INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
Developing an Efficient Support Vector Machine Learning Model  
for Mobile Charging Stations.  
*
Ezuruka Evelyn Ogochukwu , Prof S. O. Anigbogu  
Department of Computer Science, Nnamdi Azikiwe University Awka.  
*
Corresponding Author  
DOI: https://doi.org/10.51244/IJRSI.2024.1108116  
Received: 06 August 2024; Accepted: 13 August 2024; Published: 19 September 2024  
ABSTRACT  
In the context of increasing global energy scarcity, optimizing electric vehicle (EV) mobile charging stations  
is critical for promoting sustainable transportation. This study introduces the use Artificial Neural Network  
(ANN) models, and Support Vector Machine (SVM) enhanced with the Adams optimizer, to address the  
challenge of efficient EV charging in energy-constrained environments. The models are designed to predict  
optimal charging station locations and schedules, with the ANN-Adams optimization fine-tuning the model  
parameters to improve accuracy and performance, while ensuring dynamic adaptation to fluctuating energy  
availability and demand patterns. This research contributes to the development of intelligent, adaptive  
systems for EV infrastructure, paving the way for more resilient and energy-efficient urban mobility  
solutions. The system employs supervised learning, where models were trained and tested on labeled  
datasets. The performance of the SVM models was compared to that of a Multilayer Perceptron Network  
(MLPN) with Adams optimization. The results showed that the MLPN with Adams optimization achieved  
an accuracy of 97.85%, while the SVM model had a prediction accuracy of 81%. The ANN recorded  
91.29% accuracy. Both approaches significantly improved classification accuracy, model generalization on  
testing datasets, and reduced misclassification errors.  
INTRODUCTION  
Electric vehicle (EV) mobile charging stations are an innovative solution designed to address the growing  
demand for EV charging infrastructure. Unlike fixed charging stations, mobile charging units can move to  
different locations, providing flexibility and convenience for EV users. These mobile units are particularly  
useful in urban areas, events, emergencies, and locations where installing permanent charging infrastructure  
is not feasible. The global transition towards sustainable transportation has accelerated the adoption of EVs  
as a viable alternative to conventional internal combustion engine vehicles. This shift is driven by the need  
to reduce greenhouse gas emissions, lower dependency on fossil fuels, and mitigate the adverse effects of  
climate change. However, the widespread deployment of EVs presents several challenges, particularly in the  
realm of charging infrastructure. As EVs continue to gain popularity, the optimization of charging schedules  
becomes increasingly important to balance demand with grid capacity (Usman et al., 2016). Efficient and  
reliable charging solutions are critical to supporting the growing number of EVs on the road and alleviating  
concerns related to range anxiety among users. In this context, mobile charging stations have emerged as a  
flexible and adaptive solution to address the dynamic and distributed nature of EV charging demands. This  
research focuses on utilizing advanced machine learning techniques to optimize the operation and  
deployment of mobile EV charging stations in energy-constrained environments. The study primarily  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
emphasizes the use of a Multilayer Perceptron Network (MLPN) enhanced with the Adams optimizer and an  
Artificial Neural Network (ANN), as these models achieved the highest accuracy in predicting optimal  
charging station locations and schedules. The MLPN with Adams optimization demonstrated superior  
performance, achieving an accuracy of 97.85%, followed closely by the ANN without an optimizer, which  
recorded an accuracy of 91.29%.These models were designed to dynamically adapt to fluctuating energy  
availability and charging demands, making them particularly well-suited for energy-scarce environments.  
The Adams optimizer, in particular, played a crucial role in fine-tuning the MLPN model parameters,  
leading to significant improvements in accuracy and performance. In conclusion, this research addresses the  
critical challenge of optimizing EV mobile charging stations in energy-scarce environments by leveraging  
advanced machine learning techniques and optimization methods. The proposed framework offers a scalable  
and resilient solution for sustainable urban mobility, with significant implications for policymakers, urban  
planners, and stakeholders in the transportation sector. As the world continues to embrace electric mobility,  
the innovative solutions presented in this study will play a pivotal role in shaping the future of transportation  
and energy management.  
