Latest revision as of 13:21, 3 May 2024

Abstract

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (${\textstyle 3\times 3}$). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry. From the experimental analysis, it is clearly proves that the proposed model achieved nearly 98% to 99% of accuracy, precision, recall and F-score, when compared with existing techniques.

Keywords: Electric vehicle, deep residual attention network, artificial butterfly optimization algorithm, charging behaviour information, energy needs

1. Introduction

Vehicle energy consumption is a subject that has been extensively studied and evaluated. The influence of driving styles, ITS technologies, and other intelligent systems on fuel usage was the primary focus of study until very recently [1]. The core of EV charging prediction involves analyzing historical data related to vehicle charging patterns, such as duration, frequency, and energy consumption, along with contextual factors like time of day, location, and even weather conditions. By applying sophisticated algorithms, these systems can predict peak charging times, suggest optimal charging schedules, and even recommend when additional resources might be needed to handle high demand. The benefits of accurate EV charging predictions extend beyond individual convenience to impact broader energy management systems. Utilities can use these predictions to balance loads more effectively, integrate more renewable energy sources, and reduce overall carbon footprints. For EV infrastructure providers, predictive analytics can guide the expansion of charging networks, ensuring that new stations are built where they will be needed most.

Recently, the energy consumption of electric vehicles (EVs) has been included in these analyses. The goals and methods used to calculate EV energy consumption studies fall into one of many categories. Research documents the creation of energy models for optimization [2], effect analysis on energy consumption [3], and worldwide energy consumption [4] as a consequence of the adoption of EVs or hybrids. Predictions of the (all-electric) range may sometimes be made using the energy model [5]. EV consumption from data [6] or test cycles or creating a vehicle model that simulates are the two methods used to determine energy consumption. To anticipate more accurate figures for energy consumption, real-world measurements are preferable [7], but this method depends on accessible data and statistical modelling and is typically decoupled behaviour. Instead, modelling the vehicle's dynamics and drivetrain behaviour provides a direct relationship to these elements, making it easier to pinpoint the characteristics of the powertrain that have the greatest impact on fuel economy [8].

At least in the case of personal automobiles and other light transport vehicles, it is generally agreed that EVs are the future of environmentally responsible transportation [9]. This is because the cars have a feature called zero local emissions, which significantly aggregates [10]. However, EVs' battery charging and powering energy mix still have issues. This combination may result in a greater overall CO2 output due to the electricity generation process necessary for EV operation/use [11]. A growing number of manufacturers have adopted electric propulsion technology over the last few decades as a result of the many studies completed

However, progress in technology regarding the energy source of force for EVs is a long way from delivering sophisticated and energy-efficient technical and technological solutions, and certain pressing challenges must be addressed immediately in this area [12]:

• Determining a battery knowledge that offers the best low charging time, high lifecycle,

• operational safety decreased production prices;

• Calculating well-to-wheel emissions due to the vigour mix used in the charging procedure;

Some of the real obstacles to EVs' widespread penetration of the automotive market are the ones stated above, but customers' perceptions and attitudes also play a crucial role. There has been several research on customer and user sentiment towards EV uptake and purchase [13].

Despite the intriguing potential, a few things could still be improved, such as the charging period and the necessity for public charging. Even while recharging times for EVs have been substantially faster in recent years, they are still much longer than those for fueling internal combustion engine cars [14]. Extremely fast and wireless charging are two potential examples of emerging technologies that must overcome several obstacles before they can be widely used. Because of the lack of private charging stations, most EV owners must make do with public ones, which burdens the electrical system because of the EVs' high power needs [15]. Uncoordinated charging behaviour should be avoided to prevent power grid deterioration and breakdowns. Managing charging station schedules more effectively is the best option. There is a wealth of literature on efficient scheduling with data-driven methods, such as optimization and metaheuristic strategies. Charging behaviour has also been analyzed using transaction data and interviews with EV drivers to account for the influence of psychological variables.

