Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method

Qiu, Xianghui; Wu, Weixiong; Wang, Shuangfeng

doi:10.1016/j.jpowsour.2020.227700

Cited by 129 publications

(48 citation statements)

References 24 publications

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“…In this way, the Bayesian method can present the current state of the system, but can also evaluate future trends before a given threshold. Stochastic algorithms are mainly used within degradation models and the most common Bayesian network algorithms are Particle Filters, Kalman Filters, and hidden Markov models [29,30]. Degradation can be difficult to determine, especially, when the progress status is hidden.…”

Section: Stochastic Algorithmsmentioning

confidence: 99%

“…To verify the method, it is tested on a case study of an automotive lead-acid battery. Qiu et al [30] also studies a battery, but looks at both state of charge, state of health, and RUL. To improve the accuracy of the prediction, BS-SRCKF is deployed for the state of charge, which in turn is combined with MHKF and an EKF to do a joint estimate of the state of charge and state of health.…”

Section: Stochastic Algorithmsmentioning

confidence: 99%

“…In Tabel 2 a set of application areas was defined. A large proportion was identified as being experimental [30,32,38,48,50,54]. Meaning that a component was investigated in an experimental setting and hence not necessarily attached to a manufacturer or plant.…”

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

“…Some components can have a steady linear degradation at the start of their lifetime, but then suddenly start dropping by the end of it. This is common within e.g., batteries [30,33,54]. Hence, an issue of determining a hidden state process.…”

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

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Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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Section: Stochastic Algorithmsmentioning

confidence: 99%

Section: Stochastic Algorithmsmentioning

confidence: 99%

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

Section: Challenges For Predictive Maintenance Applicationsmentioning

confidence: 99%

See 2 more Smart Citations

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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“…In recent years, to enhance the flexibility of the capacity model, the prediction model has been merged with the real-time estimation method without measured data. Qie et al [29] proposed the RUL prediction method based on the SOC/SOH joint estimator. The SOC/SOH was estimated using a multiscale hybrid Kalman filter and the estimated SOH was used to update the RUL prediction model.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

et al. 2020

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To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.

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Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

Sakile

Sinha

2022

Advcd Theory and Sims

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As a new means of transportation, electric vehicles (EVs) have a lot of potential. On the other hand, EVs that employ lithium‐ion batteries face certain difficulties in forecasting the battery's health and remaining useful life. This paper uses the adaptive joint algorithm approach to calculate the battery's online parameters and accurate state of charge (SOC). To establish the battery online parameters, the forgetting factor recursive least square (FFRLS) technique is utilized, and the extended Kalman filter (EKF), unscented Kalman filter (UKF) are employed to estimate accurate SOC. Compared to the EKF/UKF method, the joint algorithm (FFRLS‐UKF) approach produces better results. The results are validated using the urban dynamometer driving schedule cycle and the ECE extra‐urban driving cycle (low powered vehicles) to determine the performance of the proposed algorithm. The error of the estimated SOC has fallen from 3.3% to 2%. The proposed adaptive joint algorithm has substantially improved the system's accuracy and provides better results than the EKF/UKF technique. Furthermore, the random variable noise is also supplied to the test data to ensure that the proposed method is robust.

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Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method

Cited by 129 publications

References 24 publications

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

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