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Recycling spent lithium-ion batteries is a significant way to achieve life-cycle management and a green circular economy, helping to achieve carbon neutrality. The form of battery pack disassembly has gradually moved away from manual manipulation to a human–robot collaborative process. However, there is no established methodology for assessing spent lithium-ion battery disassembly between the environmental benefits, technical feasibility, and economic viability of human–robot collaborative disassembly (HRCD). To ensure the efficiency and security of HRCD processes, this paper develops a resilience assessment model for HRCD using stability, redundancy, efficiency and adaptation metrics. The resilience assessment theory is used to obtain the internal relations among the resilience indicators of the system. In this work, we evaluate the resilience of HRCD for lithium-ion battery disassembly by integrating fuzzy Bayesian fusion with an analytical network process unfolding cloud. The 2017 Chevrolet Bolt driver demonstrated the feasibility and effectiveness of the proposed approach, which allows greater flexibility in complex tasks performed by humans and robots. In case study, the weights values of stability, redundancy, efficiency and adaptability are 0.340533, 0.161384, 0.203835 and 0.294248, respectively. IR2&HO1 and IR3&HO2 have an assessment value of 93 and 95, respectively, with eigenvalues of 1.28 and 1.23, which are level I. RI3&HO1 and RI4&HO3 belong to level IV, with an evaluation value of 38 and 45, respectively, and a characteristic value of 2.27 and 2.55, respectively. The integrated methodology proposed in this paper could be effectively used by disassembly management, to evaluate the HRCD disassembly scheme resilient strategies for better managing production. [ABSTRACT FROM AUTHOR]