Read the latest reports and research from OVO's Research Hub. Please find documents, resources and links to reports by OVO's Research team.
Parameter estimation of nonlinear fractional-order systems
A fractional-order system is a dynamical system that can be modelled by a fractional differential equation containing derivatives of non-integer order. Fractional-order systems are useful in studying the anomalous behaviour of dynamical systems; importantly for us, they are extremely useful for modelling the non-linear dynamics of electrochemical energy storage systems. Due to the immense complexity of parameter estimation of such systems in the continuous time domain, fractional nonlinear systems identification hasn’t received a lot of attention in the literature.
In this work, a novel method for estimating the nonlinear model parameters of a fractional order model directly from the measured sampled input-output data by adapting a filter-based approach (a cascade of all-pass filters and a fractional Butterworth filter).
Thermal management plays a critical role in battery operations to improve safety and prolong battery life, especially in high power applications such as electric vehicles. A lumped parameter (LP) battery thermal model (BTM) is usually preferred for real-time thermal management due to its simple structure and ease of implementation. Considering the time-varying model parameters (e.g., the varying convective heat dissipation coefficient under different cooling conditions), an online parameter estimation scheme is needed to improve modelling accuracy. In this work, a new formulation of adaptive LP BTM is proposed. Unlike the conventional LP BTMs that only consider convectional heat transfer, radiative heat transfer is also considered in the pro- posed model to better approximate the physical heat dissipation process, which leads to an improved modelling accuracy. However, the radiative heat transfer introduces nonlinearity to the BTM and poses challenges to online parameter estimation. To tackle this problem, the simplified refined instrumental variable approach is proposed as a real-time parameter estimation method by reformulating the nonlinear model equations to be linear in parameters. Test data from a Li ion battery is also collected and used to verify the resulting accuracy of the proposed BTM and the effectiveness of the proposed online parameter estimation algorithm.
Electrochemical modelling of SEI generated under calendar ageing
Predicting lithium ion cell ageing is often complicated due to the interdependency of ageing mechanisms. Research has highlighted that storage ageing is not linear with time. Capacity loss due to storing the battery at constant temperature can shed more light on parametrising the properties of the Solid Electrolyte Interphase (SEI); the identification of which, using an electrochemical model, is systematically addressed in this work. A new methodology is proposed where any one of the available storage ageing datasets can be used to find the property of the SEI layer. A sensitivity study is performed with different molecular mass and densities which are key parameters in modelling the thickness of the SEI deposit. The conductivity is adjusted to fine tune the rate of capacity fade to match experimental results. A correlation is fitted for the side reaction variation to capture the storage ageing in the 0%–100% SoC range. The methodology presented in this paper can be used to predict the unknown properties of the SEI layer which is difficult to measure experimentally. The simulation and experimental results show that the storage ageing model shows good accuracy for the cases at 50% and 90% and an acceptable agreement at 20% SoC.
The power capability of a lithium ion battery is governed by its resistance, which changes with battery state such as temperature, state of charge, and state of health. Characterizing resistance, therefore, is integral in defining battery operational boundaries, estimating its performance and tracking its state of health. There are many techniques that have been employed for estimating the resistance of a battery, these include: using DC pulse current signals such as pulse power tests or Hybrid Pulse Power Characterization (HPPC) tests; using AC current signals, i.e., electrochemical impedance spectroscopy (EIS) and using pulse-multisine measurements. From existing literature, these techniques are perceived to yield differing values of resistance. In this work, we apply these techniques to 20Ah LiFePO4/C6 pouch cells and use the results to compare the techniques. The results indicate that the computed resistance is strongly dependent on the timescales of the technique employed and that when timescales match, the resistances derived via different techniques align. Furthermore, given that EIS is a perturbative characterisation technique, employing a spectrum of perturbation frequencies, we show that the resistance estimated from any technique can be identified – to a high level of confidence – from EIS by matching their timescales.
Energy from electric cars could power our lives - but only if we improve the system
Power stored in electric cars could be sent back to the grid – thereby supporting the grid and acting as a potential storage for clean energy – but it will only be economically viable if we upgrade the system first. In a new paper in Energy Policy, two scientists show how their seemingly contradictory findings actually point to the same outcome and recommendations: that pumping energy back into the grid using today’s technology can damage car batteries, but with improvements in the system it has the potential to provide valuable clean energy – and improve battery life in the process.
PV + Battery systems in Sub-Saharan Africa, are they viable?
