Effects of cycling on lithium-ion battery hysteresis and overvoltage

Currently, lithium-ion batteries are widely used as energy storage systems for mobile applications. However, a better understanding of their nature is still required to improve battery management systems (BMS). Overvoltages and open-circuit voltage (OCV) hysteresis provide valuable information regarding battery performance, but estimations of these parameters are generally inaccurate, leading to errors in BMS. Studies on hysteresis are commonly avoided because the hysteresis depends on the state of charge and degradation level and requires time-consuming measurements. We have investigated hysteresis and overvoltages in Li(NiMnCo)O2/graphite and LiFePO4/graphite commercial cells. Here we report a direct relationship between an increase in OCV hysteresis and an increase in charge overvoltage when the cells are degraded by cycling. We fnd that the hysteresis is related to difusion and increases with the formation of pure phases, being primarily related to the graphite electrode. These fndings indicate that the graphite electrode is a determining factor for cell efciency.Peer ReviewedPostprint (published version

Electric vehicle battery performance investigation based on real world current harmonics

Electric vehicle (EV) powertrains consist of power electronic components as well as electric machines to manage the energy flow between different powertrain subsystems and to deliver the necessary torque and power requirements at the wheels. These power subsystems can generate undesired electrical harmonics on the direct current (DC) bus of the powertrain. This may lead to the on-board battery being subjected to DC current superposed with undesirable high- and low- frequency current oscillations, known as ripples. From real-world measurements, significant current harmonics perturbations within the range of 50 Hz to 4 kHz have been observed on the high voltage DC bus of the EV. In the limited literature, investigations into the impact of these harmonics on the degradation of battery systems have been conducted. In these studies, the battery systems were supplied by superposed current signals i.e., DC superposed by a single frequency alternating current (AC). None of these studies considered applying the entire spectrum of the ripple current measured in the real-world scenario, which is focused on in this research. The preliminary results indicate that there is no difference concerning capacity fade or impedance rise between the cells subjected to just DC current and those subjected additionally to a superposed AC ripple current

Batteries and Supercapacitors Aging

Electrochemical energy storage is a key element of systems in a wide range of sectors, such as electro-mobility, portable devices, and renewable energy. The energy storage systems (ESSs) considered here are batteries, supercapacitors, and hybrid components such as lithium-ion capacitors. The durability of ESSs determines the total cost of ownership, the global impacts (lifecycle) on a large portion of these applications and, thus, their viability. Understanding ESS aging is a key to optimizing their design and usability in terms of their intended applications. Knowledge of ESS aging is also essential to improve their dependability (reliability, availability, maintainability, and safety). This Special Issue includes 12 research papers and 1 review article focusing on battery, supercapacitor, and hybrid capacitor aging

Lithium-ion battery data and where to find it

Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. In this work, the datasets associated with lithium batteries in the public domain are summarised. We review the data by mode of experimental testing, giving particular attention to test variables and data provided. Alongside highlighted tools and platforms, over 30 datasets are reviewed

Li-ion batteries monitoring for electrified vehicles applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Lifetime Prediction and Simulation Models of Different Energy Storage Devices

Energy storage is one of the most important enablers for the transformation to a sustainable energy supply with greater mobility. For vehicles, but also for many stationary applications, the batteries used for energy storage are very flexible but also have a rather limited lifetime compared to other storage principles. This Special Issue is a collection of articles that collectively address the following questions: What are the factors influencing the aging of different energy storage technologies? How can we extend the lifetime of storage systems? How can the aging of an energy storage be detected and predicted? When do we have to exchange the storage device? The articles cover lithium-ion batteries, supercaps, and flywheels

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic

On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by a flexible integrated vehicle and smart-grid system

Renewable energies are a key pillar of power sector decarbonisation. Due to the variability and uncertainty they add however, there is an increased need for energy storage. This adds additional infrastructure costs to a degree that is unviable: for an optimal case of 15GW of storage by 2030, the cost of storage is circa: £1000/kW. A promising solution to this problem is to use the batteries contained within electric vehicles (EVs) equipped with bi-directional charging systems to facilitate ancillary services such as frequency regulation and load balancing through vehicle to grid (V2G) technologies. Some authors have however dismissed V2G as economically unviable claiming the cost of battery degradation is larger than arbitrage. To thoroughly address the viability of V2G technologies, in this work we develop a comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial C6/LiNiCoAlO2 batteries. The comprehensive model accounts for all established modes of degradation including calendar age, capacity throughput, temperature, state of charge, depth of discharge and current rate. The model is validated using six operationally diverse real-world usage cycles and shows an average maximum transient error of 4.6% in capacity loss estimates and 5.1% in resistance rise estimates for over a year of cycling. This validated, comprehensive battery ageing model has been integrated into a smart grid algorithm that is designed to minimise battery degradation. We show that an EV connected to this smart-grid system can accommodate the demand of the power network with an increased share of clean renewable energy, but more profoundly that the smart grid is able to extend the life of the EV battery beyond the case in which there is no V2G. Extensive simulation results indicate that if a daily drive cycle consumes between 21% and 38% state of charge, then discharging 40% to 8% of the batteries state of charge to the grid can reduce capacity fade by approximately 6% and power fade by 3% over a three month period. The smart-grid optimisation was used to investigate a case study of the electricity demand for a representative University office building. Results suggest that the smart-grid formulation is able to reduce the EVs’ battery pack capacity fade by up to 9.1% and power fade by up to 12.1%