Search Results (16)

The accurate estimation of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is essential for ensuring safety and enabling effective battery health management systems. To address this challenge, data-driven solutions leveraging advanced machine learning and deep learning techniques have been developed. This study introduces a novel framework, Deep Neural Networks with Memory Features (DNNwMF), for predicting the RUL of LIBs. The integration of memory features significantly enhances the model’s accuracy, and an autoencoder is incorporated to optimize the feature representation. The focus of this work is on feature engineering and uncovering hidden patterns in the data. The proposed model was trained and tested using lithium-ion battery cycle life datasets from NASA’s Prognostic Centre of Excellence and CALCE Lab. The optimized framework achieved an impressive RMSE of 6.61%, and with suitable modifications, the DNN model demonstrated a prediction accuracy of 92.11% for test data, which was used to estimate the RUL of Nissan Leaf Gen 01 battery modules. Full article

Electric and hybrid vehicles have become widespread in large cities due to the desire for environmentally friendly technologies, reduction of greenhouse gas emissions and fuel, and economic advantages over gasoline and diesel vehicles. In electric vehicles, overheating, vibration, or mechanical damage due to collision with an object or another vehicle can lead to the failure of lithium-ion batteries up to thermal runaway and fire. Therefore, the development of battery safety control systems is one of the most important factors contributing to the large-scale electrification of public and private transport. This review examines the design features of the location and management of the battery pack to achieve maximum safety and operational efficiency when using an electric vehicle. The power characteristics and life-cycles of various types of lithium-ion batteries depending on the chemical nature of their electrodes are considered, using the example of commercial vehicles’—Tesla, Nissan Leaf, Porsche Taycan, Zeekr, and Chevrolet Volt—strategic technologies for the placement and packaging of batteries, and battery cooling and monitoring systems (State of Health and State of Charge) are also discussed. In conclusion, the current challenges in the field are summarized and promising research directions are proposed. Full article

This analysis compares the energy efficiency and operational costs of combustion vehicles (Hyundai Accent 1.6 L and Chevrolet Sail 1.5 L) with the Nissan Leaf, an electric vehicle, under current fuel and electricity pricing in Ecuador. Combustion vehicles, converting gasoline into mechanical energy, demonstrate substantial energy losses, leading to higher operational costs, especially with recent gasoline price hikes to USD 2.722 per gallon. In stark contrast, the Nissan Leaf exhibits significantly greater energy efficiency, consuming only 15–20 kWh per 100 km, which translates to lower running costs (USD 11.20 to fully charge a 40 kWh battery). Despite the clear economic and environmental benefits of electric vehicles, their adoption in Ecuador is hampered by geographical challenges such as diverse terrain that can affect vehicle range and battery longevity. Moreover, the limited and uneven distribution of EV charging stations, mostly concentrated in urban areas, poses significant barriers. For broader implementation, a strategic expansion of the EV infrastructure and careful consideration of the national energy grid’s capacity to support increased electric vehicle uptake are essential. Addressing these challenges is crucial for realizing the full potential of electric vehicles in enhancing Ecuador’s sustainability and energy independence. Full article

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article

In this paper, a modular multi-input, single output DC/DC converter is proposed to enhance the energy management of a fast-charging station for electric vehicles (EVs). The proposed bidirectional converter can work in different modes of operation with fewer components and a modular design to extend the input power sources and increase the charging power rate. The converter has several merits compared to the conventional converters, such as centralizing the control, reducing power devices, and reducing power conversion stages. By using MATLAB/Simulink, the converter was tested in many operation modes and was used to charge a Nissan Leaf EV’s battery (350 V, 60 Ah) from hybrid sources simultaneously and individually in power up to (17 kW). In addition, it was tested on a hardware scale at a low power rate (100 W) for the validation of the simulation work and the topology concept. In addition, its different losses and efficiency were calculated during the different operation modes. Full article

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site. Full article

In the last few years, the growing demand for electric vehicles (EVs) in the transportation sector has contributed to the increased use of electric rechargeable batteries. At present, lithium-ion (Li-ion) batteries are the most commonly used in electric vehicles. Although once their storage capacity has dropped to below 80–70% it is no longer possible to use these batteries in EVs, it is feasible to use them in second-life applications as stationary energy storage systems. The purpose of this study is to present an embedded system that allows a Nissan^® LEAF Li-ion battery to communicate with an Ingecon^® Sun Storage 1Play inverter, for control and monitoring purposes. The prototype was developed using an Arduino^® microcontroller and a graphical user interface (GUI) on LabVIEW^®. The experimental tests have allowed us to determine the feasibility of using Li-ion battery packs (BPs) coming from the automotive sector with an inverter with no need for a prior disassembly and rebuilding process. Furthermore, this research presents a programming and hardware methodology for the development of the embedded systems focused on second-life electric vehicle Li-ion batteries. One second-life battery pack coming from a Nissan^® Leaf and aged under real driving conditions was integrated into a residential microgrid serving as an energy storage system (ESS). Full article