One of the biggest challenges facing us today is finding a sustainable solution to provide clean and affordable energy to the millions of Africans who live without it. Over 600 million people in Sub-Saharan Africa did not have access to electricity in 2015 and while more than 60% of them live in rural areas, the rate of residential rural electrification there is as low as 17%. Solar Home Systems can potentially increase the penetration of electricity access in rural Sub-Saharan Africa. In this paper, the viability of using Solar Home Systems which employ lithium-ion batteries is investigated, particularly considering the degradation of batteries. It is found that, exposed to the hot climates of Sub-Saharan Africa, capacity fade after 5 years of cycling is approximately 20% equating to a battery system replacement cost of approximately USD 50. Although this, in-and-of-itself, is not preventive, the upfront costs of Solar Home Systems, in the region of USD 7k-21k, can act as a deterrent.
Clean energy stored in electric vehicles to power buildings
Stored energy from electric vehicles (EVs) can be used to power large buildings – creating new possibilities for the future of smart, renewable energy - thanks to ground-breaking battery research, from WMG at the University of Warwick.
Dr Kotub Uddin, with colleagues from WMG’s Energy and Electrical Systems group and Jaguar Land Rover, has demonstrated that vehicle-to-grid (V2G) technology can be intelligently utilised to take enough energy from idle EV batteries to be pumped into the grid and power buildings – without damaging the batteries. This new research into the potentials of V2G shows that it could actually improve vehicle battery life by around ten percent over a year. Read full press release.
PV + battery systems in the UK, what are the requirements of techno-economic viability
Rooftop photovoltaic systems integrated with lithium-ion battery storage are a promising route for the decarbonisation of the UK’s power sector. From a consumer perspective, the financial benefits of lower utility costs and the potential of a financial return through providing grid services is a strong incentive to invest in PV-battery systems. Although battery storage is generally considered an effective means for reducing the energy mismatch between photovoltaic supply and building demand, it remains unclear when and under which conditions battery storage can be profitably operated within residential photovoltaic systems. This fact is particularly pertinent when battery degradation is considered within the decision framework. In this work, a commercially available coupled photovoltaic lithium-ion battery system is installed within a mid-sized UK family home. Photovoltaic energy generation and household electricity demand is recorded for more than one year. A comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial Lithium-ion batteries is developed. The battery ageing model is used to estimate the cost of battery degradation associated with cycling the battery according to the power profile logged from the residential property. The results show that control strategy, efficiency of the system and size of the battery impact the economic viability of such systems.
How does external pressure effect the performance of Li-ion cells
In application, lithium-ion pouch-format cells undergo expansion during cycling. To prevent contact loss between battery pack components and delamination and deformation during battery operation, compressive pressure is applied to cells in automotive battery modules/packs by way of rigid cell housing within the modules. In this work, the impact of such compressive pressure on battery degradation is studied. Samples of commercial, 15 Ah LiNiMnCoO2/Graphite electrode pouch-type cells were cycled 1200 times under atmospheric, 5 psi and 15 psi compressive loads. After 1200 cycles, the capacity fade for 0, 5 and 15 psi loads was11.0%, 8.8% and 8.4%, respectively; the corresponding power fade was found to be 7.5%, 39% and 18%, respectively, indicating power fade peaks between 0 and 15psi. This contrasting behaviour is related to the wettability increase and separator creep within the cell after compressive load is applied. The opposing capacity fade and power fade results require consideration from automotive battery engineers at the design stage of modules and packs. In addition to capacity fade and power fade results, the study identified the evolution of compressive pressures over multiple cycles, showing that pressure increases with cycling.
Parameter estimation of the fractional-order Hammerstein-Wiener model
Block-oriented models that consist of various configurations of linear dynamic blocks and non-linear memoryless blocks have traditionally been employed to model non-linear dynamical systems. The simplest examples in this class are cascaded systems with the non- linear block either preceding (Hammerstein model) or following (Wiener model) the linear block. There are several practical applications of the Hammerstein and Wiener formulations, for example, in battery modelling, impedance estimations are enhanced by introducing a Wiener static non-linearity to the ordinary equivalent circuit model. Within this formulation, the non-linearity can be described by fractional-order calculus, which employ fractional derivatives and integrals. Although fractional-order systems were introduced in the 18th century, research in this area has largely been restricted to the integer-order case due to insufficient computational resources. Since the 1980s, as computing technology matured, the necessary tools to implement fractional-order systems for modelling, estimation and control were developed. Fractional-order systems have subsequently found wider applications. This work proposes a direct parameter estimation approach from observed input–output data of a stochastic single-input–single-output fractional-order continuous-time Hammerstein–Wiener model by extending a well known iterative simplified refined instrumental variable method.
