Co-design Optimization of Intelligent Electric Vehicles

9 July, 2019 | Vehicular Technology

Summary by Lyu Chen

Cyber-Physical Systems (CPSs) exhibit a multidisciplinary nature. Intelligent vehicles are a typical example of CPS which have recently gained increasing attention from academia, automotive industry, and governmental organizations.

Meanwhile, electrified vehicles have been a key topic of R&D for decades, due to the fossil fuel crisis and stringent standards on vehicle emissions. Thus, combination of the above two aspects makes the intelligent electric vehicle (iEV) a newly emerging focus.

As a complex CPS, an iEV involves several subsystems: the controller, the physical vehicle plant, and the human driver. They are highly coupled and interacted, determining vehicle’s overall performance jointly. To achieve the optimal vehicle performance, a wealth of optimization approaches has been explored and implemented.

However, since the physical plant is not optimized in sync with the controller, and different subsystems are designed independently, they fail to achieve the globally optimal selection of architecture and parameters. The design space is therefore limited, and the vehicle performance potentials can hardly be fully explored.

In this context, CPS-based co-design optimization methodology provides the ability to extend the design space and improve the overall system performance. The co-design of an iEV can be formulated as a multi-objective optimization problem.

The goal is to find optimal assignments for design variables to maximize performance while satisfying a number of constraints. Platform-Based Design (PBD) is one promising approach that can be adopted to solve the formulated complex co-design optimization problem. The PBD is a meet-in-the-middle approach that favors re-usability.

At the top layer, there are high-level requirements and constraints. The bottom layer is defined by a design platform, i.e., a library of components characterized by their behaviors and performance. For iEV design, the bottom layer could contain the models of the vehicle dynamics, powertrain, brakes, and driver-style-based controller.

The design problem is to select a set of components and their parameters so that the constraints are satisfied with the objective functions optimized. The selection process is called mapping and completed in the middle-layer meeting point. The obligations captured in the requirements and constraints are discharged by particular components or combinations thereof.

With the above methodology, the co-design optimization of the physical topology and parameters, controller protocols and variables, with consideration of driver styles and behaviors for iEV becomes possible.

Full article: IEEE Transactions on Industrial Electronics, Volume 66, Number 4, April 2019

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