Electric vehicles (EVs) are central to the global strategy for reducing carbon emissions and combatting climate change. As the adoption of EVs accelerates, efficient and effective charging infrastructure becomes increasingly critical. This paper aims to explore the challenges associated with EV charging behavior, which are significant for several reasons such as Grid Integration, Economic Impacts, Environmental Benefits and Technological Advancements. By addressing these challenges, this research aims to contribute to the development of more efficient, user-friendly, and sustainable charging infrastructure, thereby supporting the broader adoption of EVs. This is essential not only for environmental sustainability but also for the economic and social shifts toward cleaner transportation solutions.

We've devised a new method that helps remedy these drawbacks in three significant ways.

1. A redesigned DRA-Net was presented with a better mechanism to detect EVs' charging behaviour. This greatly enhances the model's accuracy, resilience, and localization capabilities.The new design of DRA-Net includes improved localization features that allow for more precise identification of the location of charging events. This is particularly important for spatial analysis of EV charging demands and infrastructure planning.

2. Second, unlike traditional models, DRA-Net recognizes targets against complicated backgrounds. Unlike traditional models which often struggle in cluttered environments, DRA-Net now employs sophisticated pattern recognition and machine learning algorithms that excel in distinguishing EVs and their charging status in complex scenes. This capability is vital for effective deployment in varied and unpredictable urban settings where backgrounds can significantly vary.

3. The classification accuracy is enhanced by the BOA model's optimal weight selection in the suggested perfect. This optimization is achieved through a novel algorithm that evaluates and adjusts weights dynamically based on real-time data, significantly enhancing the classification accuracy of the charging behavior.

The residue of the paper is laid out as shadows. In Part 2, you'll find the bibliography. The suggested model is explained in Section 3, the trial investigation is discussed in Section 4, and the conclusions are drawn in Section 5.

2. Related works

Ullah et al. [16] created a one-of-a-kind interpretable system to forecast where EVs would plug in. The study used two years of data on regular and fast charging events from 500 EVs in Japan. To predict customer preferences about charging stations, the XGBoost model achieved better than the other ML classifiers. Further, the recently developed SHAP approach was utilized to ascertain the importance of characteristics and the intricate nonlinear interaction effects of various station selection behaviours. Preliminary results from this study suggest that an interpretable ML model may be created by merging ML models with SHAP to predict EV drivers' preferences for charging stations.

Using the Variational-Bayesian Gaussian-mixture model and a dataset of more than 220,000 actual charging records, Cui et al. [17] demonstrate in-depth comprehension of fast-charging behaviour among EV users at public stations. The cluster model prioritizes charging-related elements such as charging energy and charging time, with an emphasis on the time spent in the home after charging, to enhance charge suggestion approaches and power distribution. Our proposed method for predicting charging behaviour is based on behavioural libraries using stacking regression technology, which was motivated by the potential future uses of the charging behaviour cluster shown in the research as mentioned earlier. The outcomes prove that the suggested paradigm can accurately anticipate and assess the relevance of charging behaviour.

To predict EV charging durations, Alshammari and Chabaan [18] utilized an Ant Colony Optimisation metaheuristic to fine-tune the parameters of an ensemble machine learning approach that XGBoost. The suggested Ensemble Machine Learning Ant Colony Optimisation (EML_ACO) method was shown to achieve training results of MAPE. The calculated values for R2 are 12.4 per cent, MAE 13.3 per cent, RMSE 21.1 per cent, and MAPE 12.4 per cent.

Zhang et al. [19] analyzed the real-world travel and charge passenger vehicles in Beijing over three months to better predict future charging needs. We provide a "multi-level & multi-dimension" framework for extracting travel and charging characteristic characteristics based on our analysis of more than 60 such components spanning electric vehicle behaviour's time, space, and energy dimensions. Next, a two-stage GMM and K-means-based clustering model will be provided to classify EV owners into six subgroups according to their shared and differentiated driving behaviours, preferences, and other factors. In addition, a trip-chain-demand forecast model is built to produce cars in a certain category, better reflecting real-world conditions. We combined the results of individual charge prediction runs at a three-dimensional resolution of 0.46 km and a temporal resolution of 15 min to accurately demonstrate the proposed model's ability to forecast future charging needs in the real world. The findings may aid in studying charging behaviours' effect on grid demand and developing a more effective charging infrastructure.

