Professor Denise Morrey

The reliable and cost-effective operation of battery packs relies on state of power (SOP) algorithms to estimate the available power of the system. The challenges in developing these algorithms include the nonlinear behavior of batteries under high-power demands and the impact of temperature, state of charge (SOC), stack pressure and previous load history at high C-rates. This study employs analysis of variance (ANOVA) and design of experiments (DOE) to assess the impact of key factors on the power output of lithium-ion LCO pouch cells. The findings demonstrate that the effect of cell-to-cell variation on power output is more pronounced than degradation and random errors of the experiments. Further analysis shows that temperature and state of charge have a significant influence on power availability (𝑝-value

The exponential growth of electric and hybrid vehicles, now numbering close to 6 million on the roads, has highlighted the urgent need to address the environmental impact of their lithium-ion batteries as they approach their end-of-life stages. Repurposing these batteries as second-life batteries (SLBs) for less demanding non-automotive applications is a promising avenue for extending their usefulness and reducing environmental harm. However, the shorter lifespan of SLBs brings them perilously close to their ageing knee, a critical point where further use risks thermal runaway and safety hazards. To mitigate these risks, effective battery management systems must accurately predict the state of health of these batteries. In response to this challenge, this study employs time-series artificial intelligence (AI) models to forecast battery degradation parameters using historical data from their first life cycle. Through rigorous analysis of a lithium-ion NMC cylindrical cell, the study tracks the trends in capacity and internal resistance fade across both the initial and second life stages. Leveraging the insights gained from first-life data, predictive models such as the Holt–Winters method and the nonlinear autoregressive (NAR) neural network are trained to anticipate capacity and internal resistance values during the second life period. These models demonstrate high levels of accuracy, with a maximum error rate of only 2%. Notably, the NAR neural network-based algorithm stands out for its exceptional ability to predict local noise within internal resistance values. These findings hold significant implications for the development of specifically designed battery management systems tailored for second-life batteries.

The increasing number of electric vehicles (EVs) on the roads has led to a rise in the number of batteries reaching the end of their first life. Such batteries, however, still have a capacity of 75–80% remaining, creating an opportunity for a second life in less power-intensive applications. Utilising these second-life batteries (SLBs) requires specific preparation, including grading the batteries based on their State of Health (SoH); repackaging, considering the end-use requirements; and the development of an accurate battery-management system (BMS) based on validated theoretical models. In this paper, we conduct a technical review of mathematical modelling and experimental analyses of SLBs to address existing challenges in BMS development. Our review reveals that most of the recent research focuses on environmental and economic aspects rather than technical challenges. The review suggests the use of equivalent-circuit models with 2RCs and 3RCs, which exhibit good accuracy for estimating the performance of lithium-ion batteries during their second life. Furthermore, electrochemical impedance spectroscopy (EIS) tests provide valuable information about the SLBs’ degradation history and conditions. For addressing calendar-ageing mechanisms, electrochemical models are suggested over empirical models due to their effectiveness and efficiency. Additionally, generating cycle-ageing test profiles based on real application scenarios using synthetic load data is recommended for reliable predictions. Artificial intelligence algorithms show promise in predicting SLB cycle-ageing fading parameters, offering significant time-saving benefits for lab testing. Our study emphasises the importance of focusing on technical challenges to facilitate the effective utilisation of SLBs in stationary applications, such as building energy-storage systems and EV charging stations.

Current research involving applying stack pressure to lithium-pouch cells has shown both performance and lifetime benefits. Fixtures are used to mimic this at the cell level and conventionally prescribe a constant displacement onto the cell. This increases stack pressure, but also causes pressure to vary. Despite this, applying an initial stack pressure improves cell conductivity and cell lifetime (Mussa et al., 2018, Zhou et al., 2020, Müller et al., 2019, Li et al., 2022, and Cannarella and Arnold, 2014). In this work, a fixture was designed that applies constant pressure to the cell independent of displacement. The fixture uses pneumatics to apply a constant stack pressure independent of elastic and plastic swelling. Cells constrained by the constant pressure fixture and a conventional displacement based fixture were evaluated using a Hybrid Pulse Power Characterisation (HPPC) test to measure internal resistance and maximum deliverable power. Multiple stack pressures were applied to investigate the variance in pressure over operational conditions and performance between constant pressure and constant displacement based methods. All tests were further compared to a control case with no applied stack pressure. The constant pressure based method reduced pressure variation during charging and discharging, reduced the discharge impedance and improved discharged power, but did not improve charge performance. Discharge performance benefits from constant pressure could influence pack design to improve vehicle performance.

