Real Electric Vehicle Research Articles

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

PM-Assisted Synchronous Reluctance Machine Flux Weakening Control for EV and HEV Applications

In this paper, a novel robust torque control strategy for permanent magnet assisted synchronous reluctance machine drives applied to electric vehicles and hybrid electric vehicles is presented. Conventional control techniques can highly depend on machine electrical parameters, leading to poor regulation under electrical parameters deviations or, in more serious cases, instabilities. Additionally, machine control can be lost if field weakening is not properly controlled and, as a consequence, uncontrolled regeneration is produced. Thus, advanced control techniques are desirable to guarantee electric vehicle drive controllability in the whole speed/torque operation range and during the whole propulsion system lifetime. In order to achieve these goals, a combination of a robust second-order current-based sliding mode control and a look-up table/voltage constraint tracking based hybrid field weakening control is proposed, improving the overall control algorithm robustness under parameter deviations. The proposed strategy has been validated experimentally in a full-scale automotive test bench (51-kW prototype) for being further implemented in real hybrid and electric vehicles.

Design and optimization of switched reluctance motor for electrical vehicles

This paper deals with replacement of an existing DC motor by an optimized switched reluctance motor (SRM) for an automotive application, especially for electrical vehicles. The SRM was designed by means of 2D and 3D software based on finite element method and independently designed by a new optimization procedure. The input parameters were taken into account on requirements for a real electrical vehicle, e.g., maximum torque, acceleration and power at different speeds. The designed and subsequently optimized motor was constructed, and the static nonlinear and dynamic performances and parameters were measured and compared with the calculated ones.

Impact of Heating System on the Range of an Electric Vehicle

For an accurate evaluation of the driving range of an electric vehicle (EV), many conditions must be considered (road profile, traffic influence, etc.). However, a cabin heating system is not often considered despite its significant impact. In this paper, the impact of the cabin heating system is studied on the driving range of an EV. A real EV is used as a reference. A multidomain model is developed and validated by experimental results on the vehicle. From this validated model, the impact of the heating system on the range is evaluated up to 30% in cold climatic conditions. In a classical approach, an ecodriving mode enables an increase in the range by reducing the vehicle acceleration and velocity. When considering the heating system, the energy balance is more complex: the eco-driving mode can lead to an over-consumption of energy. A better compromise is required as a function of the climatic condition.

Experimental Study on Asymmetric Wireless Power Transfer System for Electric Vehicle Considering Ferrous Chassis

Steel chassis of an electric vehicle (EV) could reduce the efficiency of a wireless power transfer (WPT) system installed on which. To improve the performance of practical EV-oriented wireless charging systems, asymmetric WPT systems in ferrous environment are studied in this paper. First, inevitable adverse effects of the ferrous chassis on a nearby WPT coil are investigated. Then, impedance matching criteria to achieve the theoretical maximal efficiency of a general WPT system are theoretically elaborated. Further, one common symmetric and two asymmetric EV oriented WPT systems are built and comparative experiments on these three WPT systems are conducted. Vulnerability to ferrous environment, throughput power, efficiency, misalignment sensibility, as well as electromagnetic field radiation of these three WPT systems are assessed and compared in both ideal environment and chassis-emulated ferrous environment. Experimental results indicate that, asymmetric WPT system with a smaller transmitter and a larger receiver is more suitable to be implemented on a real EV for wireless charging.

Simulation of Real Driving Cycles of Electric Cars in Laboratory Conditions

This article is focused on the area of research and development of vehicles with electric drive. The paper presents the results of research based on real electric vehicle EDISON EV as the real traffic in laboratory conditions reflecting. The analysis of special driving style was created in order to increase electric vehicle range.

