A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.

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Trastuzumab-based palliative chemotherapy for HER2-positive gastric cancer: a single-center real-world data

BMC Cancer ◽

10.1186/s12885-021-08058-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Tae-Hwan Kim ◽

Hun Do Cho ◽

Yong Won Choi ◽

Hyun Woo Lee ◽

Seok Yun Kang ◽

...

Keyword(s):

Gastric Cancer ◽

Real World ◽

Group Performance ◽

Eastern Cooperative Oncology Group ◽

Conversion Surgery ◽

Single Center ◽

Her2 Positive ◽

Real World Data ◽

World Data ◽

The Real

Abstract Background Since the results of the ToGA trial were published, trastuzumab-based chemotherapy has been used as the standard first-line treatment for HER2-positive recurrent or primary metastatic gastric cancer (RPMGC). However, the real-world data has been rarely reported. Therefore, we investigated the outcomes of trastuzumab-based chemotherapy in a single center. Methods This study analyzed the real-world data of 47 patients with HER2-positive RPMGC treated with trastuzumab-based chemotherapy in a single institution. Results With the median follow-up duration of 18.8 months in survivors, the median overall survival (OS) and progression-free survival were 12.8 and 6.9 months, respectively, and the overall response rate was 64%. Eastern Cooperative Oncology Group performance status 2 and massive amount of ascites were independent poor prognostic factors for OS, while surgical resection before or after chemotherapy was associated with favorable OS, in multivariate analysis. In addition, 5 patients who underwent conversion surgery after chemotherapy demonstrated an encouraging median OS of 30.8 months, all with R0 resection. Conclusions Trastuzumab-based chemotherapy in patients with HER2-positive RPMGC in the real world demonstrated outcomes almost comparable to those of the ToGA trial. Moreover, conversion surgery can be actively considered in fit patients with a favorable response after trastuzumab-based chemotherapy.

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Gemcitabine plus nab-paclitaxel in older patients with metastatic pancreatic cancer: A post-hoc analysis of the real-world data of a multicenter study (the NAPOLEON study)

Journal of Geriatric Oncology ◽

10.1016/j.jgo.2021.06.011 ◽

2021 ◽

Author(s):

Futa Koga ◽

Yasunori Kawaguchi ◽

Mototsugu Shimokawa ◽

Kenichiro Murayama ◽

Shunya Nakashita ◽

...

Keyword(s):

Pancreatic Cancer ◽

Multicenter Study ◽

Real World ◽

Older Patients ◽

Metastatic Pancreatic Cancer ◽

Real World Data ◽

World Data ◽

The Real ◽

Post Hoc Analysis ◽

Post Hoc

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Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

Sustainability ◽

10.3390/su132313333 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13333

Author(s):

Shaheer Ansari ◽

Afida Ayob ◽

Molla Shahadat Hossain Lipu ◽

Aini Hussain ◽

Mohamad Hanif Md Saad

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Recurrent Neural Network ◽

Single Channel ◽

Lithium Ion ◽

Remaining Useful Life ◽

Channel Input ◽

Capacity Degradation ◽

Battery Capacity ◽

Useful Life

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

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