Update paper.md

minor tweaks up to the end of 'statement of need'

joss/paper.md

@@ -47,15 +47,15 @@ repository: https://github.com/pybop-team/PyBOP
 
 The Python Battery Optimisation and Parameterisation (`PyBOP`) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users.
 
-`PyBOP` enables parameter identification from data for various battery models, including both the electrochemical and equivalent circuit models provided by the popular open-source `PyBaMM` package [@Sulzer:2021]. Using the same workflow, `PyBOP` can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. `PyBOP` facilitates optimisation using a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line control, and design optimisation to accelerate research and development and improve battery utilisation.
+`PyBOP` enables parameter identification from data for various battery models, including the electrochemical and equivalent circuit models provided by the popular open-source `PyBaMM` package [@Sulzer:2021]. Using the same approaches, `PyBOP` can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. `PyBOP` facilitates optimisation with a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line estimation and control, and design optimisation, accelerating battery research and development.
 
 # Statement of need
 
-`PyBOP` is a Python package that provides a user-friendly, object-oriented interface for optimising battery model parameters. `PyBOP` leverages the open-source `PyBaMM` package [@Sulzer:2021] to formulate and solve battery models. Together, these packages serve a broad audience including students, engineers, and researchers in academia and industry, enabling the use of advanced models where previously this was not possible without specialised knowledge of battery modelling, parameter inference and software development. `PyBOP` emphasises clear and informative diagnostics and workflows to support users with varying levels of domain expertise, and provides access to a wide range of optimisation and sampling algorithms. These methods are provided through interfaces to `PINTS` [@Clerx:2019], `SciPy` [@SciPy:2020], and `PyBOP`'s own implementations of algorithms such as adaptive moment estimation with weight decay (AdamW), gradient descent, and Cuckoo search.
+`PyBOP` is a Python package that provides a user-friendly, object-oriented interface for optimising battery model parameters. `PyBOP` leverages the open-source `PyBaMM` package [@Sulzer:2021] to formulate and solve battery models. Together, these software tools serve a broad audience including students, engineers, and researchers in academia and industry, enabling the use of advanced models where previously this was not possible without specialised knowledge of battery modelling, parameter inference, and software development. `PyBOP` emphasises clear and informative diagnostics and workflows to support users with varying levels of domain expertise, and provides access to a wide range of optimisation and sampling algorithms. These are provided through interfaces to `PINTS` [@Clerx:2019], `SciPy` [@SciPy:2020], and `PyBOP`'s own implementations of algorithms such as adaptive moment estimation with weight decay (AdamW), gradient descent, and cuckoo search.
 
-`PyBOP` supports the battery parameter exchange (BPX) standard [@BPX:2023] for sharing battery parameter sets. These parameter sets are typically costly to obtain due to the specialised equipment and time required for characterisation experiments, the need for battery domain knowledge, and the computational cost of parameter estimation. `PyBOP` reduces the requirements for the latter two categories by providing fast parameter estimation, standardised workflows, and parameter set interoperability (via BPX).
+`PyBOP` supports the battery parameter exchange (BPX) standard [@BPX:2023] for sharing battery parameter sets. These parameter sets are typically costly to obtain due to the specialised equipment and time required for characterisation experiments, the need for domain knowledge, and the computational cost of estimation. `PyBOP` reduces the requirements for the latter two by providing fast parameter estimation methods, standardised workflows, and parameter set interoperability (via BPX).
 
-This package complements other lithium-ion battery modelling packages built around `PyBaMM`, such as `liionpack` for battery pack simulation [@Tranter2022] and `pybamm-eis` for fast numerical computation of the electrochemical impedance for any battery model, since the identified parameters from `PyBOP` are easily exportable to these other packages.
+`PyBOP` complements other lithium-ion battery modelling packages built around `PyBaMM`, such as `liionpack` for battery pack simulation [@Tranter2022] and `pybamm-eis` for fast numerical computation of the electrochemical impedance of any battery model. The identified parameters from `PyBOP` are easily exportable to these other packages.
 
 # Architecture