scholarly journals Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7755
Author(s):  
Benjamin Schaden ◽  
Thomas Jatschka ◽  
Steffen Limmer ◽  
Günther Robert Raidl

The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice.

2021 ◽  
Vol 22 (1) ◽  
pp. 101-111
Author(s):  
Kamal Singh ◽  
Anjanee Kumar Mishra ◽  
Bhim Singh ◽  
Kuldeep Sahay

Abstract This work is targeted to design an economical and self-reliant solar-powered battery charging scheme for light electric vehicles (LEV’s). The single-ended primary inductance converter (SEPIC) is utilized to enhance the performance of solar power and battery charging at various solar irradiances. Various unique attributes of a SEPIC converter offer the effective charging arrangement for a self-reliant off-board charging system. Further, the continuous conduction mode (CCM) function of the converter minimizes the elementary stress and keeps to maintain the minimum ripples in solar output parameters. A novel maximum power point tracking (MPPT) approach executed in the designed system requires only the battery current to track the maximum power point (MPP) at various weather situations. Both the simulated and real-time behaviors of the developed scheme are examined utilizing a battery pack of 24 V and 100 Ah ratings. These responses verify the appropriateness of the designed system for an efficient off-board charging system for LEV’s.


Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4717 ◽  
Author(s):  
Sylvester Johansson ◽  
Jonas Persson ◽  
Stavros Lazarou ◽  
Andreas Theocharis

Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid’s capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.


Author(s):  
Yue Wang ◽  
David Infield ◽  
Simon Gill

This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.


Author(s):  
Stefan Meisenbacher ◽  
Karl Schwenk ◽  
Johannes Galenzowski ◽  
Simon Waczowicz ◽  
Ralf Mikut ◽  
...  

2013 ◽  
Vol 6 (4) ◽  
pp. 863-874 ◽  
Author(s):  
Jens Schmutzler ◽  
Claus Andersen ◽  
Christian Wietfeld

2014 ◽  
Vol 4 (4) ◽  
pp. 89-96 ◽  
Author(s):  
Ching Chuen Chan ◽  
Linni Jian ◽  
Dan Tu

Author(s):  
K. Jyotheeswara Reddy ◽  
N. Sudhakar ◽  
S. Saravanan ◽  
B. Chitti Babu

AbstractHigh switching frequency and high voltage gain DC-DC boost converters are required for electric vehicles. In this paper, a new high step-up boost converter (HSBC) is designed for fuel cell electric vehicles (FCEV) applications. The designed converter provides the better high voltage gain compared to conventional boost converter and also reduces the input current ripples and voltage stress on power semiconductor switches. In addition to this, a neural network based maximum power point tracking (MPPT) controller is designed for the 1.26 kW proton exchange membrane fuel cell (PEMFC). Radial basis function network (RBFN) algorithm is used in the neural network controller to extract the maximum power from PEMFC at different temperature conditions. The performance analysis of the designed MPPT controller is analyzed and compared with a fuzzy logic controller (FLC) in MATLAB/Simulink environment.


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