| 1. |
- Hamednia, Ahad, 1990, et al.
(författare)
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Charge Planning and Thermal Management of Battery Electric Vehicles
- 2023
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 72:11, s. 14141-14154
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Tidskriftsartikel (refereegranskat)abstract
- This paper studies optimal thermal management and charging of a battery electric vehicle driving over long-distance trips. The focus is on the potential benefits of including a heat pump in the thermal management system for waste heat recovery, and charging point planning, in a way to achieve optimality in time, energy, or their trade-off. An optimal control problem is formulated, in which the objective function includes the energy delivered by the charger(s), and the total charging time including the actual charging time and the detour time to and from the charging stop. To reduce the computational complexity, the formulated problem is then transformed into a hybrid dynamical system, where charging dynamics are modeled in the domain of normalized charging time. Driving dynamics can be modeled in either trip time or travel distance domains, as the vehicle speed is assumed to be known a priori, and the vehicle is only stopping at charging locations. Within the hybrid dynamical system, a binary variable is introduced for each charging location, in order to decide whether to use or skip a charger. This problem is solved numerically, and simulations are performed to evaluate the performance in terms of energy efficiency and time. The simulation results indicate that the time required for charging and total energy consumption are reduced up to $30.6\%$ and $19.4\%$, respectively, by applying the proposed algorithm.
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| 2. |
- Hamednia, Ahad, 1990, et al.
(författare)
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Optimal Thermal Management, Charging, and Eco-driving of Battery Electric Vehicles
- 2023
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 72:6, s. 7265-7278
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses optimal battery thermal management, charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimization problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modeling the driving dynamics in a spatial domain, where decisions are made along the traveled distance. Charging dynamics are modeled in a temporal domain, where decisions are made along a normalized charging time. The actual charging time is modeled as a scalar variable that is optimized simultaneously with the optimal state and control trajectories, for both charging and driving modes. The performance of the proposed algorithm is assessed over a road with a hilly terrain, where two charging possibilities are considered along the driving route and the battery is soaked to the ambient before departure. According to the results, trip time including driving and charging times, is reduced by 44%, compared to a case without active heating/cooling of the battery.
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| 3. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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PHEV Energy Management: A Comparison of Two Levels of Trip Information
- 2012
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627.
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Konferensbidrag (refereegranskat)abstract
- Plug-in hybrid electric vehicles (PHEVs) have rechargeable energy storage which can be used to run the vehicle on shorter range on electricity from the grid. In the absence of a priori information about the trip, a straightforward strategy is to first deplete the battery down to a minimum level and then keep the state of charge (SoC) around this level. However, largely due to the battery losses, the overall fuel economy can be improved if the battery is discharged gradually. This requires some a priori knowledge about the trip.This paper investigates the tradeoff between improved fuel economy and the need for a priori information. This investigation is done using a variant of telemetry equivalent consumption minimization strategy (T-ECMS) which is modified to be used for a PHEV. To implement this strategy, several parameters need to be tuned based on an assumption of the future trip. By studying two different levels of details, the tradeoff between fuel economy and a priori information is evaluated. It is shown that the proposed strategy improves the fuel economy considerably even when general information is available. However, increase in the details of the a priori information improves the fuel economy even further.
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| 4. |
- Egardt, Bo, 1950, et al.
(författare)
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Electromobility Studies Based on Convex Optimization DESIGN AND CONTROL ISSUES REGARDING VEHICLE ELECTRIFICATION
- 2014
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Ingår i: IEEE Control Systems. - 1066-033X. ; 34:2, s. 32-49
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a framework to study design tradeoffsin the search for electromobility solutions based on approximatemodeling of the power flows in the powertrain as afunction of component sizes. An important consequence ofthe modeling assumptions is that the optimal energy managementand component sizes can be computed simultaneouslyin a convex program, which means that competingdesigns can be evaluated in an objective way, avoiding theinfluence of a separate control system design. The fact thatthe optimization problem is convex allows large problemsto be solved with moderate computational resources, whichcan be exploited by, for example, running optimizationsover very long driving cycles. The problem formulationalso admits design decisions for the charging infrastructureto be included in the optimization.
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| 5. |
- Lindroth, Tobias, et al.
(författare)
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Online Learning Models for Vehicle Usage Prediction During COVID-19
- 2024
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Today, there is an ongoing transition to more sustainable transportation, for which an essential part is the switch from combustion engine vehicles to battery electric vehicles (BEVs). BEVs have many advantages from a sustainability perspective, but issues such as limited driving range and long recharge times slow down the transition from combustion engines. One way to mitigate these issues is by performing battery thermal preconditioning, which increases the energy efficiency of the battery. However, to optimally perform battery thermal preconditioning, the vehicle usage pattern needs to be known, i.e., how and when the vehicle will be used. This study attempts to predict the departure time and distance of the first drive each day using online machine learning models. The online machine learning models are trained and evaluated on historical driving data collected from a fleet of BEVs during the COVID-19 pandemic. Additionally, the prediction models are extended to quantify the uncertainty of their predictions, which can be used to decide whether the prediction should be used or dismissed. Based on our results, the best-performing prediction models yield an aggregated mean absolute error of 2.75 hours when predicting departure time and 13.37 km when predicting trip distance.
