A risk analysis method for potential failure modes in the lithium-ion battery assembly process based on optimized FMEA and DHHFLTS

Abstract

To enhance product quality and operational safety of lithium-ion batteries, this paper proposes a risk analysis method based on an optimized Failure Modes and Effects Analysis (FMEA). Firstly, as an evaluation language with a double-hierarchy structure, the Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) can both express rich semantics and accurately capture the evaluators' hesitancy. Secondly, the Similarity Measure (SM) and Water Filling Theory (WFT) are utilized to calculate the consensus degree of evaluator's evaluation information, thereby determining the weight information. Compared to the current methods that calculates weights based on the dispersion of evaluation information, SM-WFT is more suitable for FMEA. Then, the Combined Compromise Solution (CoCoSo) employs multiple aggregation methods for compromise calculation, thereby minimizing the likelihood of identical risk rankings in FMEA. Finally, addressing the difficulty of experimentally verifying the calculation results of new methods, this paper proposes a Multi-Objective Aggregation Technique (MOAT). By constructing a multi-objective mathematical model, a comparative analysis is conducted on the risk ranking results and evaluation information obtained from the new method versus other methods. At the end of the paper, a case study on risk analysis of potential failure modes in the lithium-ion battery assembly process is presented to verify the practicality and objectivity of the new method.

Keywords: CoCoSo; Double hierarchy hesitant fuzzy linguistic term set; Multi-Objective aggregation technique; Potential failure modes risk analysis; SM-WFT.

Fig. 1

Schematic diagram of the structure of the DHLTS.

Fig. 2

Distribution diagram of the second-hierarchy linguistic terms set.

Fig. 3

The five-step FMEA method.

Fig. 4

Flowchart for optimizing FMEA risk analysis of new method.

Fig. 5

Structural diagram of a square lithium-ion battery.

Fig. 6

Process flow diagram for square lithium-ion battery manufacturing.

Fig. 7

Process flow diagram of square lithium-ion battery assembly workshop.

Fig. 8

Function tree of the ultrasonic welding station.

Fig. 9

Failure network of the ultrasonic welding station.

Fig. 10

Failure net for laser welding station and battery gluing station.

Algorithm 1

Encapsulation function of MOAT.

Fig. 11

Pareto front for solving the multi-objective mathematical model.

Fig. 12

The differences in S, O, D and ranking between M1-M4 and the optimal solution.

Fig. 13

Ranking of failure modes under different .