Estimation of battery model parameters for on-line BMS applications
The accuracy of identifying the parameters of models describing lithium ion batteries (LIBs) in typical battery management system (BMS) applications is critical to the estimation of key states such as the state of charge (SoC) and state of health (SoH). In applications such as electric vehicles (EVs) where LIBs are subjected to highly demanding cycles of operation and varying environmental conditions leading to non-trivial interactions of ageing stress factors, this identification is more challenging. This paper proposes an algorithm that directly estimates the parameters of a nonlinear battery model from measured input and output data in the continuous time-domain. The simplified refined instrumental variable method is extended to estimate the parameters of a Wiener model where there is no requirement for the nonlinear function to be invertible. To account for nonlinear battery dynamics, in this paper, the typical linear equivalent circuit model (ECM) is enhanced by a block-oriented Wiener configuration where the nonlinear memoryless block following the typical ECM is defined to be a sigmoid static nonlinearity. The nonlinear Weiner model is reformulated in the form of a multi-input, single-output linear model. This linear form allows the parameters of the nonlinear model to be estimated using any linear estimator such as the well-established least squares (LS) algorithm. In this paper, the recursive least square (RLS) method is adopted for online parameter estimation. The approach was validated on experimental data measured from an 18650-type Graphite/Lithium-Nickel-Cobalt-Aluminium-Oxide (C6/LiNiCoAlO2) lithium-ion cell. A comparison between the results obtained by the proposed method and by nonparametric frequency-based approaches for obtaining the model parameters is presented. It is shown that although both approaches give similar estimates, the advantages of the proposed method are (i) the simplicity by which the algorithm can be employed on-line for updating nonlinear equivalent circuit model (NL-ECM) parameters and (ii) the improved convergence efficiency of the on-line estimation.
In freight classification, lithium-ion batteries are classed as dangerous goods and are therefore subject to stringent regulations and guidelines for certification for safe transport. One such guideline is the requirement for batteries to be at a state of charge of 30%. Under such conditions, a significant amount of the battery’s energy is stored; in the event of mismanagement, or indeed an airside incident, this energy can lead to ignition and a fire. In this work, we investigate the effect on the battery of removing 99.1% of the total stored energy. The performance of 8Ah C6/LiFePO4 pouch cells were measured following periods of calendar ageing at low voltages, at and well below the manufacturer’s recommended value. Battery degradation was monitored using impedance spectroscopy and capacity tests; the results show that the cells stored at 2.3V exhibited no change in cell capacity after 90 days; resistance rise was negligible. Energy-dispersive X-ray spectroscopy results indicate that there was no significant copper dissolution. To test the safety of the batteries at low voltages, external short- circuit tests were performed on the cells. While the cells discharged to 2.3V only exhibited a surface temperature rise of 6 °C, cells at higher voltages exhibited sparks, fumes and fire.
The impact of high-frequency current ripple on battery degradation: Autospy and Modelling Simulation
Long term ageing experimental results show that degradation resulting from coupled DC and AC current waveforms lead to additional degradation of lithium-ion batteries above that experienced through pure DC cycling. More profoundly, such experiments show a dependency of battery degradation on the frequency of AC perturbation. This paper addresses the underlying causality of this frequency dependent degradation. Cell autopsy techniques, namely X-ray photoelectron spectroscopy (XPS) of the negative electrode surface film, show growth of surface film components with the superimposition of an AC waveform. XPS results show that high frequency AC perturbations lead to the increased formation of a passivating film. In order to determine the cause of this increased film formation, a heterogeneous electrochemical model for the LiNiCoAlO2/C6 lithium ion battery coupled with governing equations for the electrical double-layer and solid electrolyte interface film growth is developed. Simulation results suggest that the increased growth of surface film is attributed to frequency dependent heat generation. This is due to ion kinetics in the double layer which are governed by the Poisson-Boltzmann equation. Additional thermal and reference cell relaxation experiments are undertaken that further corroborates the conclusion that heat generation within the battery is a function of the AC excitation frequency through resistive dissipation and the entropy of the cell reaction.
