Design Optimization of Vehicle Control Networks

The advancement of electronic technology has made significant contributions to the safety and convenience of modern vehicles. New intelligent functionalities of vehicles have been implemented in a number of electronic control units (ECUs) that are connected to each in vehicle control networks (VCNs). However, with the rapid increase in the number of ECUs, VCNs currently face several challenges, e.g., design complexity, space constraints, system reliability, and interdependency. Considering these factors, the complexity of the VCN design problem exponentially increases, which means that the problem cannot be solved within a reasonable time using conventional optimization techniques. In this paper, we report a new methodology for the optimal design of VCNs. An analytical model was derived to examine the fundamental characteristics of the VCN design problem. Compared with the case of a conventional data network, which typically considers temporal scheduling over a fixed physical topology, the VCN design problem should also consider spatial constraints, e.g., volume, position, and weight. Moreover, the spatial constraints change during the solving procedure. Such temporal and spatial joint optimization problems with varying constraints incur extremely high computational complexity. To tackle the high complexity, this paper proposes a fast solution based on a repeated-matching method, which reduces the problem complexity from
O
(
NNN
) to
O
(
NN
³
). By applying our methodology to a number of different real-world VCN design scenarios, this proposal can produce a 1% near-optimal design within a significantly reduced time.