Introduction

The term ‘Energy Management System’ sounds very complex and uninteresting, however, we spend most of our lives surrounded by EMS’s and rely on them every day. It turns out there are many things that fall into the category of being Energy Management Systems.  A common memory that most people have from childhood is learning to ride a bike. A bicycle is a good example of a simple energy management system. The operator puts energy into the system through pedaling, then speeds up. The operator’s mechanical energy is stored as kinetic energy. The operator has the choice to convert that stored energy to thermal energy by applying the brakes or gravitational potential energy by traveling uphill.

In nature, we see energy management systems that are extremely efficient. Animals are able to convert chemical energy stored in food into kinetic energy used to run, electrical energy used in neural communications, or thermal energy to maintain body temperature. Likewise, plants can convert energy from sunlight into useful fuel for growth, survival, and reproduction.

Today, on the frontier of modern engineering, advances in energy storage technologies lead the way for more complex Energy Management Systems. Combustion engine automobiles, for example, which in general, convert chemical energy to mechanical energy, are getting more and more complex. Advances in fuel efficiency have drastically increased their range. Advances in battery technology led to the re-emerging of electric vehicles in the automobile industry. While both combustion motor and electric motor vehicles are complex in their own ways, hybrid vehicles stand tall as the most complex. Hybrid vehicles obtain their intricacy by utilizing many different forms of energy and by minimizing waste. To do this, hybrid vehicles need very complex energy management systems.

Project Overview

The focus of this project was to understand hybrid energy management systems and to design a simulation for a vehicle with large inertial properties. With a large mass and wheel moment of inertia, a monster truck is a good model of a Kinetic Energy storage device (if you ignore the friction of large tire tread and assume no demolition-derby style collisions). Having the ability to store Kinetic Energy allows the system to be more efficient but, makes the Energy Management system more complex. A simulation was designed that incorporated all of the components of a hybrid vehicle: the traditional combustion engine, energy storage system, electric motor, power electronics, and a few other subsystems.

System Architecture & Requirements

The complexity of the Hybrid EMS comes from the fact that large amounts of energy need to be moved relatively quickly. To do this, there needs to be a place to get that energy from. The inertia properties alleviate this requirement. By increasing mass of the vehicle, more energy can be stored kinetically, as speed, which reduces the size of the battery necessary. Consequently, this means the vehicle requires more power when starting from rest. The diagram to the left shows a three phase motor, AKA the electric motor. The 6 switches represent the power electronics subsystem and the DC voltage bus corresponds to battery voltage. This is the architecture of the typical power inverter for hybrid vehicles. This converts DC to AC and vice versa.

Simulation

For simplicity, it was assumed that the vehicle speed was a constant 60 MPH. The simulation looked at a number of subsystem parameters over the course of the terrain, which was an input to the system. The default mode of the system was to utilize battery charge to power the electric motor. If the battery reached a certain depth-of-discharge (DOD), the combustion engine kicked on. The combustion engine’s sole purpose was to spin the rotor of the alternator, which basically made it a generator. The combustion engine would then consume the vehicle’s fuel. A secondary condition was programed to also turn on the combustion engine in the event that the battery discharge rate was higher than a specified value, indicating going up a steep hill. When the combustion engine either charged the batteries to an acceptable DOD or ran out of fuel, it would turn off. The terrain was an input that affected the electric motor directly as a normalized quasi-torque signal. Increased load on the electric motor drew more current from the Power Inverter, and ultimately the battery. Similarly, negative load on the motor supplied the Power Electronics with current, therefore recharging the battery. The other direct input to the electric motor was speed, in RPM. As previously indicated it was set to approximately 60 MPH, to reduce complexity. Lastly, the temperature control system held the battery temperature at a constant cool value to ensure constant DOD and efficiency.

Results

Below are three different simulations run for different terrains. The first corresponds to a terrain where the truck is driving on flat surface for 30 time units, at which point it begins climbing a hill. At 45 time units it becomes flat again. And finally, at 60 time units, the truck begins to go down hill. At this point, the electric motor enters regeneration mode. In the Battery Capacity plot, it can be observed that the battery is being recharged during this period. It can be observed in the Fuel Capacity plot that due to the hill, the combustion engine turns on and begins consuming fuel. During this period the alternator provides AC current to the Power Inverter (PE), which is depicted in the AC Current From D_Motor to PE plot. The two other simulations show how the truck responds to different terrains.

Ultimately, the simulation demonstrated the behavior of each subsystem within a hybrid vehicle sufficiently, although there were some discrepancies and unacceptable assumptions. First and foremost, the simulation did not consider wheel size. In fact, the wheel subsystem was left out entirely for the sake of time. Secondly, the addition of normalized signals complicated some conversions within the power electronics subsystem causing the DC current values to be larger than realistic. However, the primary goal was to model behavior, which was achieved. The current going from battery to PE resembles DC and the current going from PE to battery resembles rectified AC, which is why the DC Current from PE to ESS appears to have ripples. Lastly, the AC current being delivered to the electric motor is constant, which makes sense because a constant speed was set. However, in reality there would be other aspects affecting the speed of the truck, such as slope, that were not implemented. Essentially, the programming of the truck knew when the electric motor was in motor mode vs. generator mode and the plot below of three phase current represents the magnitude only.

Project Achievements

Ultimately, this project was a huge success. My partner, Nicole Vitale, and I achieved our goal of understanding complex hybrid energy management systems. While we were slightly disappointed to have to simplify aspects of the simulation due to time, we were satisfied with the outcome. In the end, the power electronics was the most complicated subsystem with the combustion engine as the runner-up. The power inverter is extremely complex due to its bi-directional nature. It speaks both the AC and DC languages and needs to be able to interpret them almost instantaneously. That being said, we bit off more than we could chew and made it happen. The simulation was successful in modeling the behavior of hybrid energy management systems.