Anup Barai

The driving and charging behaviours of Electric Vehicle (EV) users exhibit considerable variation, which substantially impacts the battery degradation rate and its root causes. EV battery packs undergo second-life application after first-life retirement, with SoH measurements taken before redeployment. However, the impact of the root cause of degradation on second-life performance remains unknown. Hence, the question remains whether it is necessary to have more than a simple measure of state of health (SoH) before redeployment. This article presents experimental data to investigate this. As part of the experiment, a group of cells at around 80% SoH, representing retired EV batteries, were cycled using a representative second-life duty cycle. Cells with a similar root cause of degradation in the first life (100–80% SoH) exhibited the same degradation rate in second life after being cycled with the same duty cycle during the second life. When the root cause of degradation in the first life is different, the degradation rate in the second life may not be the same. These findings suggest that the root cause of a cell’s first-life degradation impacts how it degrades in its second life. Postmortem analysis (photographic and SEM images) reveals the similar physical condition of negative electrodes which have similar degradation rates in their second life cycle. This demonstrates that cells with a similar first life SoH and root cause of degradation indeed experience a similar life during their second life. The experimental results, along with the subsequent postmortem analysis, suggest that relying solely on SoH assessment is insufficient. It is crucial to take into account the root causes of cell degradation before redeployment.

For retired automotive lithium-ion batteries, state of health (SoH) is currently utilised to grade them for a second-life application. However, researchers previously challenged this and expressed that, in addition to SoH, the actual degradation mechanism, also known as degradation mode (DM), should be considered for grading, for efficient second-life operation. To date, there is little evidence to support this. A validated DM detection technique for cell/module grading does not exist. A modified DM detection technique by tracking and indexing the incremental capacity (IC) curves was previously proposed by the authors; nevertheless, it was difficult to validate. Researchers previously proposed DM identification using Electrochemical Impedance Spectroscopy (EIS) and Differential Voltage (DV) analysis. With a direct comparison of the techniques made exploiting IC, DV, and EIS, a correlation can be made, which is presented in this article. The correlation suggests that cells identified as having the same (or different) DM by the proposed technique also identified as having the same (or different) DM growth by EIS technique proposed by other researchers. Likewise, DV analysis suggests that the DV peak’s standard deviation of similar DM cells is smaller than that of the different DM cells.

The service life of the retired automotive lithium-ion battery (LIB) can be extended through reusing without major changes and reusing through remanufacturing. Reusing through remanufacturing at the module level likely outweigh remanufacturing at the cell level and reusing without major changes. To remanufacture a battery pack, cells/modules are required to be graded and cells/modules in a remanufactured pack needs to be at identical state. The current approach utilises cell/module capacity i.e. State of Health (SoH), to grade the cells/modules at the end of first life. However, the only capacity may not be enough to match the cells/modules; how the cells/modules have been degraded (degradation mechanism, DM) may play a vital role in defining their performance in second life. However, the existing DM detection techniques are not designed to grade the cells/modules at the end of the first life. In this paper, a modified DM detection process is proposed. Here, the proposed modified DM detection process is verified by applying them to the degradation experimental results. Employing the proposed modified DM detection method, cells at same SoH at the end of first life can be graded based on their DM.

In an electric vehicle, a large number of lithium-ion cells are connected in parallel. While cells in parallel increase the reliability of the battery pack, it increases the probability of current imbalance between the parallel branches, thus ageing gradient. The current peak in a cell also can exceed the maximum charge current capability of the cell; leading to lithium plating, therefore a safety issue. The temperature gradient within a battery pack amplifies this issue. This work reports the impact of such temperature gradient on current imbalance within parallel connected cells, their long-term impact on degradation and evaluation of current distribution with degradation. Employing a real-world relevant experimental setup, a total of 1400 cycles were performed on a module with four cells in parallel. A temperature gradient of 10 °C was introduced among the cells, and current in individual parallel branches was measured employing Hall-effect sensors. Over the course of the experiment, module capacity decreased by 23.6%. Cells at higher temperature/lower exposure to active cooling experienced higher degradation. However, the cell with the lowest starting capacity, although exposed to nominal module temperature and cooling experienced the highest current amplitude towards the end of discharge/charge and thus the highest resistance degradation. It was found that current in an individual parallel branch was exceeding the maximum rated charge/discharge current by 53%.

Today’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the accuracy of the battery’s underlying state estimators, e.g., state-of-charge and state-of-energy. This is affected by accuracy of the models embedded in the battery management system. The performance of these models fundamentally depends on experimentally obtained parameterisation and validation data. These experiments are mostly performed within thermal chambers, which maintain pre-set temperatures using forced air convection. Although these setups claim to maintain isothermal test conditions, they rarely do so. In this paper, we show that this is potentially the root-cause for deterioration of range estimation at low temperatures. This is because, while such setups produce results comparable to isothermal conditions at higher temperatures (25 °C), they fail to achieve isothermal conditions at sub-zero temperatures. Employing an immersed oil-cooled experimental setup, which can create close-to isothermal conditions, we show battery state estimation can be improved by reducing error from 49.3% to 11.7% at −15 °C. These findings provide a way forward towards improving range estimation in cold weather conditions.

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.

Recent uptake in the use of lithium-ion battery packs within electric vehicles has drawn significant attention to the selection of busbar material and corresponding thickness, which are usually based on mechanical, electrical and thermal characteristics of the welded joints, material availability and cost. To determine joint behaviour corresponding to critical-to-quality criteria, this study uses one of the widely used joining technologies, ultrasonic metal welding (UMW), to produce tab-to-busbar joints using copper and aluminium busbars of varying thicknesses. Joints for electrical and thermal characterisation were selected based on the satisfactory mechanical strength determined from the T-peel tests. Electrical contact resistance and corresponding temperature rise at the joints were compared for different tab-to-busbar joints by passing current through the joints. The average resistance or temperature increase from the 0.3 mm Al tab was 0.6 times higher than the 0.3 mm Cu[Ni] tab, irrespective of busbar selection.