TY - JOUR
T1 - Radio alignment for inductive charging of electric vehicles
AU - Ni, Wei
AU - Collings, Iain B.
AU - Wang, Xin
AU - Liu, Ren Ping
AU - Kajan, Alija
AU - Hedley, Mark
AU - Abolhasan, Mehran
PY - 2015/4/27
Y1 - 2015/4/27
N2 - To maximize power transfer for inductively charging electric vehicles (EVs), charger and battery coils must be aligned. Wireless sensors can be installed to estimate misalignments; however, existing ranging techniques cannot satisfy the precision requirements of the misalignment estimation. We propose a high-precision wireless ranging and misalignment estimation scheme, where high precision is achieved by iteratively measuring, estimating, and aligning the coils. Another key aspect is to convert the nonconvex misalignment estimation to a more tractable problem with a convex objective. We develop a conditional gradient descent method to solve the problem, which performs gradient descent (or conditional gradient descent on the boundary of the search space) and projects out-of-boundary points back into the space. Employing experimentally validated models, we show that our scheme can achieve 92% of the efficiency of perfectly aligned coils in 90% of operations, and tolerate correlated distance measurement errors. In contrast, the prior art is susceptible to correlation, undergoing a significant efficiency degradation of 18.5%.
AB - To maximize power transfer for inductively charging electric vehicles (EVs), charger and battery coils must be aligned. Wireless sensors can be installed to estimate misalignments; however, existing ranging techniques cannot satisfy the precision requirements of the misalignment estimation. We propose a high-precision wireless ranging and misalignment estimation scheme, where high precision is achieved by iteratively measuring, estimating, and aligning the coils. Another key aspect is to convert the nonconvex misalignment estimation to a more tractable problem with a convex objective. We develop a conditional gradient descent method to solve the problem, which performs gradient descent (or conditional gradient descent on the boundary of the search space) and projects out-of-boundary points back into the space. Employing experimentally validated models, we show that our scheme can achieve 92% of the efficiency of perfectly aligned coils in 90% of operations, and tolerate correlated distance measurement errors. In contrast, the prior art is susceptible to correlation, undergoing a significant efficiency degradation of 18.5%.
UR - http://www.scopus.com/inward/record.url?scp=84926452867&partnerID=8YFLogxK
U2 - 10.1109/TII.2015.2400925
DO - 10.1109/TII.2015.2400925
M3 - Article
AN - SCOPUS:84926452867
SN - 1551-3203
VL - 11
SP - 427
EP - 440
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 2
M1 - 7035092
ER -