Driving pattern identification for EV range estimation

Yu, Hai; Tseng, Finn; McGee, Ryan

doi:10.1109/ievc.2012.6183207

Cited by 57 publications

(27 citation statements)

References 6 publications

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“…The objective of FEV range estimation appears in several recent works such as [7], [11], [12], [19], [20], with the majority of them being evaluated on simulated driving data. Quantification of speed and vehicle auxiliary loads impacts on battery degradation are typically considered, while several works have also taken into account exogenous (contextual) factors such as the elevation [7] or the future traffic and weather conditions as in [12] and [21].…”

Section: Introductionmentioning

confidence: 99%

Online prediction of an electric vehicle remaining range based on regression analysis

Bolovinou

Bakas

Amditis

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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Given the longitudinal average velocity and energy consumption of a Full Electric Vehicle (FEV) for any given part of a targeted road trip, this work solves the problem of online remaining range estimation, i.e., predicting, at any given travelled distance from the beginning of the trip, the actual distance the vehicle can still be driven before recharging is required. Modelling the remaining range is closely related with modelling the energy consumption of an electric vehicle. The latter remains an open problem due to unknown context data that may apply such as driving trip speed, vehicle load and topographical characteristics. In this work, a regression model is formulated in order to learn, from time/location-variant real driving data, a relationship between the future energy consumption on one side, and the following related factors, which are considered known, on the other side: i) the difference in average velocity between the future and the past ii) the difference in elevation rate between the future and the past and iii) the recent past energy consumption. Experimental results on around 2000km of discharge trips, demonstrate the effectiveness of the method over a conventional method that is based solely on historic energy usage evidence. An average Mean Absolute Error (MAE) of 1.64 km and of 1.95 km is obtained when the regression model is evaluated on a model trained without and with elevation respectively.

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Section: Introductionmentioning

confidence: 99%

Online prediction of an electric vehicle remaining range based on regression analysis

Bolovinou

Bakas

Amditis

et al. 2014

2014 IEEE International Electric Vehicle Conference (IEVC)

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“…Reference [38] used the real-world driving data that comprise 4409 trips in Southeast Michigan of the United States to build a model of the daily driving mission for studies of real-world PHEV usage. It is developed a driving pattern recognition method for EV range estimation in [39]. The travel data from the Transportation Tomorrow Survey in Toronto was used to study the impacts of driving patterns on tank-to-wheel energy use of PHEVs in [40].…”

Section: Introductionmentioning

confidence: 99%

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Liu

Christensen

et al. 2015

J. Mod. Power Syst. Clean Energy

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Electic vehicles (EVs) show great potential to cope with the intermittency of renewable energy sources (RES) and provide demand side flexibility required by the smart grid. Furthermore, EVs will increase the electricity consumption. Large scale integration of EVs will probably have substantial impacts on power systems. This paper presents a methodology to transform driving behavior of person into one of the cars in order to analyze the driving pattern of EVs based on the National Travel Surveys. In the proposed methodology, a statistical process is used to obtain the driving behavior of cars by grouping the survey respondents according to the driving license number and car number, and mapping the households with similar characteristics. The proposed methodology was used to carry out the driving pattern analysis in the Nordic region. The detailed driving requirements and charging/discharging availability of vehicles along the day were obtained.Two types of EV availabilities were studied in this paper considering different charging/discharging conditions of EVs for the power system integration, i.e. EV availability all day and EV availability at home. The results show that the daily driving requirements of the Nordic region are not very intensive. The driving patterns of vehicles in the Nordic region vary on weekdays and weekends. The two types of EV availabilities are quite different from each other.

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“…Most of the previous works on the model-based estimation rather focus on the predicting the future velocity profile of the EV [12], [13]. Such works forecast future driving profiles from the route information, speed limitation and driver behavior.…”

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

Accurate remaining range estimation for Electric vehicles

Hong

Park

Chang

2016

2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC)

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EVs (Electric vehicle) generally have only around 22% driving ranges compared with ICEVs (Internal combustion engine vehicle) with a similar price range. Running out of the EV battery SoC (State of charge) while driving gives the same inconvenience as a vehicle breakdown. In this paper, we emphasize that an accurate remaining range estimation can efficiently mitigate the range anxiety of EV drivers. Most EV drivers reserve 30% of the on-dash estimated remaining range gauge of their EV because they do not trust the current remaining range estimation accuracy of production EVs. In other words, an accurate remaining range estimation is equivalent to increasing the EV battery capacity up to 30%.Just like the analogous concepts used in the power estimation of digital circuits, a model-based remaining range estimation consists of the two consecutive steps, a driving profile estimation and a power consumption estimation using the power model. In this paper, we focus on increasing the accuracy of the power model. We come up with a hybrid modeling methodology combining a physics equation based model with empirical data. We validate the accuracy of the hybrid model in the remaining range estimation with the target EV. We collect the power consumption, velocity, road inclination, etc. of the EV in every half second with an onboard monitoring system, a perform multivariable linear regression and create an accurate EV power model. The proposed remaining range estimation yields only 2.52% error while the state-of-the-art model-based EV remaining range estimation shows 9.33% error when the same future route and speed estimation are given.

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Driving pattern identification for EV range estimation

Cited by 57 publications

References 6 publications

Online prediction of an electric vehicle remaining range based on regression analysis

Online prediction of an electric vehicle remaining range based on regression analysis

Driving pattern analysis of Nordic region based on National Travel Surveys for electric vehicle integration

Accurate remaining range estimation for Electric vehicles

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