Duty-cycle characterisation of large-format automotive lithium ion pouch cells for high performance vehicle applications

Abstract

A B S T R A C TThe long-term behaviour of lithium ion batteries in high-performance (HP) electric vehicle (EV) applications is not well understood due to a lack of suitable testing cycles and experimental data. As such a generic HP duty cycle (HP-C), representing driving on a race track is validated, and six NMC graphite cells are characterised with respect to cycle-life. To enable a comparison between the HP-EV environment and conventional road driving, two test groups of cells are subject to an experimental evaluation over 200 duty cycles that includes a representative HP-C and a standard duty cycle from the IEC 62660-1 standard (IECC). After testing, both test groups display increased energy capacity, increased pure Ohmic resistance, lower charge transfer resistance an extended OCV operating window. The changes are more pronounced for cells subject to the HP-C. Based on capacity tests, Electrochemical Impedance Spectroscopy (EIS), pseudo-OCV tests, and Pulse Multisine Characterisation, it is concluded that the changes in cell characteristics are most likely caused by cracking of the electrode material caused by high electrical current pulses. With continued cycling, cells cycled with the HP-C are expected to show degradation at an increased rate due to raised temperatures, and more pronounced electrode cracking.

Research on residual life prediction method of lithium ion battery for pure electric vehicle

“…The overall scheme of the experiment is as follows: taking the battery capacity prediction accuracy, root mean square error and battery life prediction error as the experimental indexes, the proposed method is compared with Kellner et al (2018) and Quiones et al (2018).…”

“…The proposed method is compared with the method in Kellner et al (2018) and the method in Quiones et al (2018), and the comparison result is shown in Figure 5. According to Figure 5, it can be seen from the prediction error results in Figure 5 that the prediction error of the proposed method is slightly lower than that of Kellner et al (2018) and Quiones et al (2018), and the minimum prediction error of the proposed method is only 0.013, while the minimum prediction error of Kellner et al (2018) and Quiones et al (2018) is 0.023 and 0.024 respectively.…”

“…The prediction of lithium-ion battery state and life is the key to affect the battery management system, and provides the prediction information for the system, and provides important research significance for future development (Li et al, 2019b). Kellner et al (2018) put forward the prediction method of residual life of lithium-ion battery for electric vehicles based on particle filter. The method uses genetic algorithm to map the battery degradation model to the particle degradation problem, extracts the battery life tuning prediction parameters, calculates the sensitivity of the parameters, and completes the life prediction of electric vehicle lithium-ion battery according to the sensitivity.…”

“…3 The experimental results show that the proposed method is compared with Kellner et al (2018) and Quiones et al (2018) with battery capacity prediction accuracy, root mean square error and battery life prediction error as the experimental comparison index.…”

A Modern Simple Power Prediction Index for Improving Battery Life

“…e battery life model employed in this research is developed in 2011 and employed by many models recently [20,21]. Although there are newer models [22], the one reported by Wang proposes a formula for battery life assessment.…”