LITERATURE REVIEW  
In the realm of charging EVs, various survey papers on the optimization of charging strategies have been  
published. For instance, the study in (Mukherjee & Gupta, 2015) examined scheduling algorithms for  
charging EVs in smart grids. A power and communication system has been designed for bidirectional flows  
of electricity and information. The authors categorized their work based on unidirectional and bidirectional  
charging, centralized and decentralized scheduling, and the consideration of mobility aspects. (Junming et  
al., 2019) introduced a Genetic Algorithm-based Emergent Charging Scheduling (GECS) scheme to address  
routing and scheduling optimization problems for EVs, when there is a sudden demand for rapid charging in  
a high-density area. Gruoss et al. (2020) developed a Markov chain framework (MCF) for describing the  
level of occupancy of charging infrastructure. MCF was constructed relying on information collected from  
about 40 public charging spots for an array of car-sharing automobiles. The suggested model’s accuracy in  
prediction could not be computed. The model performed flawlessly when determining the occupancy level  
of EV charging. Charging modes for EVs are of four main modes based on the Deltrix EVs Chargers  
classification (D. Chargers. EV Charging Modes, 2024 Online). The selection of an appropriate charging  
mode depends on various factors, such as EV battery capacity, current charging status, required driving  
range, user preferences, and the availability of charging infrastructure.  
1. Charging mode 1: This mode is the slowest form of charging for an EV. It involved using a standard  
home plug to connect the EV to the power grid. Charging an EV battery in mode 1 can take several  
hours or even require an overnight. However, one advantage of this gradual charging process is that it  
generates less heat and imposes less stress on the EV battery (Ahmad et al., 2022). 2)  
2. Charging mode 2: This mode also uses a home plug for charging EVs. It incorporated a specialized  
cable equipped with built-in shock protection against risks from both Alternating Current (AC) and  
Direct Current (DC), enhancing the safety of the charging process.  
3. Charging mode 3: It is the most popular charging method among EV users. It can be implemented  
both at home and at public CSs. Like mode 2, it provides shock prevention against both AC and DC  
currents. In mode 3, the EV user does not need to use a specific cable for charging; instead, the  
necessary connecting cables are provided at the stations.  
4. Charging mode 4: Often referred to as fast charging mode, it involves the use of CSs that convert AC  
power to DC, allowing direct charging for EVs. Typically, fast charging mode is notable for its  
efficiency; an average EV battery takes about 30 minutes to an hour to be fully charged. The charging  
rates supported in this mode vary, ranging from 5 kW units up to 50 kW and 150 kW. Future  
standards may even extend this range to 350 kW and 400 kW. However, these higher charging rates  
can generate significant heat, which may impact the battery’s lifespan. Therefore, a special cooling  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
system is often required to manage this heat effectively.  
The following elements are presented and examined to put the suggested architecture into execution.  
(a). Datasets: The dataset was sourced from publicly made available kaggle  
site  
at  
“https://data.cityofpaloalto.org/dataviews/257812/ELECT-VEHIC-CHARG-STATI-83602/”. A few of its  
features are station name, MAC address, start date, start time, end date, total duration, charging time, power  
split transmission (PST), pulse discharge test (PDT) and etc, With over 2000 testing and 8000 training sets  
making up the 10,000 dataset, the suggested collection provides a sizable dataset for building models.  
Table 1: Dataset for EV (source: https://data.cityofpaloalto.org/dataviews/257812/ELECT-VEHIC-CHARG-  
STATI-83602/”)  
1
Station Name  
MAC Address  
Org Name  
Post User_id  
2
PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 3284  
PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 4169  
PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 4169  
PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 2545  
PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 3765  
3
4
5
6
—
——-  
——-  
——-  
——  
9996 PALO ALTO CA / HAMILTON #1 000D:6F00:015A:9D76 City of Palo Alto 55033  
9997 PALO ALTO CA / HAMILTON #2 000D6F0000A20F47 City of Palo Alto 126779  
9998 PALO ALTO CA / HIGH #4 000D:6F00:015A:9D76 City of Palo Alto 139203  
9999 PALO ALTO CA / HAMILTON #1 000D6F0000A2108E  
PALO ALTO CA / BRYANT #2 000D6F0000A2108E  
City of Palo Alto 2670  
City of Palo Alto 134699  
(b). Data pre-processing: The preprocessing is encapsulated in set of routines capable of filtering instances  
or attributes. The data preparation phase is utilized in order to identify and deal with erroneous values and  
eliminate missing data values from the current system dataset. The date and the vehicle charging parameters  
are preprocessed into an appropriate format prior to training. The missing value replace function was used  
during the preprocessing stage to fill the values for the training dataset before creating the model.  
