Dataset Information

Promoting charging safety of electric bicycles via machine learning.

ABSTRACT: The worldwide penetration of electric bicycles has caused numerous charging accidents; however, online diagnosing charging faults remains challenging because of non-standard chargers, non-uniform communication manners and inaccessible battery inner status. The development of Internet of Things enables to acquire the input current information of chargers in the cloud platform, thereby supplying an alternative perspective to excavate underlying charge abnormalities. Through analyzing 181,282 charge records collected from the power-grid side, we establish an update-to-date deep neural network algorithm, which can automatically capture these charge feature variables, determine their dependencies and identify abnormal charge behaviors. Based on the only input current sequences, the algorithm can effectively diagnose the charging fault with the average accuracy of 85%, efficiently ensuring the charging safety of more than 20 million E-bicycles after substantial validations. Besides, this diagnosis framework can be extended to the real-time charge safety detection of electric vehicles and other similar energy storage systems.

SUBMITTER: Shuai C

PROVIDER: S-EPMC9804133 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Promoting charging safety of electric bicycles via machine learning.

Shuai Chunyan C Yang Fang F Wang Wencong W Shan Jun J Chen Zheng Z Ouyang Xin X

iScience 20221209 1

The worldwide penetration of electric bicycles has caused numerous charging accidents; however, online diagnosing charging faults remains challenging because of non-standard chargers, non-uniform communication manners and inaccessible battery inner status. The development of Internet of Things enables to acquire the input current information of chargers in the cloud platform, thereby supplying an alternative perspective to excavate underlying charge abnormalities. Through analyzing 181,282 charg ...[more]

PMID: 36594019

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Project description:ImportanceElectric scooter (e-scooter) use is increasing in France and in many urban environments worldwide. Yet little is known about injuries associated with use of e-scooters.ObjectiveTo describe characteristics and outcomes of major trauma involving e-scooters.Design, setting, and participantsA multicenter cohort study was conducted in France using the national major trauma registry between January 1, 2019, and December 20, 2022. All patients admitted to a participating major trauma center following a road traffic crash (RTC) involving an e-scooter, a bicycle, or a motorbike were included.ExposureIncluded patients were compared according to the 3 mechanisms.Main outcomes and measuresThe primary outcome was trauma severity as defined by the Injury Severity Score (ISS). Secondary outcomes included the trends of the number of patients per year, a comparison of the RTC epidemiologic factors, injury severity, resources used, and in-hospital outcomes.ResultsA total of 5233 patients involved in RTCs were admitted (median age, 33 [IQR, 24-48] years; 4629 [88.5%] men; median ISS, 13 [IQR, 8-22]). The population included 229 e-scooter RTCs (4.4%), 4094 motorbike RTCs (78.2%), and 910 bicycle RTCs (17.4%). The number of patients treated following e-scooter RTCs increased by 2.8-fold in 4 years (from 31 in 2019 to 88 in 2022), while bicycle RTCs increased by 1.2-fold and motorbike RTCs decreased by 0.9-fold. At admission, 36.7% of e-scooter users had a blood alcohol content higher than the legal threshold (n = 84) and 22.5% wore a protective helmet (n = 32). Among e-scooter RTCs, 102 patients (45.5%) had an ISS of 16 or higher. This proportion was similar for patients with motorbike RTCs (1557 [39.7%]; P = .10) and bicycle RTCs (411 [47.3%]; P = .69). With a proportion of 25.9% (n = 50), patients with e-scooter RTCs had twice as many severe traumatic brain injuries (Glasgow Coma Scale ≤8) as motorbike RTCs (445 [11.8%]) and a proportion comparable to bicycle RTCs (174 [22.1%]). The mortality of e-scooter RTCs was 9.2% (n = 20), compared with 5.2% (n = 196) (P = .02) for motorbikes and 10.0% (n = 84) (P = .82) for bicycles.Conclusions and relevanceThe findings of this study suggest that trauma involving e-scooters in France has significantly increased over the past 4 years. These patients presented with injury profiles as severe as those of individuals who experienced bicycle or motorbike RTCs, with a higher proportion of severe traumatic brain injury.

Project description:Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Dataset Information

Promoting charging safety of electric bicycles via machine learning.

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Promoting charging safety of electric bicycles via machine learning.

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