Electric Vehicle Battery Disassembly Sequence Planning Based on Frame-Subgroup Structure Combined with Genetic Algorithm

Abstract

Since the electric vehicle battery (EVB) is wildly recycled in industry, the disassembly procedures of variable EVBs is so important that can influence the efficiency and environmental impacts in remanufacturing. To improve disassembly efficiency in EVB remanufacturing, a disassembly sequence planning method based on frame-subgroup structure is proposed in this paper. Firstly, the improved disassembly relation hybrid graph and disassembly relation matrix are proposed to identify the disassembly precedence relationship and connection relationship between the components in EVB. Secondly, the frame - subgroup structure is given, and the method for solving disassembly sequence planning with frame-subgroup structure and genetic algorithm is introduced. In this method, to simplify the series of processes such as encoding, decoding, crossover and mutation, the solution space composed of all disassembly sequences is transformed into the positive integer sequence for the disassembly efficiency in battery remanufacturing. Finally, the case study of EVB disassembly sequence planning is presented to validate the feasibility of this proposed method. Comparing with other traditional methods, the advantage and application of this proposed method are introduced.

Optimization of Disassembly Strategies for Electric Vehicle Batteries

“…This question leads to an optimization problem that must be solved for individual batteries to significantly increase the economic efficiency of disassembly as the most expensive processing step in the current state [11]. Ke et al [12] performed disassembly tests on the same battery type with the same skilled workers. They observed that the workers could disassemble the battery at least 11.5% faster when they had an optimized disassembly sequence.…”

“…In this way, different decisions can be made with the help of the optimizer, such as the optimal number of tool and direction changes as well as optimal circular economy strategies at the component level. In the context of battery disassembly, Ke et al [12] proposed a disassembly sequence planning approach for EVBs based on a genetic algorithm using a frame-subgroup structure. The presented method has better convergence properties compared to other genetic algorithm implementations.…”

“…In this work, we will focus on the use of a modified genetic algorithm, since genetic algorithms are the most widely used optimization method for finding optimal disassembly strategies [25]. Furthermore, they offer multiple advantages compared to other metaheuristic methods, such as a wide application range, strong expansibility, and high robustness, since they usually do not fall into local optimal solutions [12]. In addition, genetic algorithms are attractive because, first, they quickly and cost-effectively produce high accuracy solutions, even when the solution space is huge, and second, they are easy to understand and implement since simple mathematics is involved [27].…”

“…In the literature, there are several selection procedures. Ke et al [12] used the roulette wheel selection method to select the fittest disassembly sequences for EVBs. In this work, a tournament selection technique is used.…”

“…This question leads to an optimization problem that must be solved for individual batteries to significantly increase the economic efficiency of disassembly as the most expensive processing step in the current state [11]. Ke et al [12] performed disassembly tests on the same battery type with the…”