The service life of Lithium-ion batteries disposed from electric vehicles, with an approximate remaining capacity of 75–80%, can be prolonged with their adoption in less demanding second life applications such as buildings. A photovoltaic energy generation system integrated with a second life battery energy storage device is modelled mathematically to assess the design’s technical characteristics. The reviewed studies in the literature assume, during the modelling process, that the second life battery packs are homogeneous in terms of their initial state of health and do not consider the module-to-module variations associated with the state of health differences. This study, therefore, conducts mathematical modelling of second life battery packs with homogenous and heterogeneous state of health in module level using second-order equivalent circuit model (ECM). The developed second-order ECM is validated against experimental data performed in the lab on 3Ah NCM batteries. The degradation parameters are also investigated using the battery cell’s first life degradation data and exponential triple smoothing (ETS) algorithm. The second-order ECM is integrated with the energy generation system to evaluate and compare the performance of the homogenous and heterogeneous battery packs during the year. Results of this study revealed that in heterogeneous packs, a lower electrical current and higher SOC is observed in modules with lower state of health due to their higher ohmic resistance and lower capacity, compared to the other modules for the specific battery pack configuration used in this study. The methodology presented in this study can be used for mathematical modelling of second life battery packs with heterogenous state of health of cells and modules, the simulation results of which can be employed for obtaining the optimum energy management strategy in battery management systems.

Real-time battery modelling advancements have quickly become required as the adoption of battery electric vehicles (BEVs) has rapidly increased. In this paper an open-source, improved discrete realisation algorithm, implemented in Julia for the creation and simulation of reduced-order, real-time capable physics-based models is presented. This work reduces the Doyle–Fuller–Newman electrochemical model into continuous-form transfer functions and introduces a computationally informed discrete realisation algorithm (CI-DRA) to generate the reduced-order representation. Further improvements in conventional offline model creation are obtained as well as achieving in-vehicle capable model creation for ARM-based computing architectures. Furthermore, a parametric sensitivity analysis of the presented architecture is completed as well as experimental validation of a worldwide harmonised light vehicle test procedure (WLTP) for an LG Chem. M50 21700 parameterisation. A performance comparison to a MATLAB implementation is completed showcasing a mean computational time improvement of 3.51 times for LiiBRA.jl on x86 hardware. Finally, an ARM-based implementation showcases full system model generation within three minutes for potential in-vehicle.

Road vehicles are a large contributor to nitrogen oxides (NOx) pollution. The routine roadside monitoring stations, however, may underrepresent the severity of personal exposure in urban areas because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at a local level. This study deploys a 10-cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at a constant speed may generate momentary roadside concentrations of NOx as high as 1.25 mg/m3, with a 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site.

This paper presents the details of a Computational Fluid Dynamics methodology to accurately model the process of mixture preparation in modern Gasoline Direct Injection engines, with particular emphasis on liquid film as one of the main causes of Particulate Matter formation. The proposed modelling protocol, centred on the Bai-Onera approach of droplets-wall interaction and on multi-component surrogate fuel blend models, is validated against relevant published data and then applied to a modern small-capacity GDI engine, featuring centrally-mounted spray-guided injection system. The work covers a range of part-load, stoichiometric and theoretically-homogeneous operating conditions, for which experimental engine data and engine-out Particle Number measurements were available. The results, based on the parametric variation of start of injection timing and injection pressure, demonstrate how both fuel mal-distribution and liquid film retained at spark timing, may contribute to PN emissions, whilst their relative importance vary depending on operating conditions and engine control strategy. Control of PN emissions and compliance with future, more stringent regulations remain large challenges for the engine industry. Renewed and disruptive approaches, which also consider the sustainability of the sector, appear to be essential. This work, developed using Siemens Simcenter CFD software as part of the Ford-led APC6 DYNAMO project, aims to contribute to the development of a reliable and cost-effective digital toolset, which supports engine development and diagnostics through a more fundamental assessment of engine operation and emissions formation.

