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Energy Efficiency of Electric Vehicles with Multiple Motors

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 5586

Special Issue Editor

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are the future of passenger transportation. Interest in EVs has risen sharply over the last few years, due to (i) global issues, such as the decline in fossil resources and increasing environmental pollution and (ii) the benefits of electric actuation compared to traditional actuation based on internal combustion engines. For instance, electric motors respond up to 100 times faster to torque demands and allow an accurate measurement of motor torque.

However, significant challenges are still to be addressed by the research community, including the limited range of electric vehicles. The average autonomy offered by a modern electric vehicle is still well below that of a vehicle powered by an internal combustion engine. This has motivated research on novel battery technologies and on optimal torque distribution strategies for the improvement of energy efficiency, for example via torque vectoring on vehicles with multiple motors.

Within this framework, for this Special Issue, we aim to assemble a collection of studies with topics of interest including, but not limited to, the study and analysis of the following:

- Modelling and/or experimental characterisation of the different sources of power loss in electric vehicles, including drivetrain power losses, battery power losses, and tyre slip power losses;

- Optimal layouts for electric vehicles with multiple motors;

- The suitability and performance of different types of electric motors for electric propulsion;

- Innovative control strategies for the improvement of energy efficiency, e.g., torque vectoring algorithms;

- The integration of energy efficiency features within vehicle dynamics controllers;

- Novel battery/energy storage technologies.

Dr. Basilio Lenzo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electric vehicles
  • energy efficiency
  • torque vectoring
  • understeer characteristics
  • power losses
  • hybrid vehicles
  • tyre slips
  • battery modelling
  • regenerative braking

Published Papers (2 papers)

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19 pages, 33057 KiB  
Article
A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles
by Tao Zhang, Ningyuan Guo, Xiaoxia Sun, Jie Fan, Naifeng Yang, Junjie Song and Yuan Zou
Sustainability 2021, 13(9), 5166; https://doi.org/10.3390/su13095166 - 5 May 2021
Cited by 25 | Viewed by 3027
Abstract
Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation [...] Read more.
Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal. Full article
(This article belongs to the Special Issue Energy Efficiency of Electric Vehicles with Multiple Motors)
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20 pages, 1673 KiB  
Article
On the Investigation of Energy Efficient Torque Distribution Strategies through a Comprehensive Powertrain Model
by Sara Salamone, Basilio Lenzo, Giovanni Lutzemberger, Francesco Bucchi and Luca Sani
Sustainability 2021, 13(8), 4549; https://doi.org/10.3390/su13084549 - 20 Apr 2021
Cited by 4 | Viewed by 1978
Abstract
In electric vehicles with multiple motors, the torque at each wheel can be controlled independently, offering significant opportunities for enhancing vehicle dynamics behaviour and system efficiency. This paper investigates energy efficient torque distribution strategies for improving the operational efficiency of electric vehicles with [...] Read more.
In electric vehicles with multiple motors, the torque at each wheel can be controlled independently, offering significant opportunities for enhancing vehicle dynamics behaviour and system efficiency. This paper investigates energy efficient torque distribution strategies for improving the operational efficiency of electric vehicles with multiple motors. The proposed strategies are based on the minimisation of power losses, considering the powertrain efficiency characteristics, and are easily implementable in real-time. A longitudinal dynamics vehicle model is developed in Simulink/Simscape environment, including energy models for the electrical machines, the converter, and the energy storage system. The energy efficient torque distribution strategies are compared with simple distribution schemes under different standardised driving cycles. The effect of the different strategies on the powertrain elements, such as the electric machine and the energy storage system, are analysed. Simulation results show that the optimal torque distribution strategies provide a reduction in energy consumption of up to 5.5% for the case-study vehicle compared to simple distribution strategies, also benefiting the battery state of charge. Full article
(This article belongs to the Special Issue Energy Efficiency of Electric Vehicles with Multiple Motors)
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