Does batch normalization mean that sigmoids work better than ReLUs?
See, the basic concept behind the batch-normalization is that (excerpt from a Medium article)- 


  We normalize our input layer by adjusting and scaling the activations. For example, when we have features from 0 to 1 and some from 1 to 1000, we should normalize them to speed up learning. If the input layer is benefiting from it, why not do the same thing also for the values in the hidden layers, that are changing all the time, and get 10 times or more improvement in the training speed.


Read the article here.

This is the reason why we use Batch-normalization. Now coming to your question, see as the output of sigmoid is constraints between 0 and 1, and that what is the motto of Batch-normalization.
If we use Bach-normalization with sigmoid activation, then it will be constrained between sigmoid(0) to sigmoid(1), that is between 0.5 to 0.73 ~ $frac{1}/{(1+1/e)}$.
But if we use ReLU with Batch-normalization then the output will be spread over 0 to 1 which is the good thing for us as finally, we want the output as much diverse as it can be.
So I think ReLU will be the best choice among other activations.