Control Variables Optimization and Feedback Control Strategy Design for the Blended Operating Regime of an Extended Range Electric Vehicle

Škugor, Branimir; Cipek, Mihael; Deur, Joško

doi:10.4271/2014-01-1898

Cited by 18 publications

(22 citation statements)

References 13 publications

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“…Thus, the battery target SOC for each driving distance is AB, AC and AD, based on Equation (7). In order to ensure safe and smooth operation of the extender and battery during the optimization process, the following physical constraints need to be satisfied: (8) where SOC min and SOC max represent the lower and upper bounds of the battery SOC, respectively; P chg,max and P dis,max represent the power limits for battery discharging and charging, respectively; and P ex,min and P ex,max represent the lower and upper constraints of APU output power, respectively. P delt_ex represents the change rate of the extender output power; T e,min and T e,max represent the lower and upper bounds of the engine torque, respectively; and ω ex,min and ω ex,max represent the lower and upper constraints of the extender speed, respectively.…”

Section: Dynamic Programmingmentioning

confidence: 99%

“…Subsequently, the algorithm finds the sequence of the optimal extender output power P ex (k) values that minimize the cost function over the entire drive cycle while satisfying all constraints, as shown in Equation (8).…”

Section: Dynamic Programmingmentioning

confidence: 99%

“…The battery state of charge (SOC) varies over a small range during the trip [6]. However, the EREV is a type of plug-in HEV, and it has a large battery for storing electric power from a charging station [7,8]. A charging cycle, which constitutes the whole life of the EREV, can be considered as the EREV leaving a charging station with full battery energy and arriving at one charging station or another.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Zhang

Sun

et al. 2017

Energies

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Abstract:The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

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Section: Dynamic Programmingmentioning

confidence: 99%

Section: Dynamic Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Zhang

Sun

et al. 2017

Energies

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“…3). The calculation is based on the transmission kinematics model [18,19], given by the equations included in Table 1. The engine torque is limited based on the maximum torque map, and another map is used to calculate the fuel mass flow dm f /dt.…”

Section: Control-oriented Modelingmentioning

confidence: 99%

Integration of Electric Vehicles into Energy and Transport Systems

2015

Self Cite

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There is a strong tendency of development and application of different types of electric vehicles (EV). This can clearly be beneficial for transport systems in terms of making it more efficient, cleaner, and quieter, as well as for energy systems due to the grid load leveling and renewable energy sources exploitation opportunities. The latter can be achieved only through application of smart EV charging technologies that strongly rely on application of optimization methods. For the development of both EV architectures and controls and charging optimization methods, it is important to gain the knowledge about driving cycle features of a particular EV fleet. To this end, the paper presents an overview of (i) electric vehicle architectures, modeling, and control system optimization and design; (ii) experimental characterization of vehicle fleet behaviors and synthesis of representative driving cycles; and (iii) aggregate-level modeling and charging optimization for EV fleets, with emphasis on freight transport.Key words: Charging, Driving cycles, Electric vehicles, Fleet, Modelling, Optimization Integracija električnih vozila u energetske i transportne sustave. U novije vrijeme postoji izražena težnja za razvojem i korištenjem različitih tipova električnih vozila. Ovo može biti korisno sa stanovišta transportnih sustava u smislu omogućavanja efikasnijeg,čišćeg, i tišeg transporta, kao i iz perspektive energetskih sustava zbog dodatnih potencijala za poravnanje opterećenja mreže i iskorištenje obnovljivih izvora energije. Potonje može biti ostvareno samo kroz korištenje tehnologija naprednog punjenja električnih vozila, koje sečesto temelje na primjeni optimizacijskih postupaka. Za razvoj prikladnih konfiguracija, upravljačkih sustava te metoda pametnog punjenja električnih vozila, potrebno je steći uvid u značajke voznih ciklusa razmatrane flote električnih vozila. Imajući u vidu navedeno,članak predstavlja pregled (i) konfiguracija i modeliranja električnih vozila, te optimiranja i sinteze njihova upravljačkog sustava; (ii) eksperimentalne karakterizacije ponašanja flote vozila i sinteze reprezentativnih voznih ciklusa; te (iii) modeliranja i optimiranja punjenja flote električnih vozila na agregatnom nivou, s naglaskom na teretni transport.

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“…The combination of the two is defined as the APU. There's no mechanical connection between the APU and the drive shaft, and the APU is connected to the drive motor and battery pack via an electrical power bus, and usually, via some kind of coupling device with varying degrees of complexity.In simpler implementations, when SOC of the battery pack drops below a pre-defined threshold, the APU starts to work, and supplies extra electrical power to the drive motor and battery pack, making the driving range of EREVs comparable to conventional ICE vehicles [3,4]. Control strategy of the APU partly determines system fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Efficiency Optimization and Control of Auxiliary Power Unit for Extended Range Electric Vehicles

Liu¹,

Zhang²

2016

dtetr

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Abstract. How to determine the target engine operation point is a key issue of APU (Auxiliary Power Unit) control, which has a significant influence on fuel consumption of the system. Conventionally, engine operation point was decided by ensuring that the engine works in its maximum efficiency area. However, System efficiency of APU equals the product of engine efficiency and generator efficiency. Conventional method did not pay special attention to the generator efficiency. Based on analysis of how generator efficiency and engine efficiency influence fuel consumption of the system, a fuel optimal control method with consideration of both engine and generator efficiencies is proposed. Numerical models of generator efficiency and engine efficiency are established based on experiment data. An backward optimization algorithm is proposed to calculate optimal engine operation points in order to achieve highest overall efficiency of the engine-generator system. A simulation model is established to validate the optimal algorithm, simulation results indicates that the fuel consumption of the system is reduced by more than three percent after optimization.

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Control Variables Optimization and Feedback Control Strategy Design for the Blended Operating Regime of an Extended Range Electric Vehicle

Cited by 18 publications

References 13 publications

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Integration of Electric Vehicles into Energy and Transport Systems

Efficiency Optimization and Control of Auxiliary Power Unit for Extended Range Electric Vehicles

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