\chapter{Conclusion}
In the first part of this work we focused notably on the general mathematical description of the electric vehicle. We presented a vehicular model of longitudinal dynamics which is used afterwards to determine the demanded power profile for a given driving cycle. The power profile is highly influenced by the actual terrain, that is why the position-based transformation of the power profile calculated from GPS and 3D-maps data was proposed. 

Next section shows the basic topology of an EV system. Also the main EV's components which are batteries, super-capacitors, DC/DC converter and electric motor are described and their respective mathematical models are expressed. For the purpose of energy transfer efficiency, the examples of characteristic efficiency maps for different components are presented. 

In the next chapter, some energy optimization methods and problems are examined. Furthermore, optimization algorithms principles are explained in detail with their simulations and results included. We can conclude, that by using these algorithms, we can save up to 40~\% of battery energy depending on the driving cycle and also we can notably increase the battery life cycle by eliminating battery high power demand variations. These facts will positively influence the autonomy and performance of the EV which will decrease the vehicle cost and increase the competitiveness against internal combustion engines vehicles.

Concerning the Java implementation, we have succeeded to implement the optimization algorithms and to optimize the dynamic programming algorithm computational times where we have achieved more than four thousand times shorter computational times while receiving the same results in Matlab simulations environment. 

Finally, we have demonstrated these algorithms on a real mobile platform Samsung Galaxy Tab which runs Android operating system. We have developed a prototype EV energy consumption optimization application. It acts like a clever GPS system and it can find directions of a route while optimizing the PSR between battery and super-capacitors during the driving cycle. It uses Google Maps JavaScript API to get geographic data of the driving cycle and to display the route. The application proves the functionality of the energy management algorithms and demonstrates the energy saves.

\section{Future Work}
In the current development state, the application covers basic navigation operations and electric vehicle energy consumption optimization within the route. It is desirable to improve these functionalities in the next versions.

At first, better velocity profile should be determined with respect to vehicle physical restrictions and preferably also to minimal energy consumption. Also the altitude profile should be examined more in detail in order to eliminate altitude sampling errors and aberrations. This will guarantee better power profile estimation and consequently better energy optimization.

Furthermore, the application user graphic interface should have better user-friendly ergonomic design. To be able to compete with nowadays navigation systems available on the market, more enhancements shall be added. Between the most important is for example possibility of off-line map access and planing (without internet connection) and speech synthesis for navigation.

The application should be used in automotive context which requires high application stability, reliability and security. Therefore, the application should be run under a real-time operating system. Android does not natively support hard real-time capabilities. There are some studies~\cite{mongia10} that consider Android system as soft-real time and experimentally measure application time delays. However in our case, the soft-real time system could not be sufficient. There are more approaches how to implement hard-real time capabilities to Android system. All of them consist in modifying the Android architecture~\cite{maia10}. This approaches should be examined and studied in detail in the future.