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| 6. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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An iterative dynamic programming/convex optimization procedure for optimal sizing and energy management of PHEVs
- 2014
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Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - : Elsevier BV. - 2405-8963 .- 1474-6670. - 9783902823625 ; 19, s. 6606-6611
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a time-efficient method for sub-optimal design of a plug-in hybrid electric vehicle with a parallel powertrain topology. The method finds the optimal design of the vehicle by iteratively using dynamic programming (DP) and convex optimization to minimize sum of operational and component costs over a given driving cycle. In particular, DP is used to optimize energy management, gear shifting and engine on-off for given component sizes, and convex optimization is used to optimize energy management and component sizes using the gear shifting and engine on-off strategies obtained by DP. Next, DP is re-optimized with the component sizes obtained by convex optimization, and the procedure is repeated until the component sizes converge. The result of this iterative method is compared by using DP on a grid of possible component sizes. It is shown that the iterative method gives a result very close to the global optimum in a comparably short time.
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| 7. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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Convex Optimization Methods for Powertrain Sizing of Electrified Vehicles by Using Different Levels of Modeling Details
- 2018
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Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 67:3, s. 1881-1893
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates the impact of different levels of modeling details on the problem of optimizing the total cost of ownership of a fuel-cell hybrid electric vehicle. In this optimization, the objective function is a weighted sum of operational and component costs over a driving cycle. The former includes the consumed hydrogen and electrical energy, and the latter includes the sum of the battery, fuel-cell, and electric-motor costs. Three methods with different levels of modelling details are investigated; in the first method, the power split between the two power sources together with component sizes are optimized, while assuming nonlinear loss functions for the components. In the second method, the efficiencies of the components are approximated by constant values. In the third method, the problem is simplified further by considering the energy split between the battery and the fuel-cell. As shown in the results, a more detailed model gives more accurate results at the price of increased computation time. However, the simplified models can give similar results as the detailed model in most cases. In some problems though, the model simplifications lead to results that differ notably from those obtained by using the detailed model.
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| 8. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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Effect of Driving, Charging, and Pricing Scenarios on Optimal Component Sizing of a PHEV
- 2017
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Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661. ; 61, s. 217-228
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, the problem of optimal sizing of a series PHEV is studied by formulating a convex program that minimizes the sum of operational and component costs. The solution gives the optimal sizes of the main powertrain components, simultaneously with the vehicle’s optimal energy management. Investigations are performed on driving cycles generated stochastically from real data using Markov chains, with different driving distance distributions and charging patterns. The results show that the optimal component sizing is affected more from the driving distances between charging opportunities, than the speed profile of the driving. With anticipated future battery and petroleum prices, larger battery sizes are obtained.
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| 9. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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Effect of Driving patterns on Components sizing of a Series PHEV
- 2013
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Ingår i: IFAC Proceedings Volumes (IFAC-PapersOnline). - 2405-8963. - 9783902823434 ; 7:1, s. 17-22
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Konferensbidrag (refereegranskat)abstract
- In the past decade, it has been demonstrated that Plug-in Hybrid Electric Vehicles (PHEVs)can significantly reduce petroleum consumptions. However, the extend to which these vehicles canreduce the petroleum consumption highly depends on components size and driving patterns. In otherwords, PHEVs show the best benefits if the components are dimensioned to match the driver’s drivingbehavior. In this paper, the effect of different driving patterns on the optimal sizing of three majorcomponents of series PHEVs, i.e., battery, electric motor, and engine generator unit is studied. Differentdriving cycles are generated stochastically from real driving data using Markov chains, to representlife-time driving patterns of different drivers.To find the optimal size of the components, the problem is formulated as a convex optimization problem.The optimization variables (the variables of component size and energy management) are obtained byminimizing a cost function which is the sum of the operational and component costs.
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| 10. |
- Pourabdollah, Mitra, 1984
(författare)
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On Optimization of Plug-in Hybrid Electric Vehicles
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rising concerns about the global warming and emissions on one hand, and the limited sources of fossil fuels on the other hand, has made electrificationof vehicles an interesting topic among researchers and companies. Hybrid electric vehicles (HEV) have been on market or several years. These vehicles proved to decrease the fuel consumption due to downsized engine,regeneration of the braking energy, and the higher efficiency gained from the extra freedom in choosing the engine operating points. Plug-in HEVs have the additional ability to store energy from the electricity grid usinglarge capacity batteries. The extra source of energy in these systems opens new questions concerning both the energy management (the strategy that decides the power split between power sources) and sizing of the components.The first part of this thesis is on energy management strategies for a PHEV. A trivial strategy is to run the vehicle on battery energy until the battery state of charge reaches a lower level and it is kept around that level. This strategy requires no information about the trip; however, it does not result in the best fuel economy. An energy management strategy is proposed for PHEVs which is based on minimizing an equivalent fuel consumption. To implement this strategy, some a priori information about the trip is required. The proposed strategy can improve the fuel economy considerably, even when using only information about the trip length, compared to the trivial discharge strategy. Increasing the information details about the trip results in fuel consumption close to the optimal, calculated by using dynamic programming, when full information about the trip is available.The second part of the thesis focuses on design of PHEVs. The goal here is to design a vehicle that has low cost and low fuel consumption. An approach based on convex optimization is used for simultaneous optimization of component sizes and energy management for passenger PHEVs. The optimal sizes of key components, i.e. battery, electric motor, and engine/engine generator unit are obtained by minimizing a cost function, including operational and components costs. The effects of different performance requirement levels, change in prices of batteries and energy, and also driving pattern of different drivers, on the optimal design are studied. Since the result of the optimization depends highly on the driving cycle, a systematic way to generate driving cycles that reflect driving patterns of different drivers is given.
