S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Electrical and Computer Engineering (전기·정보공학부) Theses (Ph.D. / Sc.D._전기·정보공학부)
Application of Convex Optimization Techniques for Intelligent Electric Vehicles : 지능형 전기자동차를 위한 최적화 기법의 적용
- 공과대학 전기·컴퓨터공학부
- Issue Date
- 서울대학교 대학원
- Optimal power control ; Battery/Super-capacitor ; Electric vehicle ; Intelligent vehicle ; Multi target tracking ; Probability Hypothesis Density filter
- 학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 서승우.
- Batteries are often damaged by a high peak power and a steep variation of
the power since it has a relatively low power density. In order to reduce battery
damage, the battery/super-capacitor (SC) hybrid energy storage system (HESS)
has been utilized since the SC can act as a buffer against large magnitudes and
rapid fluctuations in power. The major objective regarding the battery/SC HESS
is to minimize the magnitude/variation of the battery power and the power loss.
To achieve the objective, I formulate optimization problems to provide the optimal
HESS power using given load operation profiles. In addition, I propose an algorithm
using a barrier method and a Multiplicative Increase Additive Decrease method for
providing a feasible optimal solution for energy management in HESS.
The battery/SC HESS can be effectively utilized for Electric Vehicles (EVs)
because high peak power or rapid charging/discharging occur frequently in driving
situations. However, the optimization method proposed in the second chapter cannot
be adopted for EVs because it is difficult to obtain the future driving profile in
advance. To calculate the optimal power of the battery/SC without the future
profiles, I propose a method for computing the reference voltage of the SC based on
the characteristic of power-train and the vehicle dynamics. In addition, I formulate
the real-time optimization problem that minimizes the magnitude/variation of the
battery power and the power loss simultaneously.
To improve the power control for the battery/SC HESS in EVs, it is necessary
to know the future motor power in advance. They can be derived from the future
speed/acceleration of the vehicle through the method proposed in the third chapter
if the future speed/acceleration can be predicted. Fortunately, there are many
prediction techniques such as car following models, path planning algorithms and
model predictive schemes, which are based on results of target tracking. Therefore,
the driving environments, e.g., moving objects, should be accurately estimated.
To improve the multi-target estimation accuracy even if there are many false
detections, I propose a robust multi-target tracking scheme based on the GMPHD
filter. The proposed scheme includes the processing step of evaluating multiple
states/measurements which is designed to overcome the weight under/overestimation
problem. Furthermore, it includes the step of generating the birth intensity
for the next iteration using the measurements not associated with any tracked
states. I also show that the proposed method can be extended to nonlinear Gaussian