An Integrated Co-Design Optimization Toolchain Applied to a Conjugate Cam-Follower Drivetrain System

Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).

Automated summary

Due to increasing performance requirements, model-based optimization and control strategies are gaining popularity in the machine building and automotive industries. However, these strategies require more modelling effort and a deeper understanding of optimization techniques, as detailed models are needed to evaluate certain performance characteristics. MATLAB Simscape and similar modelling tools help reduce this effort and allow for more complex and accurate models. However, these tools cannot be used with high-performance gradient-based optimization algorithms due to the complexity of the underlying model equations. This work presents an optimization toolchain that efficiently interfaces with MATLAB Simscape to reduce user effort and computation time required for optimizing high-fidelity drivetrain models.