A lithium-ion battery RUL prognosis method using temperature changing rate

Li, Yang; Zhao, Lingling; Su, Xin; Wang, Shuai

doi:10.1109/icphm.2016.7542866

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…The electrochemical processes in the battery cells depend on temperature, and the temperature of the battery cells will vary according to energy conservation (3) [56] even with constant ambient temperature. Furthermore, cell temperature has been successfully demonstrated as predictors for SoC, SoH and SoP [48], [60], [61]. In real applications the conditions are not ideal and mostly unknown ahead of time, e.g.…”

Section: Dynamic Profilesmentioning

confidence: 99%

“…It has been proposed that battery surface temperature is correlated with battery capacity [61], as demonstrated in Figure 3d), through the Joule effect and the relationship between internal resistance and capacity [15]. Determining SoH and RUL by surface temperature alone falls in the category of experimental methods.…”

Section: A Battery Surface Temperaturementioning

confidence: 99%

“…Determining SoH and RUL by surface temperature alone falls in the category of experimental methods. Yang et al [61] proposed estimation of RUL based on the temperature difference over a full cycle, making it inappropriate for online applications and dynamic cycling. In theory it is scalable to a set of thermally isolated connected cells, however for cells in a pack under real conditions, the flow of heat between cells and the surroundings as well as temperature variation due to thermal management, makes the method impractical.…”

Section: A Battery Surface Temperaturementioning

confidence: 99%

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Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions

et al. 2019

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Owing to a global effort towards reducing carbon emissions, electric vehicles (EVs) have emerged to replace the petroleum-fueled vehicles. However, the battery is a bottleneck restricting EVs from being utilized in the same way as petroleum-fueled vehicles. Lithium-ion batteries (LiBs) are commonly used in EVs, but have an optimal temperature range, and operation outside this range causes accelerated aging in the form of capacity fading and power fading, especially in cold climates. We propose that both state parameter estimation and thermal management are interconnected problems and should be addressed together: Battery health and performance depends on temperature, while temperature depends on operational conditions, battery health, structural design and thermal management. Temperature dependent decay accounting for heat generation in cells, temperature variation between cells and heat transfer with surroundings, can allow more accurate state parameter estimation and guide thermal management strategies. This review investigates how the dynamics of temperature dependence and heat generation are addressed in the literature related to estimation of battery state parameters. Approaches involving temperature were divided into three categories: 1) maintain constant ambient temperature and omit battery temperature, 2) verify at different ambient temperatures, and 3) use available data for cell and/or ambient temperature. A valid solution to the problem in real applications, must satisfy three criteria: a) suitable for online applications, b) scalable to battery packs, and c) applicable to dynamic battery cycling occurring during normal use. The most promising methods include coupled thermal and electric models with adaptive filtering, and recurrent neural network methods.

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Section: Dynamic Profilesmentioning

confidence: 99%

Section: A Battery Surface Temperaturementioning

confidence: 99%

Section: A Battery Surface Temperaturementioning

confidence: 99%

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Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions

et al. 2019

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“…For example, Saha et al [19] used the battery's impedance as the HI for the SOH of battery, and Tong et al [20] considered the open-circuit voltage, both under the circumstances of constant discharge. Moreover, Liu et al [21] discovered that the time interval of equal discharging voltage difference in each discharge cycle can be used as a HI to represent the capacity degradation; Li et al [22] used the temperature-change rate as the battery's HI. For the HIs extracted under a constant charge process, some contributions are worthy of attention.…”

Section: Algorithms/methods Advantages Disadvantagesmentioning

confidence: 99%

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Tang

et al. 2017

Energies

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Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system's proper operation. A direct way to estimate the SOH is through the measurement of the battery's capacity; however, this measurement during the battery's operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery's operation. These indicators are extracted from the battery's voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery's capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

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“…Among current research, researchers mainly obtained indirect features from the discharging process and the charging process. Yu et al 3 found several indicators which were selected from the current and voltage of the discharging process of batteries under randomized loading conditions and similar ways to extract features were used in other studies 28‐31 . However, the drawback of the feature extraction from the discharging process is that it is highly sensitive to the batteries’ operation.…”

Section: Introductionmentioning

confidence: 99%

Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

Yang

et al. 2020

Int J Energy Res

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A lithium-ion battery RUL prognosis method using temperature changing rate

Cited by 11 publications

References 16 publications

Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions

Temperature-Dependence in Battery Management Systems for Electric Vehicles: Challenges, Criteria, and Solutions

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

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