A Simulation Platform for Optimization of Electric Vehicles With Modular Drivetrain Topologies

Nemeth, Thomas; Bubert, Andreas; Becker, Jan; Doncker, Rik W. De; Sauer, Dirk Uwe

doi:10.1109/tte.2018.2869371

Cited by 52 publications

(32 citation statements)

References 26 publications

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“…In addition to the inverter, the cost of the battery also accounts for a significant portion in the drive system [27]. The vehicle adopting the SiC inverter can save the energy consumption within the same driving range, which in turn reduces the installed battery capacity.…”

Section: Evaluation Of Energy Consumptionmentioning

confidence: 99%

“…It provides the desired velocity (v*) at which the vehicle drives in this scenario. The driver model reaches this velocity by controlling the virtual driving pedal (p) and comparing the desired velocity with the actual one (v) [27]. The vehicle control unit (VCU) is the central control entity in this model, which performs a related analysis of the signals given by the driver model as well as the information gained from the vehicle, the inverter (including the motor), and the battery, etc.…”

Section: A Simulation Modelmentioning

confidence: 99%

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The Potential Impact of Using Traction Inverters With SiC MOSFETs for Electric Buses

Wang

Xing

et al. 2021

IEEE Access

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The efficiency improvements offered by adopting Silicon Carbide (SiC) devices in Electric Vehicle (EV) inverters has been widely reported in various studies. However, it has still not been established whether or when the efficiency benefits can counteract the high price of SiC devices, especially for the scenario of urban electric buses. In this paper, the potential impact of installing SiC devices on electric buses is analyzed from the perspective of economic benefits. The SiC inverter used in the drive system is based on a discrete MOSFET in parallel. For comparison, an Si inverter based on a sixthgeneration IGBT produced by Infineon is also evaluated. A vehicle simulation platform is established in Simulink for an evaluation of energy consumption for different scenarios. It is shown that significant energy savings can be gained when the vehicle operates mostly in the partial load area. While in the nominal load area, energy savings are very limited. Based on the analysis, the conditions for offsetting the cost premium of SiC devices to realize system-level advantages are discussed using a multi-dimensional analysis considering the change in battery and device prices, driving range, vehicle mass, and driving cycles.INDEX TERMS SiC devices, electric bus, energy consumption, system-level advantages, driving cycles.

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Section: Evaluation Of Energy Consumptionmentioning

confidence: 99%

Section: A Simulation Modelmentioning

confidence: 99%

The Potential Impact of Using Traction Inverters With SiC MOSFETs for Electric Buses

Wang

Xing

et al. 2021

IEEE Access

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“…In literature, various studies have been performed to integrate simulation optimization with electrified bus systems [29]- [33]. However, there are many shortfalls in existing work.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Simulated-Annealing Based Electric Bus System Design, Simulation, and Analysis for Dehradun Smart City

et al. 2020

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Smart transportation network development with environmental issues into consideration has brought Industry 4.0 based solutions on priority. In this direction, battery-powered electric bus systems have been considered widely for ensuring flexibility, operation cost, and lesser pollutants emission. Industry 4.0 provides automation through a cyber-physical system (CPS), the interconnection of bus system entities with industrial internet-of-things (IIoT), remote information availability through cloud computing and scientific disciplines (human-computer interaction, artificial intelligence, machine learning etc.) integration. In this work, a discrete event-based simulation-optimization approach is integrated that take care of bus energy consumption according to real-time city's passenger needs and on-road friction levels. The proposed simulation optimization methodology utilizes multi-objective with dependent and independent variables for optimizing the overall system performance. In simulation optimization, objective functions are designed to tackle battery consumption, Internet-of-Thing (IoT) network performance, cloud operations efficiency and smart scientific discipline integration. Simulation parameters are based on a real-time bus system which is further analyzed, filtered and adapted as per the needs of the system. In another analysis, supercharger's capacities are varied to evaluate the performance of the proposed system and identify the low cost and efficient smart transportation system. Simulation results show different scenarios for variations in the number of buses, charging stations, bus-depots, mobile charging facilities, and bus-schedules. Simulation results show that the average passenger's waiting time in the waiting is (after ticket booking) varies between 0.2 minutes to 0.7 minutes in real-time traffic conditions. In similar traffic conditions, total passenger's time in system (ticket booking to travel) varies between 41.6 minutes (for 24 hours) to 45.5 minutes (for 1 year). In the simulation, priorities are given to those dependent and independent variables which save the battery consumption and elongate the utilization of buses. Lastly, it is also observed that the proposed system is suitable for resource-constraint devices because Gate Equivalent (GE) calculation shows that the proposed system can be implemented between 1986 GEs (communicational cost without confidentiality and authentication) and 7939 GEs (computational cost with HMAC for authentication in data storage). This ensures varies security primitivs such as confidentiality, availability and authentication. INDEX TERMS Electric vehicles, gate-equivalents, smart city, simulation-optimization, security primitives. I. INTRODUCTION Electric energy-based smart transportation system is the need for a new generation smart city's infrastructure. The associate editor coordinating the review of this manuscript and approving it for publication was Eklas Hossain. Electric energy has various advantages over fossil fuel energies. Fossil fuels emit ni...

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“…The most important are pouch cells, prismatic cells, and cylindrical cells. Traction batteries usually have a nominal voltage of around 400 V [8], although low voltage battery concepts are also being researched [9].…”

Section: Key Topological Componentsmentioning

confidence: 99%

Topology analysis of electric vehicles, with a focus on the traction battery

Nicoletti

Ostermann

Heinrich

et al. 2020

Forsch Ingenieurwes

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Over recent years, the number of battery electric vehicles (BEVs) has drastically increased due to new European Union (EU) regulations. These regulations force vehicle manufacturers to adjust their product range in order to fulfill the imposed carbon dioxide limits. Such an adjustment enforces the usage of battery electric vehicles. However, research into the optimal BEV architectures and topologies is still in progress. Therefore, the aim of this paper is an analysis of all the current electric vehicle topologies. From this analysis, the authors identify different basic battery shapes. Subsequently, these shapes are used to describe the impact of the battery on the passenger compartment. As an initial result of this analysis, the authors create a new denomination method, via which it is possible to cluster the battery topologies. In a second step, the collected data is clustered using the novel denomination method. Finally, this paper presents the benchmark topologies for the analyzed segments.

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A Simulation Platform for Optimization of Electric Vehicles With Modular Drivetrain Topologies

Cited by 52 publications

References 26 publications

The Potential Impact of Using Traction Inverters With SiC MOSFETs for Electric Buses

The Potential Impact of Using Traction Inverters With SiC MOSFETs for Electric Buses

A Novel Simulated-Annealing Based Electric Bus System Design, Simulation, and Analysis for Dehradun Smart City

Topology analysis of electric vehicles, with a focus on the traction battery

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