Mission Management for Battery Electric Trucks

Thesis Background
Sustainability including climate change are the challenges of our generation. Our contribution is to offer leading transport and infrastructure solutions enabling societies to prosper in a sustainable way. At Volvo group, we are committed to the ambitions and climate change goals of the Paris Agreement. From a lifecycle perspective, most of the emissions occur during the use phase of our products. Therefore, our priority is to develop solutions that reduce the carbon emissions from transportation. Today Volvo is the market leader in offering full range of electric trucks.


We are Vehicle Energy Management team at Group Trucks Technology (GTT). As the team’s name suggests, we work in the area of energy management, where we optimize the vehicle energy using the predicted information such as topography, curvature, and speed limits from the road ahead of the vehicle. Our goal is to deliver a platform solution to optimize the energy consumption for all three powertrain variants in Volvo (ICE, Fuel Cell and BEV (Battery Electric Vehicles).
We have been working in this technology area for several years, not just internal projects, but also within some European projects. We have also had two thesis workers in spring 2023 in this area for a Fuel cell electric vehicle (FCEV) [1].
With this master-thesis, we hope to build upon the knowledge we have gained so far and apply it to a Battery Electric Vehicle (BEV).
Problem Motivating the thesis
For a long-haul application, the range of the electric trucks is still not enough to fulfil a full day transport missions (500-800 km /day). So, it needs to make at least one or more stops to fulfil a day’s mission. This limited range and sparse availability of suitable charging stations can lead to range anxiety among drivers. We want to solve this by Mission Management, which suggests an optimal charge plan by optimizing over the whole transport mission.


The energy consumption for a truck depends on the weight of the truck (which can vary depending on loading/unloading), weather (wind speed and direction, rain etc.), traffic situations, and speed. Moreover, energy price, queuing times, charging power etc. can vary between charging stations. Due to this complexity, it can be very challenging to manually plan the mission.
Hence a need for a holistic approach like model-based optimization is needed to solve this problem. Doing a global optimization of the whole transport mission, considering the location of charging stations and energy price, vehicle characteristics, topography, mission requirements, traffic, weather etc. has a potential to solve this. By developing such solutions, we can propose an optimal charge plan (where to charge, how much to charge), and hence reduce range anxiety for the driver while minimizing the total cost of operation (TCOP) as well.




Description of thesis work
In the prior thesis work done by Sundström and Bragde [1], we explored the limitations and benefits of having such an optimization layer with full mission information. The problem was formulated to optimize the Total cost of operation (TCOP), where speed was one of the optimization variables among other variables like FC power, battery power, fueling stop etc. In this thesis we will work with a fixed speed (user input) to reduce the numerical complexity and explore other aspects like battery ageing and including temporal variables in the optimization like traffic, energy price and queuing time at charging stations.
Objective or Research Question
How can we optimize the total cost of operation (TCOP) of the mission for a BEV, for a fixed cruising set speed (user input)?
What are the important parameters and dynamics to consider for mission management for BEV?
How to incorporate cost of battery ageing aspects in the formulation, as batteries are the most expensive components in the truck.
How to incorporate time varying parameters like traffic, que times and charging price at charging stations?

Deliverables (flexible)
Formulation of the optimization problem.
Integration with Complete Vehicle simulation models (provided by Volvo).
Simulation analysis is done on a few use cases and input parameters (provided by Volvo).
Benchmarking of open-source solvers

References
[1] A. Bragde , D. Sundberg (2023) Mission Management for fuel cell heavy-duty trucks, URL: https://odr.chalmers.se/bitstreams/c1823eca-d0fa-4e0d-b9d3-2620fa9d7354/download
Suitable background


Master students in Automotive, Control, Mechatronics or Engineering Physics.
Interest in Control, Optimization
Good MATLAB/Simulink skills
Please submit your CV and motivation letter.



Thesis Level: Master

Language: English


Starting date: 15 January 2024


Number of students: 1


Physical location: Mostly at Volvo GTT, Lundby, Gothenburg


Tutor:
Saurabh Suman(+46-765538677) , olof.lindgarde@volvo.com
Olof Lindgärde(+46 76 5535910), saurabh.suman@volvo.com