An Energy Management Strategy for a Concept Battery/Ultracapacitor Electric Vehicle With Improved Battery Life

Using multi-input converters (MICs) in hybrid energy storage systems (HESSs) presents several advantages, such as low component count, control simplicity, and fully control of source energies. The power levels of sources in these systems need to be determined wisely by an energy management strategy (EMS). This paper presents an EMS for a battery/ultracapacitor (UC) HESS including a bidirectional MIC for electric vehicles (EVs). Thanks to the fact that energy flow between battery and UC is free in this MIC, the proposed EMS not only regulates the state-of-charge of UC but also smooths the battery power profile by using a fuzzy logic controller and a rate limiter. Therefore, it results in a sustainable HESS with longer battery life. Through a simulation study and an experimental setup including a real EV, the performance of the proposed system is evaluated comprehensively. Then, based on experimental results, battery cycle-life improvement due to the battery/UC hybridization is explored.

Novel state-of-health diagnostic method for Li-ion battery in service

The development of improved State-of-Health (SoH) diagnostic methods is a current research topic for battery-powered applications. For instance, the current rapid development of Electric Vehicles (EV) creates a strong demand for an accurate and reliable on-board SoH indicator during operation. Such an indicator is a key parameter required to optimize battery energy management and to track the degradation of the system performance. The electrochemical impedance spectrum (EIS) of an electrochemical system is a powerful lab-based diagnostic technique, usually measured using a frequency response analyzer. In this paper, we present an innovative diagnostic technique based on analysis of free voltage and current signals to give a so called “quasi-electrochemical impedance spectrum” (QEIS) and demonstrate its application on a Li-ion battery during a real EV duty cycle. It is worth noting that in our technique no additional signal is applied to the cell, since the current flowing into cells during use on-board is directly processed in the data treatment step.Commercial batteries (1.4Ah cylindrical LiFePO4/graphite cell) were selected in this study to validate the diagnostic method in the framework of an applied case study related to an electric school bus demonstrator. In order to study the capability of QEIS measurements as a diagnostic tool for SoH of Li-ion cells, a test procedure including ageing phases has been defined to characterise Li-ion cells before and during ageing. Voltage and current signals were treated by Fast Fourier Transform (FFT) in order to determine the QEIS spectra of Li-ion cells under study. Then, SoH prediction algorithms have been obtained from a mathematical analysis of the impedance parameters sensitive to SoH.

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation.

Integration of sampling based battery state of health estimation method in electric vehicles

Battery cost is one of the crucial parameters affecting high deployment of Electric Vehicles (EVs) negatively. Accurate State of Health (SoH) estimation plays an important role in reducing the total ownership cost, availability, and safety of the battery avoiding early disposal of the batteries and decreasing unexpected failures. A circuit design for SoH estimation in a battery system that bases on selected battery cells and its integration to EVs are presented in this paper. A prototype microcontroller has been developed and used for accelerated aging tests for a battery system. The data collected in the lab tests have been utilized to simulate a real EV battery system. Results of accelerated aging tests and simulation have been presented in the paper. The paper also discusses identification of the best number of battery cells to be selected for SoH estimation test. In addition, different application options of the presented approach for EV batteries have been discussed in the paper.

A bi-level optimisation framework for electric vehicle fleet charging management

The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system.

How Might People Near National Roads Be Affected by Traffic Noise as Electric Vehicles Increase in Number? A Laboratory Study of Subjective Evaluations of Environmental Noise.

We face a likely shift to electric vehicles (EVs) but the environmental and human consequences of this are not yet well understood. Simulated auditory traffic scenes were synthesized from recordings of real conventional and EVs. These sounded similar to what might be heard by a person near a major national road. Versions of the simulation had 0%, 20%, 40%, 60%, 80% and 100% EVs. Participants heard the auditory scenes in random order, rating each on five perceptual dimensions such as pleasant–unpleasant and relaxing–stressful. Ratings of traffic noise were, overall, towards the negative end of these scales, but improved significantly when there were high proportions of EVs in the traffic mix, particularly when there were 80% or 100% EVs. This suggests a shift towards a high proportion of EVs is likely to improve the subjective experiences of people exposed to traffic noise from major roads. The effects were not a simple result of EVs being quieter: ratings of bandpass-filtered versions of the recordings suggested that people’s perceptions of traffic noise were specifically influenced by energy in the 500–2000 Hz band. Engineering countermeasures to reduce noise in this band might be effective for improving the subjective experience of people living or working near major roads, even for conventional vehicles; energy in the 0–100 Hz band was particularly associated with people identifying sound as ‘quiet’ and, again, this might feed into engineering to reduce the impact of traffic noise on people.

