The sum of variances of all PLS components is normally less than 100%.
There are many variants of partial least squares (PLS). What you used here, is PLS regression of a univariate response variable $\mathbf y$ onto several variables $\mathbf X$; this algorithm is traditionally known as PLS1 (as opposed to other variants, see Rosipal & Kramer, 2006, Overview and Recent Advances in Partial
Least Squares for a concise overview). PLS1 was later shown to be equivalent to a more elegant formulation called SIMPLS (see reference to the paywalled Jong 1988 in Rosipal & Kramer). The view provided by SIMPLS helps to understand what is going on in PLS1.
It turns out that what PLS1 does, is to find a sequence of linear projections $\mathbf t_i = \mathbf X \mathbf w_i$, such that:
- Covariance between $\mathbf y$ and $\mathbf t_i$ is maximal;
- All weight vectors have unit length, $\|\mathbf w_i\|=1$;
- Any two PLS components (aka score vectors) $\mathbf t_i$ and $\mathbf t_j$ are uncorrelated.
Note that weight vectors do not have to be (and are not) orthogonal.
This means that if $\mathbf X$ consists of $k=10$ variables and you found $10$ PLS components, then you found a non-orthogonal basis with uncorrelated projections on the basis vectors. One can mathematically prove that in such a situation the sum of variances of all these projections will be less then the total variance of $\mathbf X$. They would be equal if the weight vectors were orthogonal (like e.g. in PCA), but in PLS this is not the case.
I don't know of any textbook or paper that explicitly discusses this issue, but I have earlier explained it in the context of linear discriminant analysis (LDA) that also yields a number of uncorrelated projections on non-orthogonal unit weight vectors, see here: Proportion of explained variance in PCA and LDA.