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The electric machine and the control system determine the performance of the electric vehicle drivetrain. Unlike rare-earth magnet machines such as permanent magnet synchronous machines (PMSMs), synchronous reluctance machines(SynRMs) are manufactured without permanent magnets. This allows them to be used as an alternative to rare-earth magnet machines. However, one of the main drawbacks of this machine is its high torque ripple, which generates significant acoustic noise. The most typical method for reducing this torque ripple is to employ an optimized structural design or a customized control technique. The objective of this paper is the use of a control approach to minimize the torque ripple effects issue in the SynRM. This work is performed in two steps: Initially, the reference current calculation bloc is modified to reduce the torque ripple of the machine. A method for calculating the optimal reference currents based on the stator joule loss is proposed. The proposed method is compared to two methods used in the literature, the FOC and MTPA methods. A comparative study between the three methods based on the torque ripple rate shows that the proposed method allows a significant reduction in the torque ripple. The second contribution to the minimization of the torque ripple is to propose a sliding mode control. This control suffers from the phenomenon of "Chattering" which affects the torque ripple. To solve this problem, a second-order sliding mode control is proposed. A comparative study between the different approaches shows that the second-order sliding mode provides the lowest torque ripple rate of the machine. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reduction in RMS error of more than 50% compared to the Nernst model. These findings are significant because they will improve the accuracy of model-based battery management systems used in electric vehicles, allowing for improved performance prediction without the requirement of recharacterization for different drive cycles or individual cell characterization. [ABSTRACT FROM AUTHOR]

One of the main technological stumbling blocks in the field of environmentally friendly vehicles is related to the energy storage system. It is in this regard that car manufacturers are mobilizing to improve battery technologies and to accurately predict their behavior. The work proposed in this article deals with the advanced electrothermal modeling of a hybrid energy storage system integrating lithium-ion batteries and supercapacitors. The objective is to allow the aging aspects of the components of this system to be taken into account. The development of a model including the electrothermal behaviors makes it possible to evaluate the progressive degradation of the performance of the hybrid energy storage system. The characterization of both components constituting the hybrid system is carried out via a hybrid particle swarm–Nelder–Mead (PSO–NM) optimization algorithm using the experimental data of an urban electric vehicle. The obtained results show the good performance of the developed model and confirm the feasibility of our approach. The use of the PSO–NM optimization algorithm facilitated the identification of the parameters of the developed model with high efficiency, as the error observed is less than 3%. The advanced model associated with an adapted sizing method can be used in many cases to compare energy management strategies in electric vehicle applications. [ABSTRACT FROM AUTHOR]

In order to democratize electric vehicles, automakers have to give a solution to reduce the total cost of ownership (TCO) and bring it lower than thermal vehicles one. This paper presents results of works on cost improvement of a 3.5 t light-duty commercial electric vehicle. TCO was separated into investment costs and operating costs. The batteries of an electric vehicle were the most expensive element of the powertrain, which can represent up to 45% of the vehicle price. What's more, contrary to the life of thermal vehicles, electric energy storage systems have a limited life of around five years, which is highly dependent on conditions of use. Thus, study of batteries represents a capital step toward TCO improvement. Inspired by the literature, a battery aging model is proposed and identified by the calendar and cycling aging tests. A multiphysical model of the battery, including aging, was identified and incorporated inside the energetic vehicle model. Charging strategies were developed and adapted to operate according to the influence of the conditions of use. These models and strategies are integrated in a software tool. Taking into account the intended use of the vehicle, this tool first designates the most suitable battery solution dimensions, and then associates the charging strategy to the requirements of use in order to ensure the best battery life or the lowest TCO. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic model of Li-ion batteries incorporating electrothermal and ageing aspects is proposed for electric vehicle applications. The main goal of the proposed model is to be both simple and sufficiently representative of the physical phenomena occurring in a battery cell. These two features allow for using this model as an evaluation tool of electric vehicle performances under different operational and environmental conditions. The developed model is based on an equivalent circuit diagram coupled with a thermal circuit and a semi-empirical ageing equation. Identification of parameters in the dynamic model is conducted by measurement tests in time domain, which uses a hybrid particle Swarm–Nelder–Mead optimization algorithm to achieve excellent prediction over the whole applicable current and state of charge ranges. The validation results show that the proposed model is able to simulate the dynamic interaction between the battery ageing and the thermal as well as electric behavior with sufficient accuracy in the range tested. [ABSTRACT FROM PUBLISHER]

