Forecasting the Retirement Quantity of Power Batteries in Henan Province for Recycling Logistics Network Optimization

DOI

10.2991/978-94-6239-758-3_51How to use a DOI?

Keywords

Power battery recycling; Grey GM(1,1) model; Stanford model

Abstract

Under the “dual carbon” goals, rapid growth of the new energy vehicle (NEV) industry has increased end-of-life (EoL) power batteries, posing challenges for recycling logistics. Accurate retirement forecasting is essential for optimizing network layout and resource allocation. Using NEV sales data in Henan Province from 2017 to 2024, a Grey GM(1,1) model is applied to forecast sales from 2025 to 2030, and installed battery capacity is estimated accordingly. Considering battery lifespan distribution, the Stanford model is used to predict the scale and temporal evolution of retirement. To improve robustness, scenario-based interval estimation and sensitivity analysis are introduced. Results show that retired battery volume will continue to grow and enter a concentrated release phase, peaking around 2032 with clear lag and phased characteristics. These findings support recycling node layout, transportation optimization, and capacity planning, and provide a reference for regional recycling system development and reverse logistics network design.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Liying Li
AU  - Junyuan Zhu
PY  - 2026
DA  - 2026/09/08
TI  - Forecasting the Retirement Quantity of Power Batteries in Henan Province for Recycling Logistics Network Optimization
BT  - Proceedings of the 2026 7th International Conference on Management Science and Engineering Management (ICMSEM 2026)
PB  - Atlantis Press
SP  - 526
EP  - 532
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-758-3_51
DO  - 10.2991/978-94-6239-758-3_51
ID  - Li2026
ER  -