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Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization–based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.

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Owing to the limited range, battery electric vehicle drivers are likely to worry about being stranded in range-critical situations, known as range anxiety. Research has revealed that the provision of accurate information through range-related in-vehicle information systems establishes a solution to overcome range anxiety. However, an important range-related information i.e. the battery state of health, is seldom being considered in the research of range anxiety due to the difficulty in daily-use acquisition. Therefore, this paper aims to explore the influence of state of health on the range anxiety of battery electric vehicle drivers by performing a driving experiment. Under the condition of state of health degradation, participants used simplified battery electric vehicle simulators equipped with different types of in-vehicle information systems to finish a designated trip in a range-critical situation. During and after the trip, they measured their experienced range anxiety level. Results show that participants perceive less range anxiety when the in-vehicle information system provides the remaining range corrected by the state of health, and range anxiety can be further reduced when the extra correlated information (the state of health) is displayed on the in-vehicle information systems. The findings provide useful inspirations that range anxiety can be reduced if the range-related in-vehicle information system is well designed in information content and presentation considering the state of health.

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