Figure 13 illustrates the concept of the five-step constant current charging pattern. To find the charging current in each steps different algorithms can be used, however it could be difficult and time ineffective find the optimal charging pattern.
With this charging strategy the charging current is injected into the battery in form of pulses, so that a rest period is provided for the ions to diffuse and neutralize.
The charging rate, which depends on the average current, can be controlled by varying the width of the pulses. It is claimed [ 77 , 79 ] that this method can really speed up the charging process, slow down the polarization effect and increase life cycles. Two different pulse charging methods exist: duty-fixed and duty-varied pulse-charge strategy.
According to [ 79 ], the duty-varied strategy can increase the charging speed and the charging efficiency with respect to the conventional duty-fixed method. In all the charging methods reported so far, only current and voltage limits are considered.
Anyway, these limits do not take into consideration the aging process and the side reactions occurring inside the battery; and hence they might result too conservative for new batteries and possibly dangerous for aged batteries due to the altered behavior and characteristics.
This, hence, motivates the development and the research of innovative charging algorithms which tackle the issue of the charging impact on battery state-of-health SoH and aging [ 76 , 80 ]. In fact, recently, many researches are focusing on develop new charging methods which minimize the charging time and extend the battery life at the same time [ 75 , 80 , 81 , 82 , 83 ]. This new category of charging strategies employs the electrochemical lithium-ion battery models to calculate quantitatively and almost precisely the amount of battery aging and to directly minimize the aging in a given charge time.
The electrochemical battery model estimates the internal states of a battery in a more accurate way since it is built starting from the internal microstructure of the lithium-ion battery. However, it is very arduous to precisely estimate the parameters because the electrochemical model is composed of complicated coupled partial differential equations PDEs , and involving a large number of parameters and boundary conditions it requires a large computation burden [ 82 ]. Therefore the complexity of these models often leads to the necessity for more memory and computational effort and thus they may not be practically implemented in the fast and real-time computations of EV BMS [ 86 ].
In recent years, many works focus on identifying simplified internal electrochemical models of the battery for efficiently determining the battery operation and hence finding the optimal charging profile. For example, in [ 75 ] Klein et al. In the single particle model SPM the electrodes are represented by two single spherical porous particles in which intercalation and de-intercalation phenomena take place. However, the variations in the electrolyte con-centration and in the potential are ignored.
This electrochemical model is exhaustively explained in [ 83 , 84 ]. In [ 81 ], a method to optimize the charging current for Li-ion batteries using a soft actor-critic SAC reinforcement learning algorithm is proposed.
As battery aging model Kim et al. Anyway, even if this model is more exhaustive than the SPM one, it is subject to a complicated mathematical structure including PDEs, ordinary-differential equations and algebraic equations. Therefore the corresponding model based algorithms can be computationally too arduous to be implemented in typical battery management systems BMSs [ 80 ]. In light of this, in [ 80 ], Zou et al. Another widely employed electrochemical battery model is the pseudo two-dimensional model P2D.
The first spatial dimension of this model, represented by variable x, is the horizontal axis. The second spatial dimension is the particle radius r.
The cell is comprised of three regions that imply four distinct boundaries. The full description of this model can be found in [ 85 ]. Finally, in [ 82 ], the authors present an effective method to estimate the parameters of the P2D model using a neural network-based estimation scheme.
Concerning the onboard charger, as seen in paragraph II, different technologies exist in literature and in industrial applications. The cost, dimension and weight of this product is strongly influenced by its power rating; nowadays many sizes are proposed such as: 0.
Many different power ratings are described also for off-board charger and consequently the cost varies as a function of the power and quality. Generally, the wide range of power ratings, the fast improvements and the lack of standardization in e-mobility technologies make quite difficult estimating the actual optimal power rate of charging technology and forecasting future tendencies for large interval of time.
In this paragraph, initially, using a genetic algorithm GA single-objective model, the best size of both the onboard and off-board charging technology is estimated; then, through a sensitive analysis, we try to evaluate the possible future trends in the e-mobility as the costs of the battery and of the charging technology vary.
GA is an heuristic search algorithm, which mimics the natural selection process and it is used to solve optimization problems.
