BEV Remaining Range Estimation Based on Modern Control Theory - Initial Study

Dedek, Jan; Docekal, Tomas; Ožana, Štěpán; Sikora, Tadeusz

doi:10.1016/j.ifacol.2019.12.738

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Range prediction is a standard feature when it comes to four-wheeled electric vehicles (EV) to inform the drivers when to charge the vehicle. To overcome the range prediction problem in the case of EV, physical models using real-time data and artificial intelligence models both trained on previous vehicle data have been investigated to estimate the range of EV where physical models are based on their ability to provide an estimate of energy consumption by modeling either the battery pack or the vehicle itself (see, e.g., [13][14][15][16][17][18][19][20][21]). However, when it comes to the two-wheeler e-mobility especially, there are very few works that have been conducted [8,[10][11][12][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques

Amin

Cho

2023

Applied Sciences

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Electric two-wheelers are becoming increasingly popular across the world, particularly in cities where their small size and flexibility make them a viable option for navigating congested streets. One of the most challenging aspects of e-mobility on two-wheelers is precisely calculating their range. This might be an issue for riders who must go long distances or who have limited access to charging stations. Various factors can influence an electric two-wheeler range, making it challenging to predict how far it can travel on a single charge. To tackle this problem, most of the manufacturers offer range predictions based on both test data and real-world usage scenarios. However, these estimates are customized for specific vehicle models and testing parameters that may not apply in all circumstances. Additionally, it can be challenging to obtain comprehensive technical specifications for two-wheelers available in the market, as most manufacturers do not provide detailed technical information. Hence, it is crucial to address the challenge of range prediction for two-wheelers in general, which can be advantageous for riders. In this paper, we discuss the precise prediction of the remaining range of electric two-wheelers even without knowing detailed e-mobility technical information. An application is also developed only for this research purpose, which can provide navigation services. Our approach concentrates on user behavior, weather, road conditions, and the vehicle’s performance history, which is gathered through the application. The collected data are used to train the selected ML model on the cloud. We applied various machine learning algorithms before deploying in the cloud where the SVM algorithm demonstrated outstanding performance, with a mean absolute error of 150 m for an average distance of 7.46 km. Furthermore, the model’s performance was evaluated after deployment and tested having 130 m error on average.

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Section: Related Workmentioning

confidence: 99%

An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques

Amin

Cho

2023

Applied Sciences

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“…There is a fairly large body of studies that aim at devising accurate algorithms for predicting the driving range of purely electric vehicles, often termed BEV (Battery Electric Vehicles). However, the majority of these works address the range estimation problem for electric cars (see, e.g., [3,[8][9][10][11]14]), while very few of them explicitly deal with e-bikes [7,12]. It can be argued that, besides other obvious motivations, a reason for the minor interest in the range of e-bikes is that these are actually hybrid vehicles, which can move (with the muscle force impressed on the pedals) even in case of completely discharged battery.…”

Section: Related Workmentioning

confidence: 99%

“…This limitation is because such a computation is easy and can be implemented even in simple control units. However, this choice neglects crucial aspects of the planned route (see [3] for an in-depth critique of this aspect), in particular the difference in altitude, the combined weight of bike and biker, and road conditions. For instance, the same remaining battery capacity can lead to different distances in a flat street or in a hill road.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm to Predict E-Bike Power Consumption Based on Planned Routes

2022

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E-bikes, i.e., bikes equipped with a small electrical engine, are becoming increasingly widespread, thanks to their positive contribution to mobility and sustainability. A key component of an e-bike is the battery that feeds the drive unit: clearly, the higher the capacity of the battery, the longer the distances that the biker will cover under engine support. On the negative side, the additional weight incurred by the electric components is likely to ruin the riding experience in case the battery runs out of power. For this reason, an integrated hardware-software system that provides accurate information about the remaining range is essential, especially for older or “not-in-shape” bikers. Many e-bikes systems are already equipped with a small control unit that displays useful information, such as speed, instantaneous power consumption, and estimated range as well. Existing approaches rely on machine learning techniques applied to collected data, or even on the remaining battery capacity and the assistance level required by the drive unit. They do not consider crucial aspects of the planned route, in particular the difference in altitude, the combined weight of bike and biker, and road conditions. In this paper, we propose a mathematical model implemented in an application to compute battery consumption, and hence the presumed remaining range, in a more accurate way. Our application relies on external sources to compute the route and the elevation data of a number of intermediate points. We present the mathematical model on which our application is based, we show the implemented application in shape of an app, and we report the results of the experiments.

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