An experimental investigation was carried out to investigate Particulate Number (PN) emissions from a modern, small-capacity Gasoline Direct Injection (GDI) engine. The first part of the study focused on improving measurement repeatability using the Cambustion DMS-500 device. Results showed that sampling near the exhaust valve – while dampening the pressure oscillations in the sampling line – can significantly improve the repeatability. It was also found that uncontrolled phenomena such as deposition in the exhaust system from earlier engine operation can undermine the accuracy of measurements taken at tailpipe level. The second part of the work investigated PN emissions from three types of gasoline fuel, Pump-grade, Performance and Reference. Fuel chemical composition was found to have an appreciable impact on PN, but the magnitude of this effect differs in various operating points, being more pronounced at higher engine load. The Reference fuel was found to have the lowest PN emission tendency, conceivably because of its lower aromatics, olefins and heavy hydrocarbons content. A sweep of operating parameters showed that higher injection pressure reduces PN, but the extent of the reduction depends on fuel physical properties such as volatility.

The ability to model tyre dynamics precisely is often one of the most critical elements for realistic vehicle dynamics control and handling investigations. The industry-standard empirical models are able to predict the important tyre forces accurately over a short range of vehicle operating conditions, which is often restricted to the operating conditions experienced during the tyre testing process. In this paper, an alternative and practical method to model Formula SAE tyres has been proposed and studied in a series of possible running scenarios. A simple, analytically solved brush-type tyre model is considered for the physical part with the introduction of a novel approach for defining the contact length formulation that incorporates the influence of inflation pressure, camber angle and velocity, while a set of ordinary differential equations are employed to predict the thermal behaviour of the tyre model, which are mostly based on an already-existing method that has not been experimentally validated before. The resulting tyre models provide realistic and informative behaviour of the tyre, which has the ability to consider the majority of the typical operating conditions experienced on a Formula SAE vehicle. The performance of the proposed tyre models is compared against experimental tyre test data, which shows good agreement and indicates that the tyre models have the ability to give realistic predictions of the tyre forces and thermal behaviour in the case of thermal tyre model. Furthermore, the temperature-dependent tyre model has been incorporated into a two-track model of Oxford Brookes Racing’s Formula SAE vehicle to study the effectiveness of the tyre model during transient handling simulation. The resulting simulations suggest that the proposed tyre model has the ability to represent realistic operating conditions of tyres, and also that tyre temperatures influence the vehicle dynamic behaviour significantly during on-limit scenarios.

This paper takes a longitudinal view of literature to explain the current period as disruptive technology drives an evolutionary adaptation of the construction industry in a historical socio-technological process. The authors argue the way Internet of Things (IoT) solutions are conceived as singularly focused “point solutions” undermine future opportunities. An evolutionary view is overlooked because extant literature describes technology in a particular epoch. An ecosystem perspective needs to influence IT strategy as an emerging “digital layer” transcends a smart city and continues to function long after a traditional construction project completes. We describe innovation as a succession of transformational waves in an evolutionary process that is currently manifesting as “Industry 4.0” and changing expectations for the construction industry. The paper concludes by listing emerging trends and warns existing UK construction companies must understand the transformational process they are in and learn how to adapt with a stronger drive for R&D.

Electric vehicles (EVs) are increasingly regarded as the way forward to deliver a much-needed improvement in the transport sector's sustainability profile, and the UK is embarking on a major transition towards them. While previous studies focused mainly on greenhouse gas (GHG) emissions, this article assesses the extent to which EVs may contribute to reducing the UK's dependence on (mostly imported) non-renewable primary energy. The study combines a life-cycle model of a compact battery electric vehicle (BEV) with a prospective energy analysis of a range of electricity supply alternatives for the vehicle's use phase. The key metric analysed is the non-renewable cumulative energy demand (nr-CED). Results show that, already under current conditions, the nr-CED of a compact BEV in the UK is lower by approximately 34% with respect to that of an otherwise similar internal combustion engine vehicle (ICEV). Such reduction is then expected to improve further under all future scenarios, indicating that a transition to EVs is indeed a recommendable option to reduce the UK's demand for non-renewable energy, especially if this is accompanied by a shift to a more renewable electric grid.

