Abstract

The battery in an EV is designed according to a power to energy ratio and is a trade-off in the design of the pack. It also suffers from effects such as rate capacity effect, ripple effects and inefficiency under charging. These effects result in losses through which the capacity and life span of the batteries are compromised affecting range and drivability.
In this thesis a novel development path resulting in a novel Power and Energy Management Strategy (PEMS) is presented. The effects of (dis)charging a battery are researched and converted to an energy optimisation formula and result in reduced power demand for the converter which reduces weight. The resulting Power Management Strategy (PMS) aims to recover energy more efficiently into UC while responding fast to a change in demand.
The effects of converters on the battery current ripple are researched and discussed, resulting in an optimal topology layout, improved battery life and reduced losses. Through the use of Markov Chain analysis and a newly derived Bias function a predictive Energy Management Strategy (EMS) is developed which is practical to use in EVs.
This resulted in a PEMS which because of the fast PMS results in a fast response time. The use of Markov Chain results in predictive EMS and improves the efficiency of the energy sources and allows the design to be reduced in size.
Through the design methodology used the parallel topology (the battery converter parallel to the UC Module) was rated preferred choice over battery only and battery with UC Module. The rating was based on capacity, ripple control, weight, 10 year cost, potential for motor controller efficiency improvement, range and efficiency.
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The combination of method and PEMS resulted in an improved life expectancy of the pack to over 10 year (up from 7) while increasing range and without sacrificing drivability.