# Development of a Power and Communication Bus Using HIL and Computational Intelligence
## 7 Conclusions  
The goal of this research was to develop and test a power and communication bus, with a focus on developing and finding the best parameters for auto-addressing. All goals were achieved. The bus was developed with better parameters than were expected; it is up to four times faster (up to 2 Mbps, where 512 kbps was the target) and can transfer up to 480 W (48 V and 10 A). DLN is cutting-edge technology, which can significantly reduce the number of cables in cars, which at the end reduce \(CO_{2}\) footprint, the weight of a car and also reduce costs by reducing production time and used materials. The authors are confident that the proposed solution can be used in all non-safety applications in a car. The cost of the example of usage is much lower than the current solution.  
The authors, using the HIL setup, artificial intelligence computing in a particular genetic algorithm, swarm optimization and planning an experimental method, checked multiple parameters of DLN and chose the best one to ensure the proper operation of the auto-addressing function. The best results were achieved by using artificial intelligence algorithms, in both cases of the solution and the difference between module addresses. Out of the two used algorithms, both have better results in the highest number of individuals in a population. Particle swarm optimization delivered the best results in the tested approach. However, in future, the use of PSO algorithms with more variants of minimal neighbour fraction parameters have to be tested, especially 0.2 and 0.3.  
The proposed bus DLN can be implemented in new cars after real life tests and admission tests. DLN can be commonly used in QM solutions and not in safety systems due to the lack of redundancy. The proposed Hardware-In-The-Loop system connected to artificial intelligence proved to be a perfect method for adjusting physical hardware. This approach can be used in the future for solving similar problems.  
Figure 19: Results from all algorithms. Black ball—LHS. Red fronts and diamonds—GA, Blue x—PSO.  
**Author Contributions:** Conceptualization, M.S. and P.P.; methodology, M.S. and P.P.; software, M.S.; validation, M.S. and P.P.; formal analysis, M.S.; investigation, M.S.; resources, M.S.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, P.P.; visualization, M.S.; supervision, P.P.; project administration, M.S.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Paid from the funds of the Ministry of Education and Science 12/DW/2017/01/1 from day of 7 th of November 2017 r.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. Publicly available data sets were analyzed in this study. Data can be shared on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
LIN Local Interface Network  
S Hardware-In-The-Loop structure  
**P**  
Parameters--Decision variables  
R Measure Results  
C criteria  
\(c_{i}\)\(i\)-th criterion function  
\(\textbf{p}_{h}\) hardware parameters  
\(\textbf{p}_{s}\) software parameters  
\(\Omega\) boundaries of ph and ps  
Rpi  
Raspberry PI  
AP Access Point  
MQTT Message Queuing Telemetry Transport  
IOT Internet Of Things  
PS Power Supply  
M Master board  
**C**  
Client Board  
MOA Multi Objective Algorithm  
A/S Actuator or Sensor  
\(c_{n}\)\(i\)-th of Client Board  
MDA (\(c_{2}\)) minimal difference between address  
LHS Latin Hypercube Sample  
DLN Device Lightweight Network  
EMI Electromagnetic Interference  
OEM Original Equipment Manufacturer  
PCB Printed Circuit Board