Modelling dynamic behaviours of the lithium‐ion (Li‐ion) battery and supercapacitor in electric vehicle applications is a key aspect for the emulation of the hybrid power supply. In this study, a dynamical model based on two non‐linear equivalent circuits is developed to describe the characteristics of the battery and supercapacitor during both steady‐state and transient conditions. The necessary parameters for the proposed model are extracted from measurement data in time and frequency domain using an optimisation algorithm. The developed model is coupled to power electronics devices fed by DC power supply to carry out a laboratory emulator of the hybrid power supply. This tool is mainly used for testing and verification of the electric vehicle performances with convenient and reproducible way. The proposed emulator avoids time‐consuming preconditioning and safety problems generally caused by the misuse of electrochemical components such as the Li‐ion battery. The modelling and experimental results show a good performance of the hybrid power supply emulator and confirm their feasibility over a wide range of operating points. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper examines and optimizes parameters that affect the sizing and control of a hybrid embedded power supply composed of Li-ion batteries and supercapacitors in electric vehicle applications. High demands including power and energy density, low charge/discharge power stress on the battery (long lifetime), lightweight design, and relatively modest cost at the same time cannot be provided solely by batteries or supercapacitors. For this reason, we propose the use of a Li-ion battery/supercapacitor hybrid embedded power supply for an urban electric vehicle. The sizing process of this system including the optimization of the power sharing is done thanks to a developed hybrid Particle Swarm–Nelder–Mead algorithm involving multi-objective optimization. This approach also allows us to optimize the proposed energy management strategies based on frequency rule-based control and different ways of supercapacitors energy regulation. Obtained results show that the hybrid embedded power supply with the proposed control strategies is able to offer the best performances for the chosen electric vehicle in terms of weight, initial cost, and battery lifetime. [ABSTRACT FROM PUBLISHER]

In recent years, Li-ion batteries are widely used in various applications, such as electric and hybrid vehicles application. Their higher specific power and energy density, high cycle lifetime and decreasing costs have made them an attractive and alternative energy storage technology to lead-acid or nickel-metal hydride batteries in embedded power supplies. In the present work, the electric modeling of a Li-ion battery cell in real operation conditions imposed by an electric vehicle application is carried out. A dynamic equivalent circuit model has been used to simulate several electrochemical processes occurring in a commercially available 40 Ah Li-ion battery cell with NMC cathode material and graphitic anode. The model is parameterized with measurement data in time-domain using a hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm. This last one is used to solve the parameters identification problem of Li-ion battery model. The developed model of Li-ion battery cell has been validated on real driving cycle provided by an urban electric vehicle. Obtained results show that there is a good match between experiment and simulation results with a maximum modeling error less than 0.5%, which proves the well performance of our model and confirms the interest of a hybrid (PSO–NM) optimization algorithm for battery identification parameters. [ABSTRACT FROM AUTHOR]

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements' life expectancies and supplies the power system with the required power. [ABSTRACT FROM AUTHOR]

Nowadays, electric vehicles are one of the main topics in the new industrial revolution, called Industry 4.0. The transport and logistic solutions based on E-mobility, such as handling machines, are increasing in factories. Thus, electric forklifts are mostly used because no greenhouse gas is emitted when operating. However, they are usually equipped with lead-acid batteries which present bad performances and long charging time. Therefore, combining high-energy density lithium-ion batteries and high-power density supercapacitors as a hybrid energy storage system results in almost optimal performances and improves battery lifespan. The suggested solution is well suited for forklifts which continuously start, stop, lift up and lower down heavy loads. This paper presents the sizing of a lithium-ion battery/supercapacitor hybrid energy storage system for a forklift vehicle, using the normalized Verein Deutscher Ingenieure (VDI) drive cycle. To evaluate the performance of the lithium-ion battery/supercapacitor hybrid energy storage system, different sizing simulations are carried out. The suggested solution allows us to successfully optimize the system in terms of efficiency, volume and mass, in regard to the battery, supercapacitors technology and the energy management strategy chosen. [ABSTRACT FROM AUTHOR]

This paper addresses the management of a Fuel Cell (FC) – Supercapacitor (SC) hybrid power source for Electric Vehicle (EV) applications. The FC presents the main energy source and it is sustained with SCs energy storages in order to increase the FC source lifespan by mitigating harmful current transients. For this aim, the reported work proposes a Grey Wolf Optimizer (GWO) for an efficient power management of the studied hybrid power system. The key idea of the proposed approach is to incorporate the benefit of the GWO in terms of fast optimization and convergence accuracy, in order to achieve efficient energy sources exploitation and provide the desired driving performances. Simulations and experimental results verify the validity of the proposed management algorithm. • An advanced power management strategy. • Grey Wolf. • Electric Vehicle Application. [ABSTRACT FROM AUTHOR]