Evidence of the impact of high-frequency current ripple on battery degradation
The power electronic subsystems within electric vehicle (EV) powertrains are required to manage both the energy flows within the vehicle and the delivery of torque by the electrical machine. Such systems are known to generate undesired electrical noise on the high voltage bus. High frequency current oscillations, or ripple, if unhindered will enter the vehicle’s battery system. Real-world measurements of the current on the high voltage bus of a series hybrid electric vehicle (HEV) show that significant current perturbations ranging from 10 Hz to in excess of 10 kHz are present. Little is reported within the academic literature about the potential impact on battery system performance and the rate of degradation associated with exposing the battery to coupled direct current (DC) and alternating currents (AC). This paper documents an experimental investigation that studies the long-term impact of current ripple on battery performance degradation. Initial results highlight that both capacity fade and impedance rise progressively increase as the frequency of the superimposed AC current increases. A further conclusion is that the spread of degradation for cells cycled with a coupled AC–DC signal is considerably more than for cells exercised with a traditional DC waveform. The underlying causality for this degradation is not yet under- stood. However, this has important implications for the battery management system (BMS). Increased variations in cell capacity and impedance will cause differential current flows and heat generation within the battery pack that if not properly managed will further reduce battery life and degrade the operation of the vehicle.
Tracking Li-ion battery degradation using electrochemical models
Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations.
The use of multisine signals for Li-ion battery equivalent circuit modelling
The Pulse Power Current (PPC) profile is often the signal of choice for obtaining the parameters of a Lithium-ion (Li-ion) battery Equivalent Circuit Model (ECM). Subsequently, a drive-cycle current profile is used as a validation signal. Such a profile, in contrast to a PPC, is more dynamic in both the amplitude and frequency bandwidth. Modelling errors can occur when using PPC data for parametrisation since the model is optimised over a narrower bandwidth than the validation profile. A signal more representative of a drive-cycle, while maintaining a degree of generality, is needed to reduce such modelling errors.
In this work, a signal design technique defined as a pulse-multisine is presented. This superimposes a signal known as a multisine to a discharge, rest and charge base signal to achieve a profile more dynamic in amplitude and frequency bandwidth, and thus more similar to a drive-cycle. The signal improves modelling accuracy and reduces the experimentation time, per state-of-charge (SoC) and temperature, to several minutes compared to several hours for an PPC experiment. This technique is then used to parametrise an ECM model.
Please click here to link through to the original paper HERE and HERE.
Estimating the remaining range of a battery
Predicting the remaining range of a battery reliably, accurately and simply is imperative for effective power management of electrified vehicles and reducing driver anxiety resulting from perceived low driving range. Techniques for predicting the remaining range of an electric vehicle exist; in the best cases they are scaled by factors that account for expected energy losses due to driving style, environmental conditions and the use of on-board energy consuming devices such as air-conditioning. In this work, experimental results that establish the dependence of remaining electrical energy on the vehicle battery immediate cycling history are presented. A method to estimate the remaining energy given short-term cycling history is presented. This method differs from the traditional state of charge methods typically used in battery management systems by considering energy throughput more directly.
A comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage
Degradation of Lithium-ion batteries is a complex process that is caused by a variety of mechanisms. For simplicity, ageing mechanisms are often grouped into three degradation modes (DMs): conductivity loss (CL), loss of active material (LAM) and loss of lithium inventory (LLI). State of Health (SoH) is typically the parameter used by the Battery Management System (BMS) to quantify battery degradation based on the decrease in capacity and the increase in resistance. However, the definition of SoH within a BMS does not currently include an indication of the underlying DMs causing the degradation. Previous studies have analysed the effects of the DMs using incremental capacity and differential voltage (IC-DV) and electrochemical impedance spectroscopy (EIS). The aim of this study is to compare IC-DV and EIS on the same data set to evaluate if both techniques provide similar insights into the causes of battery degradation. For an experimental case of parallelized cells aged differently, the effects due to LAM and LLI were found to be the most pertinent, outlining that both techniques are correlated. This approach can be further implemented within a BMS to quantify the causes of battery ageing which would support battery lifetime control strategies and future battery designs.
In this work, a novel acausal and reconfigurable battery pack model is presented. The model structure adopted for the battery cell is based on an equivalent circuit representation. The circuit elements are modified to take account of both hysteresis and diffusion limitation. The latter is known to be a nonlinear function of large operating currents or long operating times. It is shown that the integration of a current dependent time constant within the cell model better emulates the solid diffusional dynamics of lithium intercalation into the active material under large electrical loads. The advantages of an acausal modeling approach, when scaling-up individual cell models into a complete battery system are also presented. Particular consideration is given to emulating the impact of cell to cell variations on pack performance. Using statistical analysis of battery tests, cell model parameter variations are characterized and quantified. The cell and scaled-up pack model are parameterized for a number of commercially available cell formats, energy capacities and chemistries. The new models are validated using transient, real-world, electrical data measured from an electric vehicle (EV) operating within an urban environment.