World Electr. Veh. J., Volume 11, Issue 3 (September 2020) – 14 articles

As the automotive industry steers towards electromobility and electric vehicle adoption surges, Brazil and other Latin-American countries remain laggards. The Brazilian scenario exhibits unique features, such as a powerful automotive sector with large investments in internal combustion engine technology and a well-established biofuels market based on flex-fuel technology. Although energy security, urban air pollution, greenhouse gas emissions mitigation, and technological advantage have been common drivers for the adoption of electric vehicles worldwide, the Brazilian immediate motivations are different, and the biofuels business ecosystem is likely to transform the path for electromobility. High tag price and public charging infrastructure absence have deeply discouraged electric vehicles adoption. A lack of regulation and a national consensus about the role of electric vehicles have been notorious. In fact, only in 2018 did the electricity regulatory agency (ANEEL) issue a resolution permitting the sale of electricity for recharging. The objective of this review was to create an outlook of the Brazilian transportation landscape. We identified relevant players, public charging infrastructure initiatives, market and other barriers, and regulation actions by consulting academic literature, media sources, and reports. We do not claim to predict the evolution of electrification. Instead, we aim to consolidate the information which can be used for decision support or strategy definition among entrepreneurs or policymakers. The main findings here are the necessity of a model for electrification able to create a synergy with biofuels and the urgency of having well-defined policies on what Brazil wants from electromobility. Full article

With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness. Full article

To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect. Full article

Widespread adoption of electric vehicles (EVs) requires additional safety countermeasures to prevent DC injection from EVs into the AC grid via Electric Vehicle Supply Equipment (EVSE). Moreover, for energy purchase, and even more so for vehicle-to-grid (V2G) services, the EVSE must conduct high precision bidirectional power and energy measurements. This paper introduces operating principles, structure, performance, and cost comparison of three current sensing technologies—current transformer, shunt and fluxgate—for metering and protection within an EVSE, concluding with recommendations among those sensors for the most beneficial applications concerning EV charging. Full article

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies; however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to reschedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO; however, under some circumstances, the technologies are on par. This emphasises the need for a detailed analysis of the local bus network in order to make an informed procurement decision. Full article

By taking a cylindrical LiFePO4 power battery as the research object, the cycle performance test was conducted under different charging current aging paths in a preset low-temperature environment and combined with EIS results to analyze the dynamic characteristics of the battery during the aging process, using the PDF (Probability Density Function) curve to analyze the change of battery energy storage characteristics, and analyze the aging mechanism of the power battery by analyzing the change in the lithium precipitation energy difference. The experimental results showed that under a low-temperature environment, the effect of increasing the charge rate is mainly reflected in slowing down the phase transformation reaction. From the analysis of lithium precipitation of the battery, it can be seen that the main mechanism of the aging of the battery is the loss of active lithium under the conditions of low-rate cycling at sub-zero temperature. The products from the side reaction between the lithium plating and the electrolyte build up on the SEI (Solid Electrolyte Interphase) film, which significantly increases the battery impedance late in the cycle. The work in this paper complements the mechanistic studies of lithium-ion batteries under different aging paths and is also useful for capacity estimation models and research on battery health. Full article

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS. Full article

Precise motor speed regulation control is essential to achieve a good gear shifting quality of the integrated motor-transmission (IMT) system, in which the relative speed between outgoing shaft and the gearwheel to be engaged can be eliminated directly through regulation of the motor speed. The speed regulation control confronts the difficulty that there exist external disturbances on the motor shaft, like the unknown load torque arised from bearing friction, oil shearing and oil churning, etc. To deal with these disturbances, a robust speed regulation controller combined a nominal proportional control and integral sliding mode control is proposed. The former is designed to achieve a good speed tracking performance and the latter provides functionality of disturbances rejection. The effects of different controller parameters for the robust controller design are assessed via simulations. Moreover, to verify the effectiveness of the combined control scheme in practical engineering use, experiments are carried out on a test bench with a real IMT powertrain system. Results indicate that the proposed approach can attain a rapid and smooth speed regulation process with a simple controller structure and good robustness. Full article

