Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be obtained directly and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate predictors of SOC. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18 h automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

Recent combinations of interactive technology, humans, and water have resulted in “WaterHCI”. WaterHCI design seeks to complement the many benefits of engagement with the aquatic domain, by offering, for example, augmented reality systems for snorkelers, virtual reality in floatation tanks, underwater musical instruments for artists, robotic systems for divers, and wearables for swimmers. We conducted a workshop in which WaterHCI experts articulated the field's grand challenges, aiming to contribute towards a systematic WaterHCI research agenda and ultimately advance the field.

Control-based continuation (CBC) is an experimental method that can reveal stable and unstable dynamics of physical systems. It extends the path-following principles of numerical continuation to experiments and provides systematic dynamical analyses without the need for mathematical modelling. CBC has seen considerable success in studying the bifurcation structure of mechanical systems. Nevertheless, the method is not practical for studying relaxation oscillations. Large numbers of Fourier modes are required to describe them, and the length of the experiment significantly increases when many Fourier modes are used, as the system must be run to convergence many times. Furthermore, relaxation oscillations often arise in autonomous systems, for which an appropriate phase constraint is required. To overcome these challenges, we introduce an adaptive B-spline discretisation that can produce a parsimonious description of responses that would otherwise require many Fourier modes. We couple this to a novel phase constraint that phase-locks control target and solution phase. Results are demonstrated on simulations of a slow-fast synthetic gene network and an Oregonator model. Our methods extend CBC to a much broader range of systems than have been studied so far, opening up a range of novel experimental opportunities on slow-fast systems.

A parameterisation methodology, model content, and open-source parameters are provided for an equivalent circuit model (ECM) of the Molicel P45B, a recent (2022) 4.5 Ah high-power lithium-ion cell with 21700 cylindrical form factor. The ECM comprises a voltage source, a series resistance, 2 parallel resistor-capacitor (RC) elements in series, and additional features (reversible heat source, open-circuit voltage hysteresis) required to accurately represent the behaviour of a lithium-ion cell with a nickel-based positive electrode and mixed graphite-SiOx negative electrode. A combined parameterisation and validation methodology is presented, which considers holistic model performance, and moves beyond classical characterisation using only discrete pulses from quasi-equilibrated cell conditions. Characterisation utilises conductive surface-controlled temperature conditions for cell testing to decouple thermal e ects from electrical parameters. The validation methodology considers the full performance envelope of the cell for state-of-charge (SOC), operating current, and temperature. Validation cases include realistic drive cycles with regenerative charging as well as continuous current charge-discharge. Practical connection of the electrical prediction to degradation models are discussed, including open-circuit voltage resolution into contributions from the negative and positive electrodes.

Engineering-scale battery systems operate under spatiotemporally varying temperature distributions. Thermal impacts are therefore challenging to account for, due to the lack of any single overall system temperature. Distributed electrothermal models can describe the emergent dynamics of this heterogeneity, and predict its impacts on system-level performance. Nevertheless, these models are too computationally demanding to be used for online management and control. In this work, we introduce a method to capture the behaviours of heterogeneous battery systems, using models that are light enough for online use. We build on work by previous authors, who have noted that a cell with a temperature gradient will show the same impedance as an identical cell at some uniform temperature. We develop a modelling paradigm from this, by proposing simple mathematical and parameterisation procedures that recover the effective cell temperature. We undertake a numerical analysis here, as it allows the effective temperature to be computed in a way which would not be possible from experimental data. An electrical submodel is fitted using standard procedures, from experimental voltage and current data under homogeneous conditions; unusually, the thermal submodel is also fitted using voltage and current data, taken from simulations of heterogeneous cycling conditions. The full model is seen to accurately recover the effective temperature of a cell, despite having never been fitted to any thermal data. Hence, our methods are demonstrated to not be overfitting, but to instead be capturing a fundamental characteristic of battery system operation. Our method has the significant benefit of circumventing the need for complex and expensive thermal experiments, as it only relies on data from simple cell cycling tests. Furthermore, the resulting models are very light-weight compared to their distributed counterparts, whilst still capturing emergent dynamics, making them suitable for enhanced battery management systems. Commercial benefits are driven by the impacts of complex heterogeneous behaviour in large form factor cells, where investigations and experiments are ongoing.