(Special section ‘Design, modelling and control of electric vehicles’) This study provides a detailed deterministic and stochastic sensitivity analysis of the propulsion energy cost of electric vehicles (EVs) with respect to environmental variables. In particular, the effects of wind speed, rolling resistance, parasitic power and temperature are highlighted. The study provides exact analytical expressions as well as simulations to illustrate the key results. It is shown that the sensitivity of energy consumption with respect to the four environmental variables greatly vary with operating conditions of the vehicle. These environmental effects can have a profound effect on the overall energy consumption of EVs and drastically affect range. The significance of the authors’ findings for vehicle range estimation is discussed and potential avenues to exploit the strong dependency between propulsion energy and environmental factors are proposed.

Currently, the vehicle manufacturers use the high power Li-ion technology to supply the electric and hybrid vehicles. This technology is able to ensure the power needed to propel the vehicle. Until now several studies have been made by the laboratories and manufacturers to characterise this technology. The aim of these test (electric, thermal, aging,…) is to make comparison between Li-ion technologies and choice the best one for each application. For that, they use accelerated cycling with different condition to characterise cells, what can reduce the tests duration. Unfortunately, this type of cycle cannot give us information about the aging of HP Li-ion technology under real use of the vehicle. Firstly, the requirements specification (vehicle specification, battery technologies, mission) has been presented. After that, the authors will present the test bench developed in the laboratory to characterise batteries and study the aging of the HP technology. Here the authors present the study of the Li-ion HP behaviour during almost 3 years and the modelling (electric, thermal and aging modelling) using a real driving cycle. The experimental results are compared with the results obtained with the developed ageing model. The obtained results prove the good performances of this technology in electric vehicle applications.

This study introduces a multirate method for the simulation of multi-physics systems containing a wide range of time scales. This method has been designed for strongly coupled systems and is able to deal with high mutual dependency between fast and slow state variables. The proposed method based on a cycle formulation approach is applied to the ageing behaviour simulation of the energy storage unit (ESU) of an all-electric ferry. The objective is to design this ESU taking into account the electrical and thermal models, permitting to predict its ageing over the 20 years of the ferry operation. So, simulations could be excessively time consuming. The main objective behind this study is to reduce the simulation time to make possible an optimisation process. Using the proposed multirate method based on embedded extrapolation methods formulated on cycles, a speed-up factor of about 1000 compared with standard integration methods is obtained. Numerical results for the electric ferry example are presented for different tolerances in order to demonstrate the performance of the method.

This study provides a detailed analysis of an optimal drivetrain configuration, based on multi-cycles, for a plug-in electric vehicle (EV). The investigation aims to identify the best EV configuration according to the required power and the transmissible traction torque. The study focuses on an EV with four different combinations of drive systems among in-wheel motors and differential ones. To find out the best EV drive system configuration, it is adopted an optimisation process by means of a genetic algorithm that defines the electric motors (EMs) torque curves and powertrain transmission ratio in order to improve vehicle travel range and performance. The vehicle power demand is divided between the drive systems following rules established by the power management control which aims to reduce the lithium-ion battery discharges during the driving cycles: FTP-75 (urban driving), HWFET (highway driving) and US06 (high speeds and accelerations). After the simulations, the potential of each configuration is indicated according to its respective drive system and hence the best configurations are determined.

The braking system plays an important role in electric vehicles. In order to achieve high energy regeneration efficiency and also ensure driving safety at the same time, the regenerative braking system should be well coordinated with the mechanical braking system. As the brake-by-wire technology is developed, individual control of braking force on each wheel can be realised, which is suitable for the future electrified vehicles. This paper aims to investigate the braking force control strategy for electric vehicles under a low road friction condition. In order to further improve the braking force control accuracy and eliminate the effect of the vehicle load variation, a vehicle load estimation method is proposed. The standard hybrid braking force control strategy of electric vehicle is introduced for comparison with the proposed control strategy. To prevent a front wheel lock-up and maximise the regenerative braking efficiency under a low tyre–road friction condition, a revised control strategy is presented with the front wheel slip ratio consideration. Simulation results indicate that by using the proposed strategy, the vehicle braking performance under the low tyre–road friction condition is guaranteed and the regenerative braking force on front axle is fully utilised compared with the standard control strategy.

A detailed research on fault mechanism of lithium (Li)-ion battery at over-discharge condition is reported in this study. Cells were cycled with different depths of discharge and reference performance tests were performed to extract parameters in dynamic and equilibrium conditions. The over-discharge process indicates that the abrupt change of temperature and impedance can be used for fault predication, while the parameter variations from federal urban driving schedule test can clearly identify the fault mode. Curves from incremental capacity analysis suggest loss of active material at negative electrode (LAMNE) as well as Li inventory loss may dominate the over-discharge process. Finally, post-mortem examination was carried out to validate authors’ mechanism deduction and diagnosis approach. The proposed approach is suitable for over-discharge diagnosis and predication in electric vehicle applications.

The design of the powertrain of electric vehicles (EVs) and hybrid EVs could require the testing of different electrical machines (EMs) in order to evaluate their performances and to find the most suitable solution for that specific propulsion system. The number of EMs that can be tested is limited by economical and practical issues. Moreover, numerical simulations cannot always represent a reliable mean to understand the real behaviour of the system. This study presents a new mathematical model to emulate different virtual machines using only one real machine.

Automotive manufacturers face serious difficulties concerning the expected fossil fuel depletion and the emitted pollutants during combustion. The fuel cell (FC) vehicle is considered a promising solution for greener and sustainable transportation. The integration of FC in vehicles is limited by some technological constraints and technical barriers which can be partially met by splitting the power and energy into different ‘downsized’ FCs named multi-stack FCs. This study aims at proposing a multi-stack FC configuration for electric vehicles (EVs) to minimise its start-up time, heating/cooling and cycling problems. To properly reach such desired operating conditions, a novel thermal management technique is proposed. Using the modularity provided by multi-stack solution, each FC is activated depending on the FCs temperature and the required vehicle power. Simulation results, using MTCSim^© software, demonstrate the capability of the proposed thermal management approach to effectively enhance the EV's multi-stack FC's life span, cycling and efficiency, besides optimising its operation performance.

Battery electric city bus model has been created and validated with measurement results from a full-scale city bus prototype. The model utilises multi-physics modelling approach with multiple-domains from friction-tyre and transmission model to electrical drive and a simplified battery model. For the electrical drive model, four approaches to model the losses were studied. The modelled and measured motor torque, speed, current in the battery and inverter, and the bus speed were compared under two bus line driving cycles using three payloads. The simulated results were in good agreement with the experimental results; the error between the simulated and measured energy consumptions was 1.5% with the most accurate model.

Uncontrolled charging of electric vehicles (EVs) is expected to cause problems for power distribution networks as existing vehicles are continually being replaced by electric. Therefore, smart charging algorithms that prevent such problems will become necessary as uptake of EVs increases and they become more popular. Smart EV charging is not only useful to provide the necessary charge (energy) required by the user but may also be used to support the grid and protect battery health, which is investigated in this study. Factors that affect battery life are quantified and their impact on battery degradation and ability (of EV) to support the grid are analysed. Charging regimes that can meet the driver needs, provide grid support and protect the state of health of the battery are proposed in this study. The analysis presented demonstrates that smart charging that involves charging before departure, less frequent charging and limited vehicle-to-grid can prolong battery life compared with providing the same EV charge in an uncontrolled way. Thus, grid power is supported and battery life is protected by the proposed smart charging regimes.