• Energy Research

The empirical success of the Artificial Intelligence (AI), has enhanced importance of the transparency in black box Machine Learning (ML) models. This study pioneers in developing an explainable and interpretable Deep Neural Network (DNN) model for a Guideless Irregular Dew Point Cooler (GIDPC). The game theory based SHapley Additive exPlanations (SHAP) method is used to interpret contribution of the operating conditions on performance parameters. Furthermore, in a response to the endeavours in developing more efficient metaheuristic optimisation algorithms for the energy systems, two Evolutionary Optimisation (EO) algorithms including a novel bio-inspired algorithm i.e., Slime Mould Algorithm (SMA), and Particle Swarm Optimization (PSO), are employed to simultaneously maximise the cooling efficiency and minimise the construction cost of the GIDPC. Additionally, performance of the optimised GIDPCs are compared in both statistical and deterministic way. The comparisons are carried out in diverse climates in 2020 and 2050 in which the hourly future weather data are projected using a high-emission scenario defined by Intergovernmental Panel for Climate Change (IPCC). The results revealed that the hourly COP of the optimised systems outperform the base design. Although power consumption of all systems increases from 2020 to 2050, owing to more operating hours as a result of global warming, but power savings of up to 72%, 69.49%, 63.24%, and 69.21% in hot summer continental, Arid, tropical rainforest and Mediterranean hot summer climates respectively, can be achieved when the systems run optimally.

This study is pioneered in developing digital twins using Feed-forward Neural Network (FFNN) and multi objective evolutionary optimization (MOEO) using Genetic Algorithm (GA) for a counter-flow Dew Point Cooler with a novel Guideless Irregular Heat and Mass Exchanger (GIDPC). The digital twins, takes the intake air characteristics, i.e., temperature, relative humidity as well as main operating and design parameters, i.e., intake air velocity, working air fraction, height of HMX, channel gap, and number of layers as the inputs. GIDPC’s cooling capacity, coefficient of performance (COP), dew point efficiency, wet-bulb efficiency, supply air temperature and surface area of the layers are selected as outputs. The optimum values of aforementioned operating and design parameters are identified by the MOEO to maximise the cooling capacity, COP, wet-bulb efficiencies and to minimise the surface area of the layers in four identified climates within Koppen-Geiger climate classification, namely: tropical rainforest, arid, Mediterranean hot summer and hot summer continental climates. The system monthly and annual performances in the identified optimum conditions are compared with the base system and the results show the annual improvements of up to 72.75% in COP and 23.57% in surface area. In addition, the annual power consumption is reduced by up to 49.41% when the system is designed and operated optimally. It is concluded that identifying the optimum conditions for the GIDPC can increase the system performance substantially.

Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community.