Modeling of Electric Vehicle Charging Energy Consumption using Machine Learning

Renata, Dionysius Aldion; Fauziah, Khotimatul; Aji, Prasetyo; Larasati, Adisa; Halidah, Hafsah; Tasurun, Dwidharma Priyasta; Astriani, Yuli; Riza,

doi:10.1109/icacsis53237.2021.9631324

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Concerning the validation methods, 25 of the selected studies use them to assess the prediction accuracy of ECEV models. As can be seen in Figure 10, the cross-validation method is the most frequently used in 15 studies [36,[47][48][49]64,71,102,106,109,111,115,120,121,160,164], followed by 10-fold cross-validation in 10 studies [41,43,51,52,83,103,141,147,165,170], 5-fold cross-validation in 6 studies [91,94,108,152,158,167], then 3-fold cross-validation in 3 studies [92,93,152]. Less studied cross-validations methods are not shown in Figure 10, such as 1-2-4-fold cross validation [152], 6-fold cross validation [137], 8-fold cross validation [146], 9-fold cross-validation [122], and leave-one-out cross-validation [83] which are in only 1 study each.…”

Section: Evaluation Measures and Validation Methodsmentioning

confidence: 99%

“…Adaptive Neuro Fuzzy Inference System (ANFIS) [19,118,162] Multilayer feed forward NN (MFFNN) [99,118,121] Back Propagation NN (BPNN) [95,96,100,104,107,115,117,128,129,134,142,159] Multilayer Perceptron (MLP) [54,71,84,108,112,127,138,141,145,181] NARX NN [97] Stacked Autoencoder (SAEs) [116] ST-ResNE [151] Feed Forward NN [86] Extreme Learning Machine (ELM) [155] Recurrent 1 Kernel LMS (KLMS) [106] Fixed budget quantized KLMS (QKLMS-FB) [106] Recursive Least Squares (RLS) RLS [106] 1 Kernel RLS (KRLS) [106] RLS Tracker KRLS-T [106] Fuzzy Logic (FL) FL [32,113,123,142] 6 Fuzzy Inference System (Mamdami) [131] Interval Type-2 Fuzzy System (IT2FS) [153] Differential Evolution And Grey Wolf Optimizer (DEGWO) [110] 1…”

Section: # Of Studies Totalmentioning

confidence: 99%

“…Thus, optimizing them is required. Of the selected studies, only 22 studies use optimization methods, 10 studies use Grid search (GS) [43,51,52,71,83,106,141,158,164,165], 5 studies the use genetic algorithm [96,100,115,140,169], 4 studies [36,160,165,173] use particle swarm optimization (PSO), and 2 studies use random search [132,146]. Other optimization methods are less used such as Bayesian optimization [132], Fine-tuning [138], least mean squared (LMS) optimization [162], lightning search algorithm, backtracking search optimization, and gravitational search algorithm [160], each one being used in only one study.…”

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confidence: 99%

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Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

2023

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Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.

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Section: Evaluation Measures and Validation Methodsmentioning

confidence: 99%

Section: # Of Studies Totalmentioning

confidence: 99%

mentioning

confidence: 99%

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Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

2023

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“…Another SVR approach is presented in [48], where the day-ahead electricity consumption of a charging station in Indonesia is modeled and forecasted incorporating both historical charging transactions and weather data. The findings suggest that SVR surpasses other machine learning algorithms in accuracy.…”

Section: ) Support Vector Regression (Svr)mentioning

confidence: 99%

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Marzbani,

Osman,

Hassan

2023

IEEE Access

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Accurate forecast of electric vehicle energy demand is vital for maintaining the stability and reliability of power systems. With the increasing prevalence of electric vehicles in transportation systems, the anticipating demand surges with precision in terms of timing and location is becoming ever more critical for utilities to guarantee sufficient supply. The intermittent and stochastic nature of electric vehicle electricity consumption is a significant challenge in accurately forecasting of electric vehicles demand. As a result, there is a growing field of research focused on developing models that can effectively capture and interpret such complex data. In improving the potential of accurate prediction models, conducting a comprehensive review of literature, examining current research overviews, and exploring potential expansions and extensions of models are all critical components. In this review, a comprehensive overview of prior research conducted for forecasting electric vehicle energy demand is presented, including a detailed examination of the benefits and drawbacks of the methods used. Additionally, potential gaps in the field are identified, and recommendations for future research directions are provided.

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“…The central system usually has many features. Aji et al in the study [24] reported that they developed a central system based on the OCPP 1.6 called SONIK, as shown in Figure 3, and included some features in order to monitor power in real time, to display the availability and location of the charge point, to show the energy consumption, and so forth, which then Renata et al in the study [25] reported that they used the energy consumption data from SONIK to develop an energy consumption predictive model using Machine Learning to forecast the charge point's energy consumption for the next day based on the data of the previous days. Orcioni and Conti in the study [26] proposed an extension of the OCPP to enable EV users to select the best solution according to their preference in the advanced reservation for using a charge point at the next few hours, where the EV users are negotiating the charging parameters (i.e.…”

Section: Introductionmentioning

confidence: 99%

Ensuring Compliance and Reliability in EV Charging Station Management Systems: A Novel Testing Tool for OCPP 1.6 Messages Conformance

Priyasta¹,

Hadiyanto²,

Septiawan³

et al. 2023

JESA

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The Open Charge Point Protocol (OCPP) has been considered the de-facto standard for communication between charge points and the central management system of the charge points. This paper presents a novel messages testing tool based on OCPP 1.6 to evaluate the conformance of the central system to the field category definitions specified by OCPP. The authors propose the test method, discuss the test case scenarios (positive and negative) used by the messages testing tool, and describe how to set up a test case to examine the system under test. Additionally, this study highlights the difference between the messages testing tool and the OCPP 1.6 Compliance Testing Tool (OCTT) provided by the Open Charge Alliance (OCA). To test its reliability, the message testing tool has been applied to an open-source central system platform. The result shows that the system under test is able to pass 100% of the positive test case scenarios, but only 33% of the negative test case scenarios. It is worth noting that similar results may occur in other central system platforms as well. The study's findings underscore the significance of using comprehensive messages testing tools during the development and release of central system platforms. This ensures conformance to the OCPP field category definitions and promotes reliable interoperability between charge points and central management systems.

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Modeling of Electric Vehicle Charging Energy Consumption using Machine Learning

Cited by 5 publications

References 7 publications

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Ensuring Compliance and Reliability in EV Charging Station Management Systems: A Novel Testing Tool for OCPP 1.6 Messages Conformance

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