Stochastic user equilibrium (SUE) is introduced by Liu and Liu [20] as a novel method for predicting the accurate spatial-temporal load in sync with traffic events. Predicting EV charging loads using SUE and trip chains is proposed to describe how EVs perform under synchronized traffic situations better precisely. Then, the expanded logit-based SUE and a similar mathematical model are projected in order to achieve more accurate traffic conditions, such as junction delays. The accusing infrastructure is designed to be compatible with a wide range of EVs, and models are in place to ensure that the entire travel chain is practical. Using a hybrid approach based on the Dijkstra-based K-shortest routes algorithms, the suggested framework is solved repeatedly with steady convergence. The suggested method's capacity to type while retaining accessibility is demonstrated by its application to a realistic traffic network. In addition, it can accurately predict the overall charging loads and the prices of individual EV trips in scenarios with complicated synchronized traffic. In particular, even when there is a substantial EV penetration with larger consistency, with even more impressive forecast efficacy, hours.

The purpose of Li et al.'s [21] research is to provide an inclusive overview of the modelling of EV behaviour and its applications for developing algorithms for EV-grid integration. Several models have been developed to capture the complexities of adopting electric vehicles (EVs), charging decisions, and user acceptance of smart charging infrastructure. Specifically, this article explains the preexisting models, including temporal, geographical, and energy dimensions, and describes the numerous sub-models of charging preference and responsiveness to smart charging. The authors propose a novel EV behaviour modelling paradigm that may guide model selection for different application contexts by creating model portfolios for time, space, energy consumption, charging preference, and reactivity. As a result, we talk a lot about how to model EV behaviour to predict EV charging demands and schedule charging logically. Future research directions are discussed, and in-depth behavioural insights are offered that might be utilized to develop effective EV behaviour models that propel EV-grid integration.

Koohfar et al. [22] employ machine learning algorithms to predict EV charging demand, including RNNs, LSTMs, Bi-LSTMs, and modifiers. Data collected over five years from 25 public charging stations in the USA confirms the accuracy of this approach. Compared to other techniques, it performs better at predicting charging requirements, representing its value for time series forecasting glitches.

3. Methodology

The methodology for estimating charge patterns is described below. We define the issue, outline the dataset, emphasize data pretreatment, and go over training strategies for several learning models.

3.1 EV charging behaviour

Assuming ${\textstyle {t}_{con}}$ depicts the charging behaviour during a session, where ${\textstyle t_{join}}$ represents the period, the car initially plugs in, ${\textstyle t_{discon}}$ reflects the period the car unplugs, and ${\displaystyle e}$ indicates the total amount of energy supplied to the car. ${\textstyle {B}_{session}}$ as subsequent:

Based on the overhead, we can express the length of accusing meeting or the session period, ${\textstyle {S}_{dur}}$, as follows:

3.2 Dataset description

In order to forecast charging actions, we use data on events in addition to the charging information. We will provide a high-level overview of the datasets we employed, focusing on their salient features.

The unpredictability of charging behaviour makes scheduling EV charging more important in public charging facilities, such as those seen in areas like shopping malls. One of the few publicly accessible datasets for commercial EV charging, the ACN [23] dataset will be used in this study. The collection includes charging statistics from two campus stations (JPL and Caltech). The JPL station will not be evaluated because it is restricted to JPL workers exclusively, unlike the Caltech station. By scanning a QR code with their mobile apps, registered users can information, such as their projected leaving time and desired energy. There is a web gateway and a python API available at [24] for accessing the dataset.

Caltech does have a tiny weather station [25], but we didn't use it because of missing results and uneven interval records for the wind variable. Furthermore, this record weather conditions that might have an effect on charging behaviour, such as precipitation and snowfall. Since the actual position of the charging station is dependent on the weather, we used NASA's) [26]. Satellite weather data have been compared to those from ground stations for accuracy in [27]. While it has been established that ground stations are better able to identify some weather aspects at a given place, for the sake of this work, we are not interested in pinpoint precision but rather a more holistic understanding of how weather affects charging behaviours. For instance, we want to see how different weather conditions, such as heavy rain vs. dryness, affect charging behaviour.

