Traditional robots have rigid underlying structures that limit their ability to interact with their environment. For example, conventional robot manipulators have rigid links and can manipulate objects using only their specialised end effectors. These robots often encounter difficulties operating in unstructured and highly congested environments. A variety of animals and plants exhibit complex movement with soft structures devoid of rigid components. Muscular hydrostats (e.g. octopus arms and elephant trunks) are almost entirely composed of muscle and connective tissue and plant cells can change shape when pressurised by osmosis. Researchers have been inspired by biology to design and build soft robots. With a soft structure and redundant degrees of freedom, these robots can be used for delicate tasks in cluttered and/or unstructured environments. This paper discusses the novel capabilities of soft robots, describes examples from nature that provide biological inspiration, surveys the state of the art and outlines existing challenges in soft robot design, modelling, fabrication and control.

Sustainability of reusing Li-ion batteries is hindered by high nickel content and is susceptible to use and recycling choices.

Metals use a chemotaxis-like process to overcome diffusion limitations and prevent dendritic electrodeposition in batteries.

Low-order, explicit models of lithium ion cells are critical for real-time battery management system (BMS) applications. This paper presents a seventh-order, electrolyte enhanced single particle model (ESPM) with electrolyte diffusion and temperature dependent parameters (ESPM-T). The impedance transfer function coefficients are explicit in terms of the model parameters, simplifying the implementation of temperature dependence. The ESPM-T model is compared with a commercially available finite volume based model and results show accurate matching of pulse responses over a wide range of temperature (T) and C-rates (I). The voltage response to 30 s pulse charge–discharge current inputs is within 5% of the commercial code for $25 °C<T<50 °C$ at $I≤12.5C$ and $-10 °C<T<50°C$ at $I≤1C$ for a graphite/nickel cobalt manganese (NCM) lithium ion cell.

A model order reduction method is developed and applied to 1D diffusion systems with negative real eigenvalues. Spatially distributed residues are found either analytically (from a transcendental transfer function) or numerically (from a finite element or finite difference state space model), and residues with similar eigenvalues are grouped together to reduce the model order. Two examples are presented from a model of a lithium ion electrochemical cell. Reduced order grouped models are compared to full order models and models of the same order in which optimal eigenvalues and residues are found numerically. The grouped models give near-optimal performance with roughly $1∕20$ the computation time of the full order models and require 1000–5000 times less CPU time for numerical identification compared to the optimization procedure.

An innovative concept for a multifunctional structural battery using lithium-ion battery materials as load bearing elements in a sandwich panel construction has been demonstrated. The structural battery prototype has exhibited an initial capacity of 17.85 Ah, an energy density of 248 Wh L⁻¹, a specific energy of 102 Wh kg⁻¹, and a capacity retention of 85.8% after 190 charge–discharge cycles at ~ C/3 rate and eight mechanical loading cycles (upto 1060 N). The mechanical stiffness in three-point bend tests follows expectations based on sandwich beam theory, proving that the battery materials are sharing in the load-carrying function of the sandwich panel. While areas for improvement of the fabrication and performance of the prototype still exist, the results of the current investigation demonstrate the promising potential of the proposed structural battery concept for the efficient use of space and mass in an electric vehicle.