On-line parameter, state-of-charge and aging estimation of Li-ion batteries

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

doi:10.1109/vppc.2012.6422617

Cited by 15 publications

(17 citation statements)

References 15 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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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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“…The existing battery battery model was developed in [8] and is shown in Figure 1. It consists of an ideal voltage source, V oc [V], which is State-of-Charge (SoC) dependent, an internal resistance R 0 [Ω], SoC dependent and different for charging and discharging, and two RC blocks, used to mimic the dynamic behavior of the battery.…”

Section: Battery Model Considering Temperature Effectsmentioning

confidence: 99%

“…Figure 8 shows a comparison between a model not including temperature influences (model presented in [8], identified using data measured at 25 • C ambient temperature) and the model developed in this work. For simplicity, the model developed in [8] will be referred to as model 1, while the one proposed in this work, including temperature effects, as model 2. Three of the tests are analyzed, with data collected at -10 • C, 0 • C and 10 • C ambient temperature.…”

Section: Experiments and Validationmentioning

confidence: 99%

“…EVs rely on batteries for providing the energy required for driving, while HEVs use batteries for temporary energy storage during driving with the goal of optimizing fuel efficiency and/or increasing performance. The high production cost of batteries, together with their inherent characteristics (the nonmeasurable nature of important parameters) motivated a great deal of research on battery modelling and state estimation ( [1] [8]). Temperature plays an important role in the behaviour of the battery, both on longer and shorter time scales.…”

Section: Introductionmentioning

confidence: 99%

“…For all these applications, it is important to have a good mathematical description of how the battery operates. In this work, the electrical battery model presented in [8] is extended to include temperature effects. A model identification procedure is presented and validation tests are performed.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced battery model including temperature effects

̧ca¹,

Wilkins²

2013

WEVJ

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Within electric and hybrid vehicles, batteries are used to provide/buffer the energy required for driving. However, battery performance varies throughout the temperature range specific to automotive applications, and as such, models that describe this behaviour are required. This paper presents a dynamic battery model describing the battery voltage based on current and temperature. A model identification method is presented and validation tests are performed. The results show that when temperature influences are included in the models, a significant increase in performance can be achieved. Modelling errors lower that 5% are achieved for validation tests conducted at -10 • C, 0 • C and 10 • C, whereas the models that do not consider temperature show errors up to 10%.

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Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean

Cârstoiu

Micea

et al. 2016

Int. J. Energy Res.

200

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Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off‐the‐shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd.

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On-line parameter, state-of-charge and aging estimation of Li-ion batteries

Cited by 15 publications

References 15 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

Enhanced battery model including temperature effects

Battery state of health estimation: a structured review of models, methods and commercial devices

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