It might be difficult to track down reliable historical traffic information for certain routes and regions. Both invasive techniques like road tubes and piezoelectric sensors, and noninvasive ones like microwave radar and video image recognition are used in traditional methods of traffic data collecting [28]. Scalability is a problem with these methods, and in most situations, only certain highways are covered. For instance, Pasadena (the source of the charge data) has an open data site [29] that details the city's daily traffic volume. Nonetheless, it covers traffic count over some period of time for most routes in the city, rendering it useless as we need data collected at regular intervals. Furthermore, not all streets and highways are protected. Therefore, we choose to utilize Google Maps traffic data, which has already been utilized in machine learning applications [30]. If commuters use the app and opt-in to sharing their location, the data is obtained by tracking the devices' precise whereabouts. To alleviate privacy concerns, gathered data is de-identified and aggregated before being made public [31]. The information may be retrieved using the Google Maps Distance Matrix API. The departure time and journey time between a specified set of coordinates for the origin and destination are calculated and returned. We gathered travel time data for the nine roads and streets closest to the charging station.

Considering the location on the Caltech campus, we thought it would be interesting to examine whether or not the frequency with which campus activities occur influences charging patterns. Caltech's online calendar [32] was consulted to determine how many events occur per hour. To keep things simple, we rounded times to the nearest hour, so that something that began at 10:20 a.m. would be recorded as having begun at 10 a.m.

3.3 Data preprocessing

To guarantee accurate predictions, it is essential to clean and preprocess the dataset. Among these is the elimination of anomalies and inaccurate data. Model performance may suffer if outliers are present. Boxplots are frequently used for this purpose. Figure 1 shows that there were outliers in the boxplots for both dependent variables. We see that the distribution of outlying values is not the same for both variables; in this case, there are much more extreme values for energy use than for session length. Some cars may have much higher energy consumption, even if the sitting time is not very long.

Figure 1. Session duration (right). Boxplots of energy consumption (left)

For this reason, we elected to build an ensemble of trees using the isolation forest approach to conduct multivariate outlier identification. iTree cases with low average path lengths are the ones that stand out [33]. The observations are 'separated' by picking a variable at random and then choosing a split value among the variable's lowest and maximum. The comments are partitioned recursively until they are all isolated. Shorter path length data for certain places after partitioning are likely to be anomalies. With the axes normalized for both reply variables, the method of identifying the outlier of variables is depicted in Figure 1. Six hundred and ninety-seven anomalies, or 4% of all observations, were found.

We only examined 97% of the records for the charge data since they were registered charging records, i.e. they had user IDs. We utilized the pytz [34] module in Python to transform the weather data, which was originally recorded in universal time, to the same time zone as the billing records. Additionally, we performed a temperature conversion from kelvin to Celsius. We then calculated the average weather during the preceding seven hours and the following ten hours for each hour, both of which were confirmed experimentally to be accurate representations. In doing so, we may learn how the state of the weather, both before and after charging, affects the choice to charge. For instance, reduced charging duration can be explained by the preceding hours' heavy snowfall. For the traffic information, we also had to adjust the time zone from Coordinated Universal Period. We then added up the sum of vehicles on the nine thoroughfares and streets throughout the course of each hour. It's important to remember that we included in both the median and maximum travel times predicted by Google Maps. At last, we added up all the campus activities for each hour.

The pandas [35] package was used to transform the time-series fields into date-time objects, which allowed for easier data merging. We then saved the closest time belonged in order to acquire weather, traffic, and events for a convinced charge record. For instance, a connection time of 22:11 indicates that it is currently 10 p.m. Because of this, we can simply parse out the remaining data. We didn't just pick the volume of traffic at a particular hour; we picked the volume of traffic from the time we got there till the time we left. For example, if a car checked in at 2:00 p.m., we included that car's arrival as part of the day's total traffic count. This would teach the model how changes in traffic volume affect pricing. Similarly, we accounted for everything that happened from the time we landed to the close of the day.

3.4 Classification using deep learning networks

With the advent of deep residual networks, deep learning's capacity for characterization and learning has been substantially enhanced, making this area of study increasingly popular among categorization researchers. When performing feature extraction using a standard deep convolutional neural network, the deep residual network may take use of the residual construction to maximize the info loss induced by the convolution procedure. This significantly enhances the reliability of rice disease detection. In 2017, a deep residual model called DenseNet was suggested at CVPR as a new type of network model. To promote feature reuse, the model employs tightly coupled connectivity, wherein all levels have access to the feature maps of their previous layers. This directly contributes to the model becoming more condensed and robust against overfitting. It also enables implicit deep supervision by having the loss function directly supervise each layer through a shortcut path..