(c). Feature engineering: The feature engineering technique is employed to transform data to have  
meaningful representation using human knowledge. This is very important and intensive which acts as a  
weakness to learning models. This phase relies mainly on human ingenuity and prior knowledge to  
compensate for the inability of algorithms to extract and organize the discriminative information from  
dataset (Bengio et al. 2020).  
(d). Model training: The libraries given below are used in Python to train the suggested model  
(i). Scikit-learn: is a popular and freely available Python framework for machine learning predictive data  
analysis (Ferreira, 2018). The multi-layer perceptron neural network and the support vector machine. we  
imported the Sci-learn package in order to train two distinct machine learning models.  
(ii). TensorFlow is an open-source deep learning system Known as the “big daddy” of deep learning  
frameworks. The dataset module is being used to build a unique dataset that will be fed into the training  
model.  
(e). Metrics of Evaluation: Comparing the various outcomes requires the use of a consistent model  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
diagnostic tool. Model anticipated results for scenarios involving multi-classification tasks, like the one  
proposed here, can be visualized in a variety of ways. Standard model evaluation metrics like accuracy,  
confusion matrices, and ROC learning curves are used to evaluate the efficacy of SVM algorithms.  
Classification accuracy is defined as the proportion of the dataset’s data points that were correctly classified.  
The error rate can be measured with the general equation given by:  
Classification accuracy =  
=
Where TN represents true negative, FP is false positive, TP is true positive and FN is false negative cases.  
Method 1: Adaptive Moment (Adam) Optimization  
Adam is a first-order gradient-based optimization technique designed to succeed SGD in training deep  
learning models. It is more memory-efficient, computationally effective, eliminates gradient challenges,  
suitable for noisy and large dataset. Adam employs a technique that maintains a single learning rate for all  
weight updates that remain fixed throughout training while keeping adaptive learning rates distinct from  
each parameter. Adam is an optimization approach that is capable of handling sparse gradients on noisy  
situations by combining the best features of the AdaGrad and Root Mean Squared Propagation (RMSProp)  
techniques. The goal is to improve the performance of a given loss function by optimizing the model  
weights. The effectiveness of the MLPNN model can be determined using the loss function as a metric. It is  
imperative to employ optimizers such as Adams and SGD to systematically modify the network weights and  
improve model performance during the training process. The cross entropy loss quantifies the model’s  
performance when the output of a classification model is a probability value that falls between 0 and 1. We  
employed Adam-grad optimizer in ANN model to adjust layer weights along with learning rates following  
each iteration phase in order to reduce losses.  
Adam estimates the first and second moments of network gradients to balance the model learning rates for  
each network weight given as:  
n
m = E [X ]  
1.0  
n
Where “m” represent moment; “X” the random variable, “n” moment of random variable assigned and the  
expected value or power.  
Adagrad adjusts the learning rate based on conditions because the actual rates are derived from parameters  
and the learning rates are also adjustable. The learning rate of parameters having low gradients will increase  
and those with large gradients will decrease.  
The Adam’s update equation which cannot be manually updated is represented as:  
∇ϴ = ∇ϴ J(ϴt)  
1.1  
1.2  
t
t
η
−
gt  
ϴt+1  
= ϴt  
√υ + 훜  
t
Where  
υ is the exponential moving average of the gradient  
t
ϵ smoothing term that avoids dividing by zero.  
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ϴ
the dynamic gradient of the past term  
t+1  
η is the learning rate  
( )  
∇ϴ J ϴt is the gradient of the loss function  
t
gt is the first derivative for loss function  
Figure 1: Train/validation loss of MLPN-Adams Optimization  
The MLPN accuracy for training and validation loss is based on the ANN model weights’ as randomly setup  
is shown in Figure 1. It provides us with further insight into how the MLPN model performs over the  
course of the training cycle (epoch). The validation loss dropped in the same order as the training over 50  
epochs. There is a correlation between the training and validation sets from the beginning to the end.  
Figure 2: Train/validation plot of MLPN-Adams Optimization  
The MLPN training and validation loss is shown in Figure 2. It illustrates how the predictive algorithm fails  
to draw valid conclusions from the testing data. The trained model performs well on training samples, but  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
when tested on the validation data set, it performs poorly at the beginning, as the graph illustrates, validation  
loss increases once again at point 10 before declining again from 50. Model fitting happens because the  
artificial neural network (ANN) algorithm in this case was trained for an extended period of time, making it  
purely too sophisticated for the data. When the loss is gradual and mild, training can be decreased with the  
intention of terminating early. Over the training loss, the validation curve is bouncing up and down.  