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| 11. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
-
Optimal sizing of a parallel PHEV powertrain
- 2013
-
Ingår i: IEEE Transactions on Vehicular Technology. - 0018-9545 .- 1939-9359. ; 62:6, s. 2469 - 2480
-
Tidskriftsartikel (refereegranskat)abstract
- Abstract—This paper introduces a novel method for the simultaneous optimization of energy management and powertrain component sizing of a parallel plug-in hybrid electric vehicle (PHEV). The problem is formulated as a convex optimization problem to minimize an objective function, which is a weighted sum of operational and component costs. The operational cost includes the consumed fossil fuel and electrical energy, whereas the component cost includes the cost of the battery, electric motor (EM), and internal combustion engine (ICE). The powertrain model includes quadratic losses for the powertrain components. Moreover, the combustion engine and the electric motor losses are assumed to linearly scale with respect to the size and the losses of baseline components. The result of the optimization is the variables of the global optimal energy management for every time instant and optimal component sizes. Due to the dependency of the result on the driving cycle, a long real-life cycle with its charging times is chosen to represent a general driving pattern. The method allows the study of the effect of some performance requirements, i.e., acceleration, top speed, and all-electric range, on the component sizes and total cost.
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| 12. |
- Pourabdollah, Mitra, 1984, et al.
(författare)
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Optimal Sizing of a Series PHEV: Comparison between Convex Optimization and Particle Swarm Optimization
- 2015
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 48:15, s. 16-22
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Konferensbidrag (refereegranskat)abstract
- Building a plug-in hybrid electric vehicle that has a low fuel consumption at low hybridization cost requires detailed design optimization studies. This paper investigates optimization of a PHEV with a series powertrain configuration, where plant and control parameters are found concurrently. In this work two often used methods are implemented to find optimal energy management with component sizes. In the first method, the optimal energy management is found simultaneously with the optimal design of the vehicle by using convex optimization to minimize the sum of operational and component costs over a given driving cycle. To find the integer variable, i.e., engine on-o, dynamic programming and heuristics are used. In the second method, particle swarm optimization is used to find the optimal component sizing, together with dynamic programming to find the optimal energy management. The results show that both methods converge to the same optimal design, achieving a 10.4% fuel reduction from the initial powertrain design. Additionally, it is highlighted that the usage of each of the methodposes challenges, such as computational time (where convex optimization outperform particle swarm optimization by a factor of 20) and the tuning effort for the particle swarm optimization and the ability to handle integer variables of convex optimization.
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| 13. |
- Pourabdollah, Mitra, 1984
(författare)
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Optimization of Plug-in Hybrid Electric Vehicles
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rising concerns about the global warming and emissions on one hand,and the limited resources of fossil fuels on the other hand, have made electrificationof vehicles a necessary topic among researchers and companies.Hybrid electric vehicles (HEV), which in addition to a primary power source,such as internal combustion engine or fuel cell, have an electric motor andan electric energy storage, such as a battery, have proved to decrease thefuel consumption. This is mainly due to regeneration of the braking energy,possibility to turn the engine off at low power demands, and higher efficiencygained from the extra freedom in choosing the engine operating points anddownsized engine. Plug-in hybrid electric vehicles (PHEV) have the additionalability to run on electrical energy charged from the electrical grid dueto their large capacity batteries. However, having extra electrical componentsin these vehicles, which results in higher cost, opens new questionsconcerning both the energy management and sizing of the components.This thesis further develops the application of convex optimization tosimultaneously minimize operational and components costs. This meansthat besides the optimal component sizes, the optimization gives the optimalenergy management strategy. Two different configurations, namelyparallel and series PHEVs, are investigated. For a parallel PHEV, the effectof different performance requirement levels and battery prices on theoptimal costs and sizes are investigated. For a series PHEV, the effectof driver’s driving and charging behaviors, performance requirements, andpricing scenarios on the optimal component sizes in different configurationsare studied. To generate driving cycles that reflect driving patterns of differentdrivers, a systematic method based on Markov chain is used.Moreover, the impact of reduction in modeling detail is investigated onboth computational time and accuracy of the results in the optimal sizingof a fuel cell PHEV. To cope with the integer variables in the problem, aniterative method using dynamic programming and convex optimization isintroduced.
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