A Novel Indirect Health Indicator Extraction Based on Charging Data for Lithium-Ion Batteries Remaining Useful Life Prognostics

Gao, Dong; Huang, Miaohua; Xie, Jiangang

doi:10.4271/2017-01-9078

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Numerous studies into battery PHM techniques have been carried out, e.g. the use of Neural Nets [10], Unscented Kalman Filters [17,19], Unscented Transform [4], Hardy Space H ∞ Observers [41] and Physics Based models [16]. Although we are assuming a hypothetical/generic battery PHM method in this paper to provide parameters in our latter modelling, it is envisaged that advanced PHM techniques can be integrated in our future verification framework.…”

Section: Battery Modelling and Phmmentioning

confidence: 99%

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management

Zhao

Osborne

Lantair

et al. 2019

Lecture Notes in Computer Science

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The battery is a key component of autonomous robots. Its performance limits the robot's safety and reliability. Unlike liquid-fuel, a battery, as a chemical device, exhibits complicated features, including (i) capacity fade over successive recharges and (ii) increasing discharge rate as the state of charge (SOC) goes down for a given power demand. Existing formal verification studies of autonomous robots, when considering energy constraints, formalise the energy component in a generic manner such that the battery features are overlooked. In this paper, we model an unmanned aerial vehicle (UAV) inspection mission on a wind farm and via probabilistic model checking in PRISM show (i) how the battery features may affect the verification results significantly in practical cases; and (ii) how the battery features, together with dynamic environments and battery safety strategies, jointly affect the verification results. Potential solutions to explicitly integrate battery prognostics and health management (PHM) with formal verification of autonomous robots are also discussed to motivate future work.

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Section: Battery Modelling and Phmmentioning

confidence: 99%

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management

Zhao

Osborne

Lantair

et al. 2019

Lecture Notes in Computer Science

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show abstract

“…Capacity and internal resistance are usually used as direct health indicators to characterize the SOH of Li-ion batteries, but they typically need to be obtained offline by intrusive methods under laboratory conditions, making them subject to many restrictions in practical application scenarios. Indirect health indicators need to be able to indirectly characterize the SOH of Li-ion batteries and can be measured online [36]. In [37], the discharging voltage difference of equal time intervals was chosen as an indirect health indicator to characterize the SOH, while in [38,39], the time interval of equal discharging voltage difference was chosen as an indirect health indicator to characterize the SOH.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

et al. 2022

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The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results.

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Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems

Wotawa

2020

Artificial Intelligence Techniques for a Scalable Energy Transition

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A Novel Indirect Health Indicator Extraction Based on Charging Data for Lithium-Ion Batteries Remaining Useful Life Prognostics

Cited by 8 publications

References 33 publications

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management

Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

Using Model-Based Reasoning for Self-Adaptive Control of Smart Battery Systems

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