On-line battery identification for electric driving range prediction

Kessels, J.T.B.A.; Roşca, Bogdan; Bergveld, Henk Jan; Bosch, P.P.J. van den

doi:10.1109/vppc.2011.6043022

Cited by 32 publications

(13 citation statements)

References 17 publications

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“…Although there are various methods for capacity estimation, most of them can be grouped into one of the two broad categories: openloop [3][4][5][6] and closed-loop [7][8][9][10][11][12][13][14][15] methods. Systems in which the output has no effect upon the input are used for open-loop methods.…”

mentioning

confidence: 99%

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Sung

Hwang

Nam

et al. 2016

J. Electrochem. Soc.

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For safe and reliable use of the battery for electric vehicles, diagnosis of its state-of-health (SOH) is essential. This is achieved by battery management systems (BMSs) that can monitor changes in the present capacity of the battery. Considering their limited computational resources, an efficient scheme is necessary. The data-driven metamodel is therefore used instead of complex battery models, which can simply capture changes in the shape of the charge curve as a battery ages. In consequence of the model reformulation, the charge curve refers to the time elapsed for charging against voltage. Under constant current charging, using time instead of capacity is favorable for computationally inexpensive BMSs. The aging-relevant parameter in the metamodel is estimated in the least-squares sense. In practice, this is often difficult as the shape of the charge curve, mostly its early part, is distorted by varying battery conditions before charging. For tolerating this distortion, a robust scheme is also required. The weighted least-squares is thus used such that the early part is given less weights whereas the later part is given more weights. The BMS-integrated metamodel and its parameter estimator are validated by using batteries with different SOH, which concludes an estimation error less than 3%. The California zero-emission vehicle (ZEV) regulation 1 was first adopted as part of the 1990 low-emission vehicle (LEV) program in the U.S., which has the aim of lowering greenhouse gas emissions and reducing petroleum consumption. The regulation is based on a credit scheme that provides automakers with credits for each ZEV they sell in California. The latest revision 1 was made to the regulation in 2012 and it will come into effect in 2018 and be in effect through 2025. According to the revised regulation, the credits earned per ZEV type will change. The credits for hybrid electric vehicles (HEVs) will become less and less and finally disappear by 2018. In contrast, the credits for battery electric vehicles (BEVs) will grow gradually. In addition, the longer range the BEVs can offer, the more credits they will receive. The revised regulation will apply to all automakers who annually sell more than 20,000 vehicles in California. If they fail to comply with the revised regulation, a $5,000 penalty per credit will be imposed. In this background, automakers that come under the revised regulation are expected to roll out ZEVs that can compete with internal combustion engine vehicles in the market. Here, ZEVs specifically involve BEVs and fuel cell electric vehicles (FCEVs). However, only the long-range BEVs are currently considered realistic and achievable in light of technology readiness and marketability.The long-range BEVs require batteries with high energy density. To use light-duty electric vehicles as an example, a range of more than 100 miles on a single charge could be realized with the battery pack energy density exceeding about 100 Wh/kg, which entails the battery cell energy density over 250 Wh/kg. As of now, a l...

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mentioning

confidence: 99%

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Sung

Hwang

Nam

et al. 2016

J. Electrochem. Soc.

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“…It is necessary to estimate the driving range for EVs to minimize the effect of driver's range anxiety and to promote the popularization of EVs [196]. A driving range estimation algorithm helps EV drivers to make effective decision on driving, charging and parking.…”

Section: Range Estimationmentioning

confidence: 99%

“…Based on published new and existing EV parameters, the simplified power train models are created and then compared with each other according to their range characteristics. In [196,202], lithium-ion and lead-acid battery models are developed respectively for driving range prediction. Both studies investigated the behaviors of batteries related to different driving styles and analyzed the relationship between the batteries and the residual range.…”

Section: Range Estimationmentioning

confidence: 99%

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Cuma

Köroğlu

2015

Renewable and Sustainable Energy Reviews

305

119

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“…Total altitude difference on the path Vrbani -SljemeSamobor -Vrbani was 859 m (Fig 5 and 6). The measurement started in the third gear of transmission with a fully loaded battery ( [9], [10]). Just before starting the incline the gear was shifted into the second and soon returned into the third gear because the motor was not overloaded even in the third gear of the transmission system.…”

Section: Measurements Done With the Converted Evmentioning

confidence: 99%

Study of a vehicle conversion from internal combustion engine to electric drive

Vražić

Vuljaj

Pavasović

et al. 2014

2014 IEEE International Energy Conference (ENERGYCON)

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A conversion of an internal combustion engine vehicle (ICE) to an electric drive vehicle (EV) was a project carried out at the Faculty of Electrical Engineering and Computing (FER). This project has lead to valuable conclusions about the ICE to EV conversion, its costs and benefits. The whole process of conversion will be presented in this article, including the applied regulations, total labor and components expenses. Finally, an analysis of the conversion cost-benefit ratio has been done, taking into account lower fuel prices, maintenance costs, battery life etc. The comparison was done between an EV and a diesel ICE vehicle.

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On-line battery identification for electric driving range prediction

Cited by 32 publications

References 17 publications

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles

A comprehensive review on estimation strategies used in hybrid and battery electric vehicles

Study of a vehicle conversion from internal combustion engine to electric drive

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