Collection mode choice of spent electric vehicle batteries: considering collection competition and third-party economies of scale

Blockchain technology embedded in the power battery for echelon recycling selection under the mechanism of traceability

This paper examines the use of blockchain technology in power battery echelon recycling. The technology helps to improve battery capacity identification and market transaction trust. The study focuses on power battery manufacturers and recycling participants. Two recycling modes are constructed using the Stackelberg game method, and the optimal decision-making of the participating subjects in the two modes of power battery echelon recycling under the embedding of blockchain technology is compared. The influence of each parameter on the optimal decision-making is analyzed. The research findings indicate that the degree of blockchain technology integration rises as the preference coefficient for traceability information increases. When recycling competition is intense and the sensitivity of recycling prices is low, the optimal recycling model for the number of spent power batteries (SPBs) to be recycled is the model in which echelon utilizers do not participate in recycling if the level of cost optimization coefficient embedded in blockchain technology is high, otherwise, it is the model in which echelon utilizers participate in recycling. The profit of power battery manufacturers and echelon utilizers decreases with the increase of the intensity of power battery recycling competition, the cost optimization coefficient of echelon utilizers and the cost optimization coefficient of manufacturers.

A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks.

Performance evaluation of regulatory schemes for retired electric vehicle battery recycling within dual-recycle channels

As electric vehicles (EVs) are developing at a rapid pace, the foreseeable "scrap tide" of EV batteries poses a severe challenge to ecological protection. This article investigates a dual-recycle channel closed-loop supply chain and provides regulatory solutions to retired EV batteries' recycling. Specifically, we construct four supervision scenarios: S1 no policy intervention, S2 reward-penalty scheme, S3 deposit-refund scheme, and S4 dual scheme combining S2 and S3. Based on the Stackelberg game and empirical data, all scenarios' recycling performance is evaluated and compared with a view to "society, economy, and environment". The results revealed: (1) Compared with S1, the recycling rate and carbon reduction rate in S2∼S4 increase by 2.6049%/0.0092%, 4.0379%/0.0285%, and 6.6660%/0.0379%, respectively; (2) The difference between S2 and S3 in recycling performance depends on regulatory intensities, yet the latter places greater burdens on consumers and firms. The S4 presents optimal environmental performance but at the expense of socioeconomic development; (3) As regulatory intensity increases, social welfare rises driven by environmental benefits, then falls due to overburdened supply chain profits, consumer surplus, and policy expenditures; (4) Carbon trading prices and EVs' potential market sizes affect regulatory schemes' operations. Our results contribute to policy-making and managerial practices for EV battery recycling.