Effects of Internal Resistance on Performance of Batteries for Electric Vehicles

ABSTRACT Effects of Internal Resistance on Performance of Batteries for Electric Vehicles by Rohit A Ugle The University of Wisconsin-Milwaukee, 2013 Under the Supervision of Professor Anoop K. Dhingra An ever increasing acceptance of electric vehicles as passenger cars relies on better operation and control of large battery packs. The individual cells in large battery packs do not have identical characteristics and may degrade differently due to their manufacturing variability and other factors. It is beneficial to evaluate the performance gain by replacing certain battery modules/cells during actual driving. The following are the objectives of our research. We will develop an on-line battery module degradation diagnostic scheme using the intrinsic signals of a battery pack equalization circuit. Therefore, a battery health map can be constructed and updated in real time. Next based on the derived battery health map, the performance of the battery pack will be evaluated a user specified trip so as to evaluate the worthiness of replacing certain modules/cells. Different electric vehicles have different performance for the same driving cycle. These variations are due to variation in driving patterns, traffic, different light patterns, random behavior of the drivers etc. To account for this random behavior of the electric vehicle performance we generate 100 random trip cycles. We aim to model the behavior of the driving cycle and battery behavior. Finally, the thesis also explores the possibility of energy exchange between the battery packs and the smart grid. In the smart grid scenario where we have the knowledge of the electricity price and the load patterns on the grid, it is beneficial for the user to schedule charging and discharging patterns for electric vehicles. Our research will define charging and discharging patterns throughout the life of the battery. We will optimize the charging and discharging times and define the opportunity cost for each day during summer and winter months. The objective is to maximize the profit earned by selling excess energy in the battery to the grid and minimize the charging cost for the electric vehicle

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles

Next generation of transportation in the form of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs generally do not have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It will be cost effective if we evaluate the performance of the battery modules prior to replacing the complete battery pack. The knowledge of the driving cycle and battery internal resistance will help to make decision to replace the worst battery modules and directly cut down on user expenditure to replace the battery. Also, optimizing the performance of battery during the driving trip is the challenging task to achieve. The knowledge of energy prices of the grid, internal resistance of the lithium ion battery pack on the electric vehicle, the age of the battery and distance travelled by the electric vehicle are very important factors on which the cost of daily driving cycle is dependent. In near future, the energy consumed by the electric vehicles will create a major consumer market for the smart grids. The smart grid system is complemented by the renewable energy sources that contribute and support the grid. The electric vehicles are not only predicted as energy consumers but also as dynamic sources of energy. These vehicles can now travel more than 100 miles with a single charging cycle whereas average day to day commute is well below the maximum capacity of these vehicles. This leaves the driver with the extra energy on the battery pack which can be used later for supporting energy requirement from the grid. As we know that cells/modules in large battery packs do not have identical properties and these degrade at different rates during the course of their lifespan. It is beneficial for the user to quantify the amount of energy that can be used to support the grid. The improvement of the electric grid to the next generation infrastructure ie ‘Smart Grid’ will enable diverse opportunities to contribute the energy and balance the load on the grid. The information about the grid like price quality, load etc will be available to the people very easily. This information can be useful to make the energy grid more economical and environment friendly. We have used the information for price of energy on the grid to optimize the cost of daily driving cycle. The goal of this research is to accurately predict the battery behavior for the daily driving cycle. The prediction of battery behavior will help the driver to decide the optimum charging patterns, energy consumed during driving and the surplus energy available in the batteries. The prior knowledge of the battery behavior, price of the energy on the grid and the trip travel will help the driver to minimize the cost of travel on daily basis as well as throughout the life of the battery