Circuit Synthesis of Electrochemical Supercapacitor Models

This paper is concerned with the synthesis of RC electrical circuits from physics-based supercapacitor models describing conservation and diffusion relationships. The proposed synthesis procedure uses model discretisation, linearisation, balanced model order reduction and passive network synthesis to form the circuits. Circuits with different topologies are synthesized from several physical models. This work will give greater understanding to the physical interpretation of electrical circuits and will enable the development of more generalised circuits, since the synthesized impedance functions are generated by considering the physics, not from experimental fitting which may ignore certain dynamics

Reduced-Order Neural Network Synthesis with Robustness Guarantees

In the wake of the explosive growth in smartphones and cyberphysical systems, there has been an accelerating shift in how data is generated away from centralised data towards on-device generated data. In response, machine learning algorithms are being adapted to run locally on board, potentially hardware limited, devices to improve user privacy, reduce latency and be more energy efficient. However, our understanding of how these device orientated algorithms behave and should be trained is still fairly limited. To address this issue, a method to automatically synthesize reduced-order neural networks (having fewer neurons) approximating the input/output mapping of a larger one is introduced. The reduced-order neural network's weights and biases are generated from a convex semi-definite programme that minimises the worst-case approximation error with respect to the larger network. Worst-case bounds for this approximation error are obtained and the approach can be applied to a wide variety of neural networks architectures. What differentiates the proposed approach to existing methods for generating small neural networks, e.g. pruning, is the inclusion of the worst-case approximation error directly within the training cost function, which should add robustness. Numerical examples highlight the potential of the proposed approach. The overriding goal of this paper is to generalise recent results in the robustness analysis of neural networks to a robust synthesis problem for their weights and biases

Tuning the feedback controller gains is a simple way to improve autonomous driving performance

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle's closed loop dynamics. The aim of this paper is to highlight the limitations of this approach; it is shown that re-tuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness

Micro-scale graded electrodes for improved dynamic and cycling performance of Li-ion batteries

Li-ion battery cathodes based on LiFePO4 are fabricated by a layer-by-layer spray printing method with a continuous through thickness gradient of active material, conductive carbon, and binder. Compared with cathodes with the more usual homogeneous distribution, but with the same average composition, both C-rate and capacity degradation performance of the graded electrodes are significantly improved. For example at 2C, graded cathodes with an optimized material distribution have 15% and 31% higher discharge capacities than sprayed uniform or conventional slurry cast uniform cathodes, and capacity degradation rates are 40–50% slower than uniform cathodes at 2C. The improved performance of graded electrodes is shown to derive from a lower charge transfer resistance and reduced polarization at high C-rates, which suggests a more spatially homogeneous distribution of over-potential that leads to a thinner solid electrolyte interphase formation during cycling and sustains improved C-rate and long-term cycling performance

A low-cost way to reduce greenhouse effects

Oak wood precursor was used for preparing low-cost CO2 sorbents. Adsorption is proposed as a cheaper alternative to chemical absorption, which is uneconomical, thus reducing the cost associated with the capture step. The raw material has been carbonised either by pyrolysis or by a hydrothermal carbonisation (HTC). Resulting biochars were then activated using CO2 . Initial chars and their activated counterparts were characterised by SEM imaging and N2 sorption measurements at 77 K. A significant rise in the BET surface area, total pore volume and micropore volume (textural parameters) occurred for all of the pristine chars after the activation process. Fast CO2 sorption kinetics (saturation reached in 3 mins.) and CO2 uptakes of up to ca. 6 wt. % have been measured by thermogravimetric analysis (TGA) at 35 ºC and 1 atm. The activated carbons (ACs) thus synthesised showed competitive performances compared to a commercial AC standard. Although the sorbents’ performances decreased at higher temperatures, they were easily regenerated after the capture stage

A multilayer Doyle-Fuller-Newman model to optimise the rate performance of bilayer cathodes in Li ion batteries

Bilayer cathodes comprising two active materials are explored for their ability to improve lithium-ion battery charging performance. Electrodes are manufactured with various arrangements of lithium nickel manganese cobalt oxide Li[Ni0.6Co0.2Mn0.2]O2 (NMC622) and lithium iron phosphate LiFePO4 (LFP) active particles, including in two different discrete sub-layers. We present experimental data on the sensitivity of the electrode C rate performance to the electrode design. To understand the complex bilayer electrode performance, and to identify an optimal design for fast charging, we develop an extension to the Doyle-Fuller-Newman (DFN) model of electrode dynamics that accommodates different active materials in any number of sub-layers, termed the multilayer DFN (M-DFN) model. The M-DFN model is validated against experimental data and then used to explain the performance differences between the electrode arrangements. We show how the different open circuit potential functions of NMC and LFP can be exploited synergistically through electrode design. Manipulating the Li electrolyte concentration increases achievable capacity. Finally the M-DFN model is used to further optimize the best performing bilayer electrode arrangement by adjusting the ratio of the LFP and NMC sub-layer thickness

Constrained optimal control of monotone systems with applications to battery fast-charging

Enabling fast charging for lithium ion batteries is critical to accelerating the green energy transition. As such, there has been significant interest in tailored fast-charging protocols computed from the solutions of constrained optimal control problems. Here, we derive necessity conditions for a fast charging protocol based upon monotone control systems theory