In recent years, topics of digitalisation, urbanisation and sustainability are shaping future developments in the automotive industry and pose new challenges to the individual areas of vehicle development. In the design and simulation of multiple components in the vehicle, especially in powertrain, generically generated driving cycles play an important role since they reflect representative user behavior. Nowadays, driving cycles are mainly associated with the worldwide fuel consumption and emission tests, but they are nevertheless used in many fields of vehicle development. A design based on the currently used consumption and emission cycles proves to be unsuitable, especially for electric vehicles. This is reflected in the current discussion about the large discrepancy between the driving ranges achieved when using emission cycles and those under real driving conditions. In order to minimize the energy consumption of a vehicle, the main requirement is a good efficiency of the electric machine. Here, not the maximum efficiency of the machine is decisive, but the averaged overall system efficiency during real driving behavior. The electric machine must therefore be designed for the driving cycle. To optimize the use of electric machines in the future, the actual power requirements of future vehicle models must first be determined. In the course of this paper, a compressed vehicle class specific driving cycle will be created based on real driving data using Markov chains, which can be used for powertrain dimensioning. Full article

This paper presents an aggregate current-harmonic load model using power exponential functions and built from actual measurement data during the individual charging of four different fully electric vehicles. The model is based on individual emitted current harmonics as a function of state of charge (SOC), and was used to deterministically simulate the simultaneous charging of six vehicles fed from the same bus. The aggregation of current harmonics up to the 11th was simulated in order to find the circumstances when maximal current-harmonic magnitude occurs, and the phase-angle location. The number of possible identical vehicles was set to four, while battery SOC, the start of charging, and the kind of vehicle were randomized. The results are presented in tables, graphs, and polar plots. Even though simulations did not consider the surrounding harmonics, supply-voltage variation, or network impedance, this paper presents an innovative modeling approach that gives valuable information on the individual current-harmonic contribution of aggregated electric-vehicle loads. With the future implementation of vehicle-to-grid technology, this way of modeling presents new opportunities to predict the harmonic outcome of multiple electric vehicles charging. Full article

There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation. Full article

This work presented a nonlinear control for a reversible power buck–boost converter (BBC) in order to control energy storage in a supercapacitor (SC) used in hybrid electric vehicles (HEV). The aim was to control a power converter in order to satisfy the following two requirements: (i) perfect tracking of SC current to its reference signal and (ii) asymptotic stability of the closed-loop system. The two objectives were achieved using an integral sliding mode control. In order to validate the proposed approach, an experimental prototype was built. The controller was integrated into dSPACE prototyping systems using the DS1202 card. It was clearly shown, using formal analysis, simulation, and experimental results, that the designed controller metall the objectives, namely, the stability of the system and the control of the current at its reference. Full article

This paper determines the profitability of the primary frequency regulation (FR) service considering the wear of the electric vehicle (EV) battery as a cost. To evaluate the profitability of the FR service, the cost of degradation from FR provision is separated from the degradation caused by driving usage. During FR, the power response is proportional to the frequency deviation with full activation power of 9.2 kW, when deviations are larger than 100 mHz. The degradation due to FR is found to be an additional 1–2% to the 7–12% capacity reduction of a 40 kWh Lithium-ion NMC battery pack over 5 years. The overall economic framework is applied in Denmark, both DK1 and DK2, and Japan, by considering historical frequencies. The DK2 FR market framework is taken as reference also for the Japanese and the DK1 cases. Electricity prices and charger efficiency are the two main parameters that affect the profitability of the service. Indeed, with domestic prices there is no profitability, whereas with industrial prices, despite differences between the frequencies, the service is similarly profitable with approx. 3500€ for a five-year period. Full article

The derivation of a battery electric vehicle (BEV) architecture represents a challenging task for car manufacturers. For the early development of combustion engine architectures, the required design parameters can be derived from the analysis of previously-built model series. Regarding BEV architectures, the manufacturers do not yet have a reference series of vehicles on the basis of which they can derive the essential design parameters. Therefore, these parameters are mainly estimated at high cost in the early development phase. To avoid cost-intensive changes in the further course of development it is crucial to choose the right set of design parameters. For this reason, the aim of this paper is the identification of a minimum set of design parameters, derived from the current state-of-the-art of vehicle development by a structured literature comparison. We group the results according to our definition of vehicle architecture and discuss each identified parameter to explain its relevance. The sum of all parameters presented in this paper builds a minimum set of design parameters, which can be employed as a guideline for the definition of BEV architectures in the early development stage. Full article