Background of thesis project
Volvo Construction Equipment, a division of Volvo Group, is one of the world's largest manufacturers of construction equipment such as wheel loaders, dumpers, excavators, road machinery, and compact machines. The production is distributed in Europe, Asia, North America and South America.
To maintain a leading position, it is important to develop innovative products with high quality and, at the same time, high energy-efficient powertrains.
Volvo CE, as part of the Volvo Group, has started to investigate further more the hybrid technology as possible solution to reach a high level of efficiency and quality on its off-road vehicles. Considering the hybrid and electric vehicle domain, the control strategy for the hybrid system and for managing the energy flow plays a key role to achieve the best system energy-efficiency together with vehicle performance and reliability.
Suitable background of students
The student should preferably have a background in computer science, control theory, optimization theory, mechatronics or hydraulics engineering.
Experience and interest in automotive applications and machine learning is valuable. Own drive, curiosity, ability to analyze and conclude and document is important skills. The focus of the thesis will be on investigation through data analysis and simulation, implementation and test on testbench or machine and documentation of algorithms.
Description of thesis work
The machine under consideration is a hybrid machine with and internal combustion engine and a battery. Majority of the construction equipment’s operating cycle is repetitive. They will be driven along the same path doing the same operation over and over again. Since the operators involved will not be changed often, the way the machine is used will be very similar. The energy usage of the machine will be quite similar among such repetitive cycles. The thesis aims to investigate and propose a method to optimize the energy distribution when the machine is operated in such repetitive environment. The main goals of this work will be to:
  • Identify that the machine is undergoing a repetitive cycle using pattern recognition, investigate and determine a method to store the energy demand during the cycle vs time/location of the machine. The goal is not to determine the map, only the energy/power cycle or an indicator to the consumption. The time for each cycle need not be very long
  • Investigate difference methods to use the information from the previous step to optimize the state of charge level of the battery to operate the machine with highest energy efficiency and without draining or overcharging the battery
  • Provide a review of the available approaches to online implementation of this strategy, followed by a section comparing simulation results of the mean strategies for the different operating scenarios
Thesis Level: Master
Starting date: February 2020
Number of students: 2
George Jithin Babu, MSc
Volvo Construction Equipment,
Dept CE42250, TCC11,
tel. 016 5414780

O nas

The Volvo Group is one of the world’s leading manufacturers of trucks, buses, construction equipment and marine and industrial engines under the leading brands Volvo, Renault Trucks, Mack, UD Trucks, Eicher, SDLG, Terex Trucks, Prevost, Nova Bus, UD Bus and Volvo Penta.

Volvo Construction Equipment is a global company driven by passion, curiosity and by our purpose: to build the world we want to live in. We believe that only through imagination and teamwork can we develop a world that is cleaner, smarter and more connected. Our company culture reflects this belief through the care and trust it places in our customers, employees and suppliers. People are at the heart of our business. It is through our strong network of talented, enquiring and innovative minds that we have been able to pave the way towards a more sustainable future. The global construction industry is our arena and our employees are our greatest assets.

We can only continue to stand out as industry leaders through the people driving us forwards. Come be a part of our team and help us build tomorrow.

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