# Recognition of Driving Behavior in Electric Vehicle's Li-Ion Battery Aging
## 1 Introduction  
The use of battery electric vehicles (BEVs) will be essential to the decarbonization of transportation networks in the near future. The quantity of CO\({}_{2}\) emitted into the environment each day can be decreased by switching from ICE-powered automobiles to electric ones. However, environmental pressure appears on the heavily polluted manufacturing and recycling processes of the batteries, which generate more than 150 kg hazardous waste per 1 gigawatt-hour [1]. To reduce the contaminants without inventing manufacturing processes and materials, maintaining the battery in optimal operations can best extend the battery life. However, analyzing the optimal operation windows for the battery in a BEV is challenging. It involves identifying driving behaviors and mapping them to intricate battery aging parameters, including current profile, depth of discharge (DOD), state-of-charge (SOC), and temperature [2]. Recognizing driving behavior is also essential for the BMS to predict the SOH appropriately. The SOC and SOH are currently measured by the BMS using data-driven, model-based (e.g., equivalent circuit, electrochemical, mathematical), and a mixture of both methods [3; 4; 5]. In the data-driven approach, BMS can best cooperate with the driving behavior to mitigate battery degradation and optimize the battery life. Furthermore, unhealthy driving behavior can be realized by the driver. When driving operations correlateto the speed of degradation, the way that people drive in BEVs can be altered in a way that can prolong the battery life. Compared to ICE vehicles, the BEV industry has yet to reach its full potential for discovering explainable driving behavior. Many of the existing driving classifications are at the application level, which emphasizes aggressive driving as a safety risk in the context of public transit [6; 7; 8]. The versatility of the collected vehicle data (e.g., controller area network (CAN) bus, image, gyroscope) is prone to indigestion in the mathematical-based traditional models. Multi-dimensional time-series data are challenging to apply to the majority of statistical models, including hidden Markov model (HMM) [9; 10; 11; 12; 13; 14], Gaussian mixture model (GMM) [15; 16; 17; 18; 19], support vector machine (SVM) [20; 21; 22; 23; 24; 25; 26; 27], Naive Bayes (NB) [28; 29; 30], fuzzy logic (FL) [31; 32; 33; 34; 35; 36], and k-nearest neighbor (KNN) [20; 37; 38; 39; 40]. The deep learning based models, including convolutional neural network (CNN) [41; 42; 43; 44; 45; 46; 47; 48], recurrent neural network (RNN) [49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60], and Fusion models [61; 62; 63], showed extremely accurate classification results, but they were rarely connected to the reasons behind the driving behaviors. In addition, the electric power in BEVs can serve the purpose of some essential driving operations (e.g., acceleration and braking). Consequently, the level of explanation for driving behavior can be increased by recognizing the electric power patterns and reverse mapping from electric power to driving operations. Previous studies have provided a substantial amount of data on the car battery and driving [64]. However, the integrated data for driving and batteries on real BEVs are still extremely limited, because they can only be collected onboard the BEV with data accessing protocol. Meanwhile, SOH analysis requires the fine-time granularity of the acquired data (e.g., 250 ms sample rate). Currently, internet of things (IoT) devices can perform data collection in various situations and on precise time scales [65]. However, many automotive electronic control units (ECUs) can only operate at a lower sample rate due to the heavy load on the automobile's ECU. Even a substantial dataset gathered via the Chinese EV GB/T 32960.3-(accessed on: 20 March 2023) protocol was unable to achieve the fine-time granularity (i.e., 15 s sampling rate). Therefore, we offer a summary of research on driving behavior based on the Li-ion battery in BEVs.