Fit a no intercept binary model in caret

Let's take data :

y <- sample(0:1, 125, T)
x <- data.frame(rnorm(125), rexp(125))

I want to perform cross validation on data above, without intercept. To exclude intercept in linear models in caret we just need to use : tuneGrid = expand.grid(intercept = FALSE). However when applying this to binary models :

library(caret)
train(as.factor(y) ~ .,
           data = cbind(y,x),
           method = "glm",
           family = binomial(link = 'logit'),
           tuneGrid = expand.grid(intercept = FALSE),
           trControl = trainControl(
             method = "cv",
             number = 5
           )
)

I get error :

Error: The tuning parameter grid should have columns parameter

My question is : Do we have any possibility to omit intercept in caret binary models ?