990, et al. (författare)
  • Computationally Efficient Algorithm for Eco-Driving Over Long Look-Ahead Horizons
  • 2022
  • Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 23:7, s. 6556 -6570
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin's Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to average driver's driving cycle, the energy savings of using the proposed algorithm is up to 11.60%.
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6.
  • Hamednia, Ahad, 1990, et al. (författare)
  • Computationally Efficient Approach for Preheating of Battery Electric Vehicles before Fast Charging in Cold Climates
  • 2023
  • Ingår i: IFAC-PapersOnLine. - 2405-8963. ; 56:2, s. 6630-6635
  • Konferensbidrag (refereegranskat)abstract
    • This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingly, an optimal control problem (OCP) has been formulated in the form of a nonlinear program (NLP), aiming at minimising the total energy consumption of the battery. The main focus here is to develop a computationally efficient approach, mimicking the optimal preheating behavior without a noticeable increase in the total energy consumption. The proposed algorithm is simple enough to be implemented in a low-level electronic control unit of the vehicle, by eliminating the need for solving the full NLP in the cost of only 1 Wh increase in the total energy consumption.
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7.
  • Hamednia, Ahad, 1990, et al. (författare)
  • Electric Vehicle Eco-driving under Wind Uncertainty
  • 2021
  • Ingår i: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). - : IEEE. ; 2021-September, s. 3502-3508
  • Konferensbidrag (refereegranskat)abstract
    • This paper addresses eco-driving of an electric vehicle driving in a hilly terrain under stochastic wind speed uncertainty. The eco-driving problem has been formulated as an optimisation problem, subject to road and traffic information. To enhance the computational efficiency, the dimension of the formulated problem has been reduced by appending trip time dynamics to the problem objective, which is facilitated by necessary Pontryagin's Maximum Principle conditions. To cope with the wind speed uncertainty, stochastic dynamic programming has been applied to solve the problem. Moreover, soft constraints on speed limits (kinetic energy) have been considered in the problem by enforcing sharp penalties in the objective. To benchmark the results, a deterministic controller has also been obtained with the aim of investigating possible constraints violations due to the wind speed uncertainty. For the proposed stochastic controller the optimised speed trajectories always remain within the limits and the violation on the trip time limit is only 8%. On the other hand, the speed and trip time constraints violations for the deterministic controller are 21% and 25%, respectively.
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8.
  • Hosseini, Seyed Mehrdad, 1977, et al. (författare)
  • Adaptive forward collision warning algorithm for automotive applications
  • 2016
  • Ingår i: American Control Conference. - 0743-1619. - 9781467386821 ; , s. 5982-5987
  • Konferensbidrag (refereegranskat)abstract
    • This paper proposes an adaptive collision warning algorithm (CWA) that supports the driver by issuing early warnings for collision avoidance without noticeably increasing the risk of false alarms in real traffic. This algorithm can also detect when an emergency intervention is necessary. Compared to existing CWAs, the proposed solution in this paper triggers alarms by solving a linear convex program using traffic data, road’s constraints and bicycle model dynamics, and by incorporating an adaptive (speed-dependent) warning threshold. A collision threat is detected by determining feasible steering trajectories without altering the vehicle’s longitudinal velocity.
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9.
  • Hu, Xiaosong, 1983, et al. (författare)
  • Integrated optimization of battery sizing, charging, and power management in plug-in hybrid electric vehicles
  • 2016
  • Ingår i: IEEE Transactions on Control Systems Technology. - 1063-6536 .- 1558-0865. ; 24:3, s. 1036-1043
  • Tidskriftsartikel (refereegranskat)abstract
    • This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.
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10.
  • Ilka, Adrian, 1987, et al. (författare)
  • An iterative Newton's method for output-feedback LQR design for large-scale systems with guaranteed convergence
  • 2019
  • Ingår i: 2019 18th European Control Conference, ECC 2019. ; June 2019, s. 4849-4854
  • Konferensbidrag (refereegranskat)abstract
    • The paper proposes a novel iterative output-feedback control design procedure, with necessary and sufficient stability conditions, for linear time-invariant systems within the linear quadratic regulator (LQR) framework. The proposed iterative method has a guaranteed convergence from an initial Lyapunov matrix, obtained for any stabilizing state-feedback gain, to a stabilizing output-feedback solution. Another contribution of the proposed method is that it is computationally much more tractable then algorithms in the literature, since it solves only a Lyapunov equation at each iteration step. Therefore, the proposed algorithm succeed in high dimensional problems where other, state-of-the-art methods fails. Finally, numerical examples illustrate the effectiveness of the proposed method.
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