Minimizing the energy consumption of an electric vehicle potentially results in extended electric driving range and energy savings, which translate to cost reduction. The energy consumption of the electric vehicle depends on the operational efficiency of the powertrain components such as battery, dc-dc converters, inverters, electric machine, transmission as well as the energy consumption of electric auxiliaries (e.g. HVAC system). The reduction of energy consumption can be achieved using Predictive Powertrain Control (PPC). A PPC works like a receding horizon controller that acts as a supervisory controller to maximize the powertrain efficiency and reduce the energy consumption of the auxiliaries. For this study, our PPC optimizes the vehicle speed using the available information of the conditions of the road ahead (slope, traffic, speed limit) and efficiency maps of the powertrain components (battery, electric machine, inverter, transmission). This results in an optimization problem which is challenging to solve due to the non-linear nature of the powertrain components, the different vehicle architectures, physical constraints, and the need of solving the optimization problem in real-time. The objective of this assignment is to develop an optimization algorithm using Dynamic Programming (DP) techniques that is tailored to the requirements of a real-time implementable PPC.
The goal of this project is to develop a Predictive Powertrain Control (PPC) algorithm based on DP. The development of a PPC strategy typically includes two main steps. The first step is formulating an optimal control problem. In this assignment, the PPC finds the optimal vehicle speed that minimizes energy consumption while satisfying a set of physical constraints. In an automotive system, the vehicle speed is influenced by the torque applied in the wheels, the rolling resistance, air drag, gravitational force, among others. The relationship between the force applied in the wheel and the energy provided by the battery depends on the system architecture and the efficiency maps of the components. This relationship is normally represented by non-linear dynamic and static equations, which make the optimization problem challenging.
The second step is to develop an optimization solver which can generate the optimal control signals, e.g. optimal vehicle speed.
Dynamic Programming (DP) is commonly used in automotive systems due to its ease of implementation, its capability of dealing with non-linearities. However, DP results in a computationally expensive algorithm whose complexity increases with the number of states in the optimization problem. Therefore, DP is commonly used for offline benchmarking purposes. A promising research direction consist of implementing an online DP algorithm, which is tailored for a PPC. Such an algorithm should exploit knowledge of the cost function (e.g. convexity) or exploit redundancy of the problem.
- State of the art review for real-time implementable DP and PPC
Optimal control problem formulation:
- Explore cost function trade-offs.
- Explore implementation aspects trade-offs
Optimal control problem implementation:
- Implementation in Matlab Simulink and/or C program
Optimal control problem validation:
- Open-loop and closed-loop testing with simplified vehicle models in Matlab Simulink
You will work together with a team of enthusiastic energy management experts on a very relevant topic in the automotive industry. The results may be used in a large European project.
You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.
Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.