Mechatronics design and experimental verification of an electric-vehicle-based hybrid thermal management system

In this study, an electric-vehicle-based thermal management system was designed for dual energy sources. An experimental platform developed in a previous study was modified. Regarding the mechanical components, a heat exchanger with a radiator, proportional valve, coolant pipes, and coolant pump was appropriately integrated. Regarding the electric components, two heaters emulating waste heat were controlled using two programmable power supply machines. A rapid-prototyping controller with two temperature inputs and three outputs was designed. Rule-based control strategies were coded to maintain optimal temperatures for the emulated proton exchange membrane fuel cells and lithium batteries. To evaluate the heat power of dual energy sources, driving cycles, energy management control, and efficiency maps of energy sources were considered for deriving time-variant values. The main results are as follows: (a) an advanced mechatronics platform was constructed; (b) a driving cycle simulation was successfully conducted; and (c) coolant temperatures reached their optimal operating ranges when the proportional valve, radiator, and coolant pump were sequentially controlled. The benefits of this novel electric-vehicle-based thermal management system are (a) high-efficiency operation of energy sources, (b) low occupied volume integrated with energy sources, and (c) higher electric vehicle traveling mileage. This system will be integrated with real energy sources and a real electric vehicle in the future.

Energy management for a commercial building microgrid with stationary and mobile battery storage

This paper investigates the Demand Side Management (DSM) in a commercial building microgrid with solar generation, stationary Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicle (EV) integration. Taking into consideration of a comprehensive pricing model, we first formulate a deterministic DSM as a mixed integer linear programming problem, assuming perfect knowledge of the uncertainties in the system. A two-stage stochastic DSM is further developed that addresses the stochastic nature in solar generation, loads, EV availabilities and EV energy demands. The proposed DSMs are validated with real solar generation, loads, BESS and EV data using sample average approximation. Detailed case studies show that the stochastic DSM outperforms its deterministic counterpart for cost saving for a wide range of prices, though at the expense of higher computational time. Computational results also demonstrate that moderate number of EVs helps to cut down the overall operation cost, which sheds light on the benefit of future large scale EV integration to smart buildings.

Harmonic Impact of Plug-In Hybrid Electric Vehicle on Electric Distribution System

This paper presents the harmonic effects of plug-in hybrid electric vehicles (PHEV) on the IEEE 37-bus distribution system at different PHEV penetration levels considering a practical daily residential load shape. The PHEV is modeled as a current harmonic source by using the Open-Source Distribution System Simulator (OpenDSS) and DSSimpc software. Time series harmonic simulation was conducted to investigate the harmonic impact of PHEV on the system by using harmonic data obtained from a real electric vehicle. Harmonic effects on the system voltage profile and circuit power losses are also investigated by using OpenDSS and MATLAB software. Current/voltage total harmonic distortion (THD) produced from the large scale of PHEV is investigated. Test results show that the voltage and current THDs are increased up to 9.5% and 50%, respectively, due to high PHEV penetrations and these THD values are significantly larger than the limits prescribed by the IEEE standards.

Simulation and Analysis of the Effect of Real-World Driving Styles in an EV Battery Performance and Aging

This paper presents the large data analysis of the real-world driving cycles and studies the effect of different driving styles on the electric vehicle (EV) battery performance and aging. For this study, a MATLAB-based software tool, real EV cycle (REV-cycle) analyzer, is developed. The real-world driving data are recorded from the I-80 highway, CA, USA. In this study, driving cycles are classified into three styles as aggressive, mild, and gentle driving based on their average acceleration. Also, two standard driving cycles (EUDC and HWFET) are simulated by the software and the results are compared. The results show that the real driving cycles are very different from the standard cycles. Also, it is observed that the traffic flow affects the driving style, as the drivers tend to drive more aggressive during the light traffic hours of the highway, while the heavy traffic limits the driver’s aggressive behavior. On the other hand, the driving style has considerable effect on the energy consumption and the battery aging. The aggressive driving style demands higher average power from the battery compared to the mild and gentle driving style. From the aging point of view, the aggressive driving style leads to higher ${ C_\text{rate}}$ demand from the battery and it expedited the capacity fade process compared to the mild and gentle driving styles.