A complete and fully consistent LCA-based comparison of a range of lightweighting options for compact passenger vehicles is presented and discussed, using advanced lightweight materials (Al, Mg and carbon fibre composites), and including all life cycle stages and a number of alternative end-of-life scenarios. Results underline the importance of expanding the analysis beyond the use phase, and point to maximum achievable reductions of environmental impact of approximately 7% in most impact categories. In particular, lightweighting strategies based on the use of aluminium were found to be the most robust and consistent in terms of reducing the environmental impacts (with the notable exception of a relatively high potential toxicity). The benefits of using magnesium instead appear to be less clear-cut, and strongly depend on achieving the complete phase-out of SF6 in the metal production process, as well as the establishment of a separate close-loop recycling scheme. Finally, the use of carbon fibre composites leads to similar environmental benefits to those achieved by using Al, albeit generally at a higher economic cost.

A reduced order model is developed for low frequency, fully coupled, undamped and constantly damped structural acoustic analysis of interior cavities, backed by flexible structural systems. The reduced order model is obtained by applying a Galerkin projection of the coupled system matrices, from a higher dimensional subspace to a lower dimensional subspace, whilst preserving some essential properties of the coupled system. The basis vectors for projection are computed efficiently using the Arnoldi algorithm, which generates an orthogonal basis for the Krylov subspace containing moments of the original higher dimensional system. A simply supported steel plate, backed by a rigid walled cavity is used as a computational test case , and the computational gains and the accuracy obtained via implicit moment matching are compared with the direct method in ANSYS. Further, a reciprocity check is performed on the coupled system by exciting the coupled system using unit structural and acoustic excitations. It is shown that the reduced order modelling technique results in a very significant reduction in simulation time, while maintaining the desired accuracy of the state variables (displacements and pressures) under investigation.

In this work, a reduced order multidisciplinary optimization procedure is developed to enable efficient, low frequency, undamped and damped, fully coupled, structural-acoustic optimization of interior cavities backed by flexible structural systems. This new method does not require the solution of traditional eigen value based problems to reduce computational time during optimization, but are instead based on computation of Arnoldi vectors belonging to the induced Krylov Subspaces. The key idea of constructing such a reduced order model is to remove the uncontrollable, unobservable and weakly controllable, observable parts without affecting the noise transfer function of the coupled system. In a unified approach, the validity of the optimization framework is demonstrated on a constrained composite plate/prism cavity coupled system. For the fully coupled, vibro-acoustic, unconstrained optimization problem, the design variables take the form of stacking sequences of a composite structure enclosing the acoustic cavity. The goal of the optimization is to reduce sound pressure levels at the driver" s ear location. It is shown that by incorporating the reduced order modelling procedure within the optimization framework, a significant reduction in computational time can be obtained, without any loss of accuracy-”when compared to the direct method. The method could prove as a valuable tool to analyze and optimize complex coupled structural-acoustic systems, where, in addition to fast analysis, a fine frequency resolution is often required.

It is shown that the reduced order model decreases the simulation time by at least one order of magnitude, while maintaining the desired accuracy of the state variables under investigation. The method could prove as a valuable tool to analyze complex coupled structural–acoustic systems, and their subsequent optimization or sensitivity analysis, where, in addition to fast analysis, a fine frequency resolution is often required.

Recent developments in measurement techniques enabled researchers to measure ultra-fine particulates of nano-scale range and provided more evidence that the smaller particulates typically emitted from gasoline engines may have more severe impacts on human respiratory system than the bigger particulates from diesel engines. The knowledge of the characteristics of particulates from gasoline engines, especially, the effect of fuel borne additives is sparse. This work presents the findings from a study into the effect of aftermarket additives on nano-scale particulates. Four commercially available fuel borne additives used in gasoline engines mainly by private vehicle owners in the United Kingdom were selected for this study. The combustion and emission performance of the additive fuels were compared against that of commercially available gasoline fuel using a 4-stroke, throttle body injected gasoline engine. The engine-out particulates in the range of 5 to 1000 nm were measured using a fast particle spectrometer along with the in-cylinder pressure trace. The work identified that the total particulate count for certain types of additives are two orders of magnitude greater than that of base fuel at the same engine operating condition. In contrast, other types of additives produce significantly lower levels of particulate when compared with the base fuel especially in the range of 10 nm size.