GAs are being used to solve a wide variety of optimization problems in the field of EVs such as: charging scheduling [ 87 , 88 , 89 ], charging station planning and location [ 90 , 91 ] and drive train control strategy implementation [92—93]. For managing the EV charging technology, a single-objective optimization is used to determine the optimal size of the charging technology both on-board and off-board and to determine a suitable battery capacity. The proposed optimization allows to find the optimal trade-off between the onboard and off-board charger power rate.
The aim of the sizing procedure is to minimize the overall costs coming from the charging of electric vehicles. Therefore, the optimization problem is modeled in the expressions 1 , 2 and 3. First of all, the objective function representing the cost of the charging system is computed in 1. Therefore, the first factor of the function describes the cost trend as a function of the battery size. This behaviour is not perfectly linear since we assume the fact that as the battery capacity increases the payload available inside the vehicle decreases.
Also for the cost trend of the onboard battery charger a non-linear behaviour has been assumed for the same reasons explained for the battery.
In particular for this component, it can be seen that a minimum level of 6. The computation of the cost coming from the off-board charger power rating follows the same concept used for the onboard charger, with the difference that a minimum value has not been imposed. The coefficient related to the capacity of the battery and to the power rating of the off-board charger is expected to assume high values, since it concerns the reduction of the driving range anxiety, which we know to be one of the major cause limiting the penetration of the EV worldwide.
The advantages brought by these two variable are linked together and considered in the last term of the function. An increase in the capacity of the battery should be compensated by an increase in the rating of the off-board charger, otherwise it is not fully appreciated, and vice versa if the off-board charger becomes more powerful, the capacity of the battery has to limit the stresses to which the battery is exposed.
In particular, the minimum of the cost function has been performed through the use of genetic algorithms coded in Matlab. The bounds listed in 3 have been used. Figures 15 , 16 and 17 given below report the minimum of the function once the value of the coefficients changes. The starting condition considers the actual prices and benefits reported in Table 2. By using these values for the coefficients, a battery capacity of 60 kWh, an off-board charger of kW and an onboard one of 14 kW are obtained from the optimization of the cost function.
In the following second step of the study, the values of coefficients are varied, one at a time, to perform a sensitivity analysis. It is possible to notice that the capacity of the battery and the power rating of the off-board charger are not influenced by the variation of the cost of the onboard charger per kW. Finally, a forecast is done about the semiconductor devices. Thanks to all their characteristics, compared in Table 3 , wide bandgap semiconductors allow for greater power efficiency, smaller size, lighter weight and lower overall cost.
In particular, by comparing SiC and GaN properties, it can be concluded that GaN is the most suitable for low power, high frequency application while SiC material is more appropriate for high power high frequency applications [ 92 ]. The first part of this paper reviews the current conditions of EV battery charging technologies.
The most common topologies which are suitable candidates for each level of an EV charger have been presented. Level 1 and level 2 charger topologies are usually mounted inside the vehicle forming in this way the so called onboard charger. On the other side level 3 chargers are installed off board the vehicles, in this way their collection leads to the creation of the so called FCSs which are promising candidates for future EV high penetration. The most common technologies used in a FCS are multilevel converters which have a high power density and a lower current harmonic distortion.
Li-ion batteries can be recharged according many different charging techniques which can more or less complicate the charger architecture and control. Furthermore, they can be realized with very basic circuits, keeping the costs of the charger to a minimum. On the other hand, the charging strategies based on electrochemical models, taking into account the internal dynamics of the battery, consider also the aging of the battery and other constraints, hence resulting in greater accuracy and.
All this is at the expense of cost and computational difficulty. A long-term forecasting of future trends in the field of EV charging systems is a very tough task for different many reasons including the lack of standards and the continuous improvements.
In the last section of this paper a possible forecasting and estimation based on GA is described. According to the results, with the actual costs, the capacity of the battery of an electric vehicle should be around 60 kWh, the power rating of the onboard charger around 14 kW and that of the off-board fast charger around kW. In: Martinez LR ed New trends in electrical vehicle powertrains. IntechOpen, UK. Google Scholar. Ces T Transportation options in a carbon constrained world: hybrids, plug-in hybrids, biofuels, fuel cell electric vehicles, and battery electric vehicles.
Int J Hydrogen Energy 34 23 — Article Google Scholar. Renew Sustain Energy Rev — Eng Sci Technol Intern J 21 5 — Sunab L, Maa D, Tanga H A review of recent trends in wireless power transfer technology and its applications in electric vehicle wireless charging.
