Customized Production Planning And Workshop Scheduling For Power Battery Considering Equipment Failures

In recent years,with the favorable policy and other factors,the new energy vehicle market in China has developed rapidly,and power batteries as the core components of new energy vehicles have a strong demand in the product market.At the same time,customers’ demand for product customization is increasing,and the large-scale production management system is no longer appropriate for the current power battery manufacturing,therefore,the production of power battery manufacturing must be transformed from traditional large-scale mass production to multi-variety and small-batch customized production.Intelligent production planning and scheduling and workshop scheduling technology will play a key role.In response to the customized production needs of power battery manufacturing enterprises,this paper adopts swarm intelligence algorithm as its core,combined with set membership filtering algorithm,to design a reasonable production planning and schedule and formulate workshop scheduling scheme.The main research works of this paper are as follows.1.A dynamic spatial particle swarm algorithm based on orthotope is proposed for the production planning and scheduling of customized power batteries.A production plan model for customized power battery enterprises is constructed,with the objective function of minimizing the total storage cost and total penalty cost of customized orders under actual production capacity constraints.The global search is carried out using the particle swarm algorithm,and to improve the global convergence of the particles,the research proposes to construct a measurement strip by the optimal particle of the swarm as the center for dynamically updating the particle swarm’s search space.Meanwhile,particles outside the search space are re-initialized to increase population diversity.Simulations demonstrate that the scheduling plan obtained with the proposed algorithm can effectively reduce the production cost of power battery manufacturing enterprises under customized condition.2.A fault diagnosis algorithm based on orthometric hyperparallel spatial directional expansion filtering is proposed for the coating machine equipment in the power battery production line.Firstly,on the basis of the traditional parallelotopic space structure,the orthometric hyperparallel space is defined using the extreme values of adjacent hyperplane space vertices,while simultaneously ensuring the monotonic convergence of parameter boundary values within the feasible set of the envelope parameter.Then,the intersection between the parallelotopic space and the strip is used to detect whether a fault has occurred in the coating machine system,followed by orthometric hyperparallel space directional expansion when a fault occurs,by relying on the intersection between the band space and the positive orthometric hyperparallel space in the direction of expansion to achieve fault isolation.Finally,the fault identification is completed using the shrinkage property of the orthometric hyperparallel space during the iteration process.Simulation examples have validated that the algorithm can effectively improve the speed and accuracy of fault diagnosis of the coating machine system.3.A hybrid variable neighborhood particle swarm algorithm is proposed for the power battery workshop scheduling problem with coating machine equipment failure under customized orders.A workshop scheduling model is constructed based on the production of power batteries,with the objective of minimizing the maximum completion time of orders,and a global search is performed using the particle swarm algorithm.To avoid particles getting trapped in local optima,the algorithm uses a multifold symmetry learning method to obtain the neighborhood solutions around the particle and break out of the local optima.Meanwhile,to prevent the optimal individual of the entire population from falling into a premature convergence state,an improved variable neighborhood method is proposed for the solution.When the coating machine in the power battery workshop experiences a failure,a local rescheduling algorithm is proposed to obtain the dynamic scheduling results for the workshop.Simulation examples demonstrate that the scheduling plan developed with the proposed algorithm can effectively reduce the maximum completion time of power battery production orders under customized condition.To sum up,the paper conducts research on customized production planning and workshop scheduling for power batteries,taking into account equipment failures,the feasibility and effectiveness of the proposed algorithm applied to power battery production management are verified by simulation examples,finally,the paper summarizes and prospects the research content of this topic.