Figure 3: DT Confusion matrix (TP=0, TN=2427, FP=0 and FN=575)  
Figure 3, shows the confusion matrix, which displays a table structure of the various SVM prediction results  
and outcomes of a binary-classification task to aid in visualizing its results. This is used to show the  
predicted and actual values of a classification model. Cell values above and below the main diagonal or off-  
diagonal elements showing the incorrectly predicted values, show the total number of correctly predicted  
values that are equal to the actual or true values. The greater the diagonal value, the more accurate the  
predicted EV Battery charging duration. According to the confusion matrix, EV Batteries with low charging  
time had 573 incorrectly predicted cases with zero(no) correct predictions. While EV Batteries with fast  
charging duration provided 2427 incorrectly predicted values with zero (0) true positive class prediction.  
(b). For MLPN-Adams Optimization Technique  
Figure 4: MLPN-Adams Confusion matrix (TP=354, TN=1510, FP=136 and FN=0S)  
A 4 × 4 confusion matrix, with 4 representing the total number of target classes, is used to determine the  
performance of a binary classification report, as shown in Figure 2. This is done to compare the predicted  
values of the ANN model with the actual target values, which are divided into four mutually incompatible  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XI Issue VIII August 2024  
possibilities. Figure 2 displays the true positive and negative cases of the MLPN EV duration of battery  
charging rate and classification. According to the data, 136 are the correct predictions and 1864 classes  
were wrongly classified cases of slow and fast battery charging duration. The confusion matric help to  
explain how well a classification system performs on a set of experimental data for which the true values are  
known is the artificial neural network confusion matrix.  
RESULTS AND DISCUSSION  
Table 1: SVM Classification Report  
The SVM classification report for shorter and longer battering charge times is shown in Table 1. It includes  
the precision, recall, and f1-score accuracy of the exiting. For slower (longer) battery charging times, the  
precision accuracy recall and f1-score produced results of 0.0 for each. The quick (shorter) battery charging  
duration yielded a precision accuracy of 0.81. A shorter battery charging time produced a f1-score of 0.89  
and a recall score of 1.00 for shorter charging times.  
Table 2: Classification Report for MLPN-Adams Optimizer  
The classification report of the MLPN-Adams Optimizer for longer and shorter battery charging durations is  
displayed in Table 2, along with precession, recall, and f1-score correctness. For the slow or extended  
battery charging parameter, the precision accuracy score was 1.00, and for the shorter or faster charging  
period, it was 0.97. Recall scores for slower charging batteries were recorded at 0.84 and 1.00 for quicker  
charging times, respectively. The f1-score for slow charging was 0.92 and for fast charging, 0.98.  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
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Table 3: SVM and MLPN-Adam’s Optimizer  
MLPN-Adam’s  
Optimizer  
Metrics  
SVM  
0
True Positive(TP)  
233  
False Negative(FN) 573 121  
False Positive(FP) 89  
0
True Negative(TN) 2427 1557  
Correct predictions 2427 1790  
Incorrect predictions 573 210  
Accuracy  
81% 97.85%  
Performance accuracy of the models, including SVM and the suggested Adams optimization techniques,  
revealed the following results: The Multilayer Perceptron Network with Adams Optimization (MLPN-  
Adams) achieved the highest accuracy of 97.85% for predicting target EV charging class duration. The  
Multilayer Perceptron Network with ANN optimization achieved the second-best result, also with an  
accuracy of 97.85%. Both of these models improved accuracy and reduced the challenges associated with  
model training. In contrast, the Support Vector Machine (SVM) model had the lowest prediction accuracy at  
81%.  
CONCLUSION  
This study effectively developed and evaluated Artificial Neural Network (ANN) and Support Vector  
Machine (SVM) models optimized with the Adams optimizer for mobile charging stations. The research  
aimed to address the challenges associated with efficient EV charging in energy-constrained environments  
by leveraging advanced machine learning techniques. The integration of the Adams optimizer with ANN  
and SVM models has proven to be a valuable approach for developing intelligent and adaptive systems.  
These models offer innovative solutions for predicting optimal charging station locations and schedules,  
enhancing the efficiency of mobile charging infrastructure. The findings of this research contribute to the  
advancement of sustainable transportation by providing a robust framework for improving energy utilization  
and service quality in dynamic urban environments. The use of these advanced models and optimization  
techniques paves the way for more resilient and effective EV charging solutions.  
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