Detection of Utilizable Capacity Deterioration in Battery Systems

Roscher, Michael A.; Assfalg, J; Bohlen, Oliver

doi:10.1109/tvt.2010.2090370

Cited by 154 publications

(60 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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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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“…According to the definition of SOH in Eq. (8), one can apply the standard capacity test [55] or pulse current test [56] to measure the battery aged capacity and increased internal resistance. However, this direct method is inconvenient and not recommended because fully discharge using the controlled current and temperature will interrupt the normal EV operations.…”

Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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“…(4) shows that the initial SoC is important to estimate the remaining capacity. Numerous methods have been developed to accurately estimate the initial SoC [21], [22]. Most of the methods are based on the nonlinear function between SoC and OCV.…”

Section: B Soc Equalizationmentioning

confidence: 99%

“…Most of the methods are based on the nonlinear function between SoC and OCV. Reference [22] indicated that cell voltage is close to OCV in an equilibrium state, and that the curves for charging and discharging are not overlapped because of the hysteresis effect [5].…”

Section: B Soc Equalizationmentioning

confidence: 99%

Development of an Optimized Algorithm for Bidirectional Equalization in Lithium-Ion Batteries

Sun¹,

Zhu²,

Lu³

et al. 2015

Journal of Power Electronics

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Many equalization circuits have been proposed to improve pack performance and reduce imbalance. Although bidirectional equalization topologies are promising in these methods, pre-equalization global equalization strategy is lacking. This study proposes a novel state-of-charge (SoC) equalization algorithm for bidirectional equalizer based on particle swarm optimization (PSO), which is employed to find optimal equalization time and steps. The working principle of bidirectional equalization topologies is analyzed, and the reason behind the application of SoC as a balancing criterion is explained. To verify the performance of the proposed algorithm, a pack with 12 LiFePO4 batteries is applied in the experiment. Results show that the maximum SoC gap is within 2% after equalization, and the available pack capacity is enhanced by 13.2%. Furthermore, a comparison between previously used methods and the proposed PSO equalization algorithm is presented. Experimental tests are performed, and results show that the proposed PSO equalization algorithm requires fewer steps and is superior to traditional methods in terms of equalization time, energy loss, and balancing performance.

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Detection of Utilizable Capacity Deterioration in Battery Systems

Cited by 154 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 brief review on key technologies in the battery management system of electric vehicles

Development of an Optimized Algorithm for Bidirectional Equalization in Lithium-Ion Batteries

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