Model-Based Design and Evaluation of Electric Vehicle Powertrain With Independent Driving Motors

2015 ◽  
Author(s):  
Haotian Wu ◽  
Haiyan Zhang
Keyword(s):  
Energy Consumption ◽  
Vector Control ◽  
Electric Vehicle ◽  
Torque Vector ◽  
Model Based ◽  
Transfer Case ◽  
Wheel Drive ◽  
Battery Capacity ◽  
Evaluation Approaches ◽  
Case Based

The transfer case based all-wheel drive electric vehicle (TCAWDEV) and dual-axle AWDEV have been investigated to balance concerns about energy consumption, drivability and stability of vehicles. An ideal AWDEV (IAWDEV) powertrain architecture is proposed by this research; the architecture has an independent driving motor at each wheel; in essence, the IAWDEV is a distributed powertrain that provides various combinations of torque vector control. This research also investigated the simplified methods to estimate the battery capacity and the operation envelope of motors, and employed model-based evaluation approaches to recursively identify the proper powertrain components. The model-based evaluation was conducted in LMS AMESim. The results show that the IAWDEV could reduce the complexity of drivetrain, and also can harvest more braking energy under poor road contact.

scholarly journals Torque Optimal Allocation Strategy of All-Wheel Drive Electric Vehicle Based on Difference of Efficiency Characteristics between Axis Motors

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1122 ◽  
Author(s):  
Xiaogang Wu ◽  
Dianyu Zheng ◽  
Tianze Wang ◽  
Jiuyu Du
Keyword(s):  
Energy Consumption ◽  
Power System ◽  
Electric Vehicle ◽  
Driving Cycle ◽  
Drive Power ◽  
Distribution Method ◽  
Torque Distribution ◽  
Wheel Drive ◽  
Average Distribution ◽  
The Difference

All-wheel drive is an important technical direction for the future development of pure electric vehicles. The difference in the efficiency distribution of the shaft motor caused by the optimal load matching and motor manufacturing process, the traditional torque average distribution strategy is not applicable to the torque distribution of the all-wheel drive power system. Aiming at the above problems, this paper takes the energy efficiency of power system as the optimization goal, proposes a dynamic allocation method to realize the torque distribution of electric vehicle all-wheel drive power system, and analyzes and verifies the adaptability of this optimization algorithm in different urban passenger vehicle working cycles. The simulation results show that, compared with the torque average distribution method, the proposed method can effectively solve the problem that the difference of the efficiency distribution of the two shaft motors in the power system affects the energy consumption of the power system. The energy consumption rate of the proposed method is reduced by 5.96% and 5.69%, respectively, compared with the average distribution method under the China urban passenger driving cycle and the Harbin urban passenger driving cycle.

scholarly journals An Attention-Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shen Li ◽  
Hailong Zhang ◽  
Huachun Tan ◽  
Zhiyu Zhong ◽  
Zhuxi Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Research Area ◽  
Traffic Information ◽  
Driving Experience ◽  
The Road ◽  
Proposed Model ◽  
Battery Capacity

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input.

scholarly journals Changes in the range of electric vehicles during operation

2019 ◽  
Vol 28 ◽  
pp. 01009
Author(s):  
Arkadiusz Dobrzycki ◽  
Michał Filipiak ◽  
Jarosław Jajczyk
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Electric Cars ◽  
Battery Capacity ◽  
Electrical Vehicles ◽  
Battery Packs

This paper describes the trends in growth of the number of electrical vehicles in Europe. The most popular electric cars available on the market were presented, with the capacity of their batteries, energy consumption and range declared by the manufacturer. These data were confronted with the results of road tests. Assuming an average annual mileage, the number of charging cycles was estimated, and on this basis the decrease of battery capacity. The results of calculations were compared with the observations of electric vehicle users. The calculations showed a mileage, after which the user should consider battery packs replacing.

Analysis of Energy Consumption in an Electric Vehicle through Virtual Reality Set-Up

2020 ◽  
Author(s):  
Marika Lamanuzzi ◽  
Jacopo Andrea Di Antonio ◽  
Federica Foiadelli ◽  
Michela Longo ◽  
Andrea Labombarda ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Energy Consumption ◽  
Electric Vehicle ◽  
Set Up

scholarly journals Multi-Objective Energy Management and Charging Strategy for Electric Bus Fleets in Cities Using Various ECO Strategies

2021 ◽  
Vol 13 (14) ◽  
pp. 7865
Author(s):  
Mohammed Mahedi Hasan ◽  
Nikos Avramis ◽  
Mikaela Ranta ◽  
Andoni Saez-de-Ibarra ◽  
Mohamed El Baghdadi ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Thermal Management ◽  
Energy Management ◽  
Management Strategy ◽  
Energy Requirement ◽  
Optimization Technique ◽  
Total Cost ◽  
Multi Objective ◽  
Battery Capacity ◽  
Eco Features

The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a “brute-force” Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%.

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