Public Funding for Industrial Systems and Control Limited

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.

"Gravitricty Ltd is developing a mechanical technology using gravitational potential for grid-connected electrical energy storage. The scale is currently around 1MW with potential for up to 20MW peak power, and energy storage from 250kWh to 10MWh per cycle. Combination with CAES, using the same vertical shaft as a pressure vessel, could increase the energy stored threefold. The technology has major advantages including rapid response (<1s to full power), high energy efficiency (75-85% round-trip efficiency), very long lifetime (50 Yrs+ for major components) with no cyclic degradation, and locational flexibility. During this 12 month project Gravitricity will work with heavy-lift experts Davy Markham and Deeptek to: A) develop detailed designs for cost reduction in future commercial projects (both in existing mineshafts and in purpose sunk shafts); B) test modular components of the Gravitricity system under gravity and under mock-grid conditions at the Power Networks Demonstration Centre; and C) identify sites and begin environmental and geophysical assessment of sites for our full-scale prototype project, which will be built in 2019 or 2020\."

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