Accelerating the Adoption and Benefits of Model-based Control in PEMD Applications

Control algorithms are at the core of the software that operates many systems, whether it is a washing machine or an EV car. Most applications have relative basic control and will not have much benefit from anything sophisticated. However, for more complex, multi-faceted systems, like an EV, a more advanced control algorithm can offer significant benefits, such as greater range or longer life components simply by having control actions that are optimised to the system. For example, the life of an EV battery is greatly influenced by how rapidly it is charged or discharged during driving, and a sophisticated control algorithm can actively minimise those cycles and ultimately extend battery life. Equally EV range can be maximised by an algorithm that minimises braking and accelerating as a car travels through traffic, junctions or undulating roads. There are many such situations where some aspect of operation can be improved by enhanced control.

An optimal control algorithm provides the "best" control action based on some defined performance metrics, and it does this using an internal math model to represent the system behaviour and an optimisation routine. These are often referred to as model-based controllers due to the reliance on the internal model.

Such advanced control strategies have been used widely in oil-refineries where the slow nature of the system allowed the complex algorithms plenty of time for their calculations. Such model-based control is now feasible within the software running on ever powerful microprocessors found in cars and other standalone machines. However, such control algorithms do require a significant engineering capability to design and deploy, and so there is a sizable barrier to adoption for all but the biggest of companies with large R&D teams.

The aim of this work is to develop application focussed training materials that remove these barriers to adoption for UK PEMD companies to allow them to understand and deploy sophisticated model-based control methods and provide a competitive edge for their products.