Accurately assessing degradation and detecting abnormalities of overcharged lithium-ion batteries is critical to ensure the health and safe adoption of electric vehicles. This article proposed a data-driven lithium-ion battery degradation evaluation framework. First, a multilevel overcharge cycling experiment was conducted. Second, the battery degradation behaviors and features were analyzed and extracted using incremental capacity analysis and Pearson correlation coefficient. Above all, a data-driven lithium-ion battery degradation evaluation method based on machine learning and model integration method was developed. The proposed integrated model was compared with other state-of-the-art methods and reached a mean squared error of 1.26 × 10 ⁻⁴ . Finally, based on prediction results, rate of degradation was calculated and classified to different degrees, and overcharged cells can be effectively identified. Moreover, to verify the feasibility of the proposed overall framework, this article carried out an experiment by connecting overcharge-induced degraded cell and fresh cells in series to simulate the real-world battery assembly and function of battery management systems. Based on the proposed scheme, the overcharged batteries in the battery series can be detected efficiently likewise.

The widespread adoption of electric vehicles (EVs) with fast-charging capability has presented significant challenges to the safe operation of power distribution networks. Issues such as node overvoltage and frequency fluctuations should be addressed to ensure power quality while maximizing charging Quality-of-Service (QoS) for EV users. This challenge is particularly crucial for fast-charging events that have more flexible demands. Charging priority has traditionally been used to determine an EV charging scheme, including waiting time, arrival order and other factors. However, the scheduling potential predicted from the vehicle side has not been fully considered in the priority establishment when determining real-time operation. To address this gap, a bi-level framework is proposed for a coordinated charging scheme at the fast-charging station that factors in demand-based priority. The framework considers available charging power allocation at different priority levels and optimizes the charging scheme under the same priority level based on three real-time QoS objectives. This approach can improve the average charging QoS for EV users compared to traditional algorithms without considering the demand priority. In robustness evaluation, the QoS of scheduled charging events can reach 86.9% (median) of the optimal solution, and the range of fluctuations in different trials is relatively more stable.

Personal driving behavior affects vehicle energy consumption as well as driving safety; therefore, driving behavior is key information for electric vehicle (EV) energy management and advanced driver assistance systems. Eco-driving is an efficient way to reduce energy consumption and air pollution. As the basis of driving behavior, limited types of driving event information, as used in several other studies, cannot be used to meet eco-driving evaluation study needs. Complex and inconsistent human-defined rules are not conducive to the establishment of driving events. Hence, it is necessary to establish a driving event classification system with more categories of drive-topics that can present a better linkage between driving behavior and energy consumption. This paper proposes a driving event recognition method. Dynamic Local Minimum Entropy is proposed, and the Latent Dirichlet Allocation algorithm is used to classify different driving events. Drive-topics are proposed which describe driving events more accurately. The data from fifty battery-electric taxis are used to train the algorithm with data collected by the Service and Management Center for EVs, Beijing, in 2018. The relationship between drive-topic and energy consumption is analyzed to demonstrate that driving behavior can be established using drive-topics to support the evaluation of eco-driving for battery-electric vehicles.