Yilmaz M, Krein PT Review of charging power levels and infrastructure for plug-in electric and hybrid vehicles. Tran VT, Sutanto D, Muttaqi KM The state of the art of battery charging infrastructure for electrical vehicles: topologies, power control strategies, and future trend.
A novel low cost integrated on-board charger topology for electric vehicles and plug-in hybrid electric vehicles. Yilmaz M, Krein PT Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces. SAE Tech Pap. J Phys Conf Ser Finance and Economics Discussion Series. Goldie-Scot L A behind the scenes take on lithium-ion battery prices. BloombergNEF, Accessed 30 June Fahem, D.
Chariag, L. Cao ; H. Musavi; M. Edington; W. Eberle ; W. Woo D, Joo D, Lee B On the feasibility of integrated battery charger utilizing traction motor and inverter in plug-in hybrid electric vehicles. Haghbin S, Carlson O Integrated motor drive and non-isolated battery charger based on the split-phase PM motors for plug-in vehicles. J Eng 6 — Shi C, Tang Y, Khaligh A A single-phase integrated onboard battery charger using propulsion system for plug-in electric vehicles.
Kim S, Kang F Multifunctional onboard battery charger for plug-in electric vehicles. Nguyen HV, Lee D Single-phase multifunctional onboard battery chargers with active power decoupling capability. In: Proc. IEEE Appl. Power Electron. APEC , Mar , pp. Xue L, Shen Z, Boroyevich D, Mattavelli P, Diaz D Dual active bridge-based battery charger for plug-in hybrid electric vehicle with charging current containing low frequency ripple.
Energies Dusmez S, Cook A, Khaligh A Comprehensive analysis of high quality power converters for level 3 off-board chargers. Feizi M, Beiranvand R Simulation of a high power self-equalized battery charger using voltage multiplier and phase-shifted full bridge converter for lithium-ion batteries. Shiramagond T, Lee W Integration of renewable energy into electric vehicle charging infrastructure. J Power Sour — Zhang SS The effect of the charging protocol on the cycle life of a Li-ion battery.
J Power Sour 2 — Chen L Design of duty-varied voltage pulse charger for improving Li-ion battery-charging response. IEEE Access — J Electrochem Soc 7 :A Woodhead Publishing, UK. Elmehdi M, Abdelilah M Genetic algorithm for optimal charge scheduling of electric vehicle fleet. Korotunov S, Tabunshchyk G, Okhmak V Genetic algorithms as an optimization approach for managing electric vehicles charging in the smart grid. Energies 7 4 Accessed 17 Apr Download references. You can also search for this author in PubMed Google Scholar.
Correspondence to Carola Leone. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and Permissions. Brenna, M. Download citation. Received : 08 April Revised : 07 July Accepted : 17 September Published : 02 October Issue Date : November Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search SpringerLink Search. Download PDF. Abstract Many different types of electric vehicle EV charging technologies are described in literature and implemented in practical applications.
Introduction Growing concern of carbon dioxide emissions, greenhouse effects and rapid depletion of fossil fuels raise the necessity to produce and adopt new eco-friendly sustainable alternatives to the internal combustion engine ICE driven vehicles. Table 1 Charging power levels Full size table. Onboard Charger Battery chargers can be implemented inside on-board or outside off-board the vehicle.
Charging system configuration for electric vehicle. Full size image. Full-bridge rectifier with conventional PFC boost converter.
Full-bridge rectifier with interleaved PFC boost converter. Example of two-stage onboard charger with LLC converter. Off-Board Charger Level 3 charger, because of their rating powers, are usually installed outside the vehicle off-board.
Najeeb G. Abdulhamid Researcher. Robin Abraham Senior Director. Mahmoud Adada Principal Engineering Manager. Logan Adams Hardware Engineer. Deanne Adams Xbox Researcher. Kaska Adoteye Senior Data Scientist. Vidhan Agarwal Senior Software Engineer.
Sharad Agarwal Senior Principal Researcher. Vishesh Agarwal Software Engineer. Janhavi Agrawal Research Software Engineer. Ankita Agrawal Data Scientist. Faisal Ahmed. Hao Ai Senior Data Scientist. Robert Aichner Principal Program Manager.
0コメント