This study aims to investigate the consumer acceptance of Vehicle-to-Grid (V2G) charging of electric vehicle (EV) drivers. To the best of the authors’ knowledge, this is the first V2G acceptance study that is based on actual users’ experience of V2G charging. A test set up with a V2G charge point at a solar carport was constructed at the Delft University of Technology. Seventeen participants in the study were given access to a V2G-compatible Nissan LEAF and the constructed V2G charging facilities, after which they were interviewed. Clear communication of the impacts of V2G charging cycles on EV batteries, financial compensation covering these impacts, real-time insight on the battery state-of-charge and the ability to set operational parameters through a user-friendly interface were all found to foster acceptance. The main barriers for acceptance were the uncertainty associated with battery state-of-charge, the increased need for planning charging and trips, the increased anxiety about the ability of the vehicle to reach its destination, economic and performance-related effects on the EV’s battery and the restriction of the freedom that users associated with their personal vehicles. The participants were found to be divided across high, conditional and low acceptance of V2G charging. The use of V2G charging over the trial period was found to inform their opinions: tangible factors such as range anxiety and the user interface were given more importance than abstract concepts such as lack of standards that were discussed by users without experience of V2G charging. Our study indicates that V2G charging in its current form is acceptable to a section of current EV users. The discussion provides insights on extending the relevance of our findings across other user groups and over further developments in the field. Full article

Balancing energy demand and supply will become an even greater challenge considering the ongoing transition from traditional fuel to electric vehicles (EV). The management of this task will heavily depend on the pace of the adoption of light-duty EVs. Electric vehicles have seen their market share increase worldwide; the same is happening in Portugal, partly because the government has kept incentives for consumers to purchase EVs, despite the COVID-19 pandemic. The consequent shift to EVs entails various challenges for the distribution network, including coping with the expected growing demand for power. This article addresses this concern by presenting a case study of an area comprising 20 municipalities in Northern Portugal, for which battery electric vehicles (BEV) sales and their impact on distribution networks are estimated within the 2030 horizon. The power required from the grid is estimated under three BEV sales growth deterministic scenarios based on a daily consumption rate resulting from the combination of long- and short-distance routes. A Monte Carlo computational simulation is run to account for uncertainty under severe EV sales growth. The analysis is carried out considering three popular BEV models in Portugal, namely the Nissan Leaf, Tesla Model 3, and Renault Zoe. Their impacts on the available power of the distribution network are calculated for peak and off-peak hours. The results suggest that the current power grid capacity will not cope with demand increases as early as 2026. The modeling approach could be replicated in other regions with adjusted parameters. Full article

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions. Full article

The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes. Full article

Plug-in electric vehicles (PEVs) are expected to play a role as power grid ancillary service providers through vehicle-to-grid (V2G) chargers, enabling higher levels of renewable electricity penetration. However, to fully exploit the storage capacity of PEVs and fast responsiveness, it is crucial to understand their operational characteristics. This work proposes a characterization procedure for V2G systems providing grid services. It extends the existing literature on response time, AC/DC conversion and reactive power assessment. Illustrative results were obtained by implementing the procedure using a Nissan Leaf battery electric vehicle (BEV) connected to a remotely operated commercial V2G CHAdeMO charger. The V2G system was characterized as having a relative inaccuracy and variability of response inferior to 3% and 0.4%, respectively. Its average communication and ramping times are 2.37 s and 0.26 s/kW, respectively. Its conversion efficiency and power factor both showed degradation in the power values below 50% of the charger’s nominal power. Moreover, the proposed visualizations revealed that: i) the V2G system implements power requests for the DC power flow; ii) the power factor control algorithm was nonoperational; and iii) the acquired data can leverage statistical models that describe the operation of V2G systems (which is of extreme value for researchers and operators). Full article

In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency. Full article

With the advance of manufacturing technologies and increasing attention on environment protection, electric vehicles (EVs) are expected to see a fast development. However, large adoption of EVs may impose a high power demand on the grid and may affect the network infrastructures. Controlled power consumption for EV charging and home appliances could help to reduce such pressures on power systems. This paper aims at analyzing the impacts of integration control for EV charging, controllable home appliances, vehicle-to-home (V2H) and home based batteries on neighbourhood level network. Simulation results are presented for Nissan Leaf and Tesla Model S. Full article

Paper deals with an extensive experimental activity carried out in Italy by ENEA Research Lab on Low impact vehicles and by the Energy engineering group of the University of L'Aquila about the energy efficiency of quick vehicle battery charging using a DC CHAdeMO compliant recharging 50 kW infrastructure. Both the charger and the vehicle (a Nissan Leaf) battery were fully monitored to gather detailed information about their behaviour at different power loads. The performances of the battery pack equipping the vehicle have also been monitored and evaluated through an extensive campaign, both on typical urban and extra-urban uses, and on vehicle rolling test bench. Full article