3.4.1 Dense connection

DenseNet is an alternative connection design to traditional networks that maximizes information transfer during picture recognition by employing dense connectivity. This indicates that in DenseNet, all of the layers above it are linked together. The goal is to maximize the transfer of knowledge between networks by having each successive layer use the feature maps it has obtained layer as input to the next layer.

where ${\displaystyle {x}_{l}}$ is input to layer ${\displaystyle l,x_{0}}$ finished ${\displaystyle x_{(l-1)}}$ are the layer ${\displaystyle l}$ from which layer ${\displaystyle l}$ receives input, ${\displaystyle (x_{0},x_{1},\ldots ,x_{(l-1)})}$ are the chin maps output by the layers beforehand layer ${\displaystyle l}$ that are being combined, and ${\displaystyle H_{l}}$ is the compound purpose that gears the join features in the reckoning, including the batch layer. Figure 2 depicts the overall DenseNet structure.

Figure 2. DenseNet's data architecture is optimized for high throughput in visual recognition tasks

Table 1 displays the DenseNet-121 net topology.

3.4.2 Transition

In DenseNet, downsampling operations are handled by the transition module. For the model to be beaten to lower the sum of channels dense module, the changeover module primarily conducts two operations: pooling, with an layer of (2 2).

3.4.3 Attentional mechanisms

When processing an image, network to focus on the parts of the duplicate that will have the most impact on the final result. The convolutional neural network may dynamically modify its focus thanks to the attention mechanism. natural language processing have both benefited from the application of attention methods. Recurrent neural networks with have found widespread use in sequential models. There are two main categories of attention mechanisms: channel attention and spatial attention. Both CBAM models were employed in this work, where the structure is mentioned in Figure 3.

Figure 3. CBAM module architecture in feedforward convolutional neural networks allows for the incorporation of both spatial and channel attention processes

Assigning attention weights successively along follows the provision of an intermediate feature map of the procedure. The adaptive adjustment is then calculated by multiplying the original feature map by the weights. Figure 4 depicts the CBAM module's two sub-modules, the spatial.

(b)

Figure 4. CBAM module's structure, including the roles played by the spatial care module and the module, allows for the dynamic modification of ear maps

3.4.4 The channel attention mechanism unit

The channel care apparatus first receipts the map ${\textstyle F\in {R}^{C\times H\times W}}$ and permits it through global maximum regular pooling to get two ${\textstyle (C\times 1\times 1)}$ maps ${\textstyle {MS\in R}^{C\times 1\times 1}}$. A two-layer multilayer with ${\textstyle C/r}$ in the first layer ${\textstyle C}$ neurons in the second layer (where ${\textstyle C}$ is the original number of neurons) is then applied to the generated feature maps in a shared network. After then, the instrument ${\textstyle M_{C}(F)}$ adds up the individual features from the shared network's output to produce the final, full feature vector. Equation (4) depicts the formula for the channel attention apparatus

where ${\textstyle \sigma }$ function; ${\textstyle {W}_{0}\in {R}^{C/r\times C},\,{W}_{1}\in \,{R}^{C/r\times C}}$, and the ${\textstyle MLP}$ weights ${\textstyle W_{0}}$ and ${\textstyle {W}_{1}}$ inputs; and ${\textstyle {W}_{0}}$ is after the ReLU purpose.

3.4.5 Spatial attention mechanism module

The feature vectors generated by the channel care apparatus module are used as inputs to the spatial attention mechanism. Two feature vectors are generated from the feature vector by first using a maximum pooling process and then an process. ${\textstyle {F}_{max}^{S}\in {R}^{1\times H\times W}}$ and ${\textstyle {F}_{avg}^{S}\in {R}^{1\times H\times W}}$, respectively. Once the maximum and average pooled characteristics have been summed, a channel splicing operation is performed on them. After that, a convolutional convolution operation (${\textstyle 7\times 7}$) is performed on the feature vectors to collapse them down to a single dimension. The feature vector is derived after a sigmoid function is applied, ${\textstyle {M}_{S}(F)\in {R}^{H\times W}}$