Trajectory-interception based method for electric vehicle taxi charging station problem with real taxi data

ABSTRACTTaxi fleets are suitable for the first stage of deployment of electric vehicles (EV) because EV taxis can provide chances for more people to experience EV. However, the lack of charging facilities and limited operation range are barriers to the adoption of EV taxis. As EV taxis can charge when there is no passenger in the vehicle, the conventional origin-destination (OD)-based flow-interception method for the charging station problem is not appropriate. In this study, we present a new EV taxi charging facility planning model that uses the trajectory-interception method with real taxi trajectory data and electric vehicle operation data. Global positioning system (GPS) data from 1,000 vehicles (taxis) and taxi battery performance data from three EVs are used. We developed an optimization algorithm to find optimal charger distribution considering charger installation cost and waiting delay simultaneously based on the evolution algorithm. The results show that the maintenance cost of the chargers and the charging stations is more sensitive than the opportunity cost of taxis, including additional travel distance and time to minimize the total cost. The methodology proposed in this study provides a robust result for the charging station location problem and can be widely applied to other cities with similar situations.

Behavioral Characterization of Electric Vehicle Charging Loads in a Distribution Power Grid Through Modeling of Battery Chargers

The plug-in electric vehicle (PEV) is a new atypical load in power systems. In future, PEV load will play a significant role in the distribution grids. This integrated load into the power grid may overload the system components, increase power losses, and affect the voltage profile in the distribution systems. Currently, the constant power load model is most commonly used for the modeling of the electric vehicle (EV) load that considers the EV loads as constant power elements without considering the voltage dependence of the EV charging system. EV load demand cannot be considered as a constant power due to the fact that modeling as a constant power load will not provide accurate information about the behavior of the charging system during the charging process. In this paper, an accurate model representing the realistic behavior of EV load is developed which is based on the ZIP load model with the ZIP parameters established through the realistic EV load data. The proposed model can be used to analyze the true behavior of the EV charger integrated to an electricity grid and determine the impacts of EV charging load on the grid. A realistic charging system was used to test and capture the EV load behavior and extract the coefficients of the EV ZIP load model, which have been verified using computer simulations and laboratory experiments. Additionally, a comparative study between the proposed ZIP load model and the constant power load model was carried out, and the results were verified with the practical EV load data. The results confirm that EV represented using constant power load will not provide the true reflection of the EV load behavior and the EV impacts on the power grid.

Advanced Modeling of a 2-kW Series–Series Resonating Inductive Charger for Real Electric Vehicle

This paper focuses on the design of a contactless charging system for electric vehicles (EVs) using inductive loops connected to a resonance converter. The study carries out the system operation, electromagnetic radiation, and testing. It is shown that the presence of the chassis leads to a double resonance and has a strong influence on the radiated fields.

Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model

Now the lithium ion batteries are widely used in electrical vehicles (EV). The battery modeling and state estimation is of great significance. The rigorous physic based electrochemical model is too complicated for on-line simulation in vehicle. In this work, the simplification of physics-based model lithium ion battery for application in battery management system (BMS) on real electrical vehicle is proposed. Approximate method for solving the solid phase diffusion and electrolyte concentration distribution problems is introduced. The approximate result is very close to the rigorous model but fewer computations are needed. An extended single particle model is founded based on these approximated results and the on-line state of charge (SOC) estimation algorithm using the extended Kalman filter with this single particle model is discussed. This SOC estimation algorithm could be used in the BMS in real vehicle.