A facile data-driven battery capacity estimation framework for on-road plug-in hybrid electric vehicle

IF 4.8 2区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Progress in Natural Science: Materials International Pub Date : 2025-02-01 DOI:10.1016/j.pnsc.2024.11.012

Jianlu Li , Yanming Chen , Tongxing Lei , Jianguo Liu , Guizheng Liu , Zhaoyang Deng , Xuebiao Wu , Zhiyu Ding , Yinghe Zhang , Junwei Wu , Yanan Chen

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引用次数: 0

Abstract

A data-driven framework with strong generalization capabilities is proposed to effectively extract features and easily access battery capacity. This framework can make highly accurate predictions for the battery capacities of plug-in electric vehicles. The feature extraction process is entirely based on statistics, which are always available and can be generalized to various types of battery data. An improved ampere-hour integral method can easily access battery capacity with just short-charging segments lasting 500 s. Several machine-learning models are trained to verify the framework's effectiveness, with the best model achieving a test error of 0.84 % based on leave-one-out validation. SHAP values are used to provide a reasonable interpretation of the relationships between the constructed features and model outputs. The proposed framework offers advantages such as reduced computational resources, wide generalization, and high prediction accuracy, showing great potential for battery management.

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来源期刊

Progress in Natural Science: Materials International 综合性期刊-综合性期刊

CiteScore

8.60

自引率

2.10%

发文量

2812

审稿时长

49 days

期刊介绍： Progress in Natural Science: Materials International provides scientists and engineers throughout the world with a central vehicle for the exchange and dissemination of basic theoretical studies and applied research of advanced materials. The emphasis is placed on original research, both analytical and experimental, which is of permanent interest to engineers and scientists, covering all aspects of new materials and technologies, such as, energy and environmental materials; advanced structural materials; advanced transportation materials, functional and electronic materials; nano-scale and amorphous materials; health and biological materials; materials modeling and simulation; materials characterization; and so on. The latest research achievements and innovative papers in basic theoretical studies and applied research of material science will be carefully selected and promptly reported. Thus, the aim of this Journal is to serve the global materials science and technology community with the latest research findings. As a service to readers, an international bibliography of recent publications in advanced materials is published bimonthly.