# SingMonitor: E-bike Charging Health Monitoring Using Sound from Power Supplies  
Xiangyong Jian  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China  
Lanqing Yang  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China  
[PERSON] Li  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China  
Yi-C. Xue  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 20000, China  
Yi-C. Xue  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 20000, China  
Yi-C. Xue  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China  
Yi-C. Xue  
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 20000, China
fires. However, these methods are costly to install and maintain and only work after a fire hazard occurs. Some appliance detection methods can also monitor the charging health of e-bikes. For example, some of them use sensors to directly obtain the operating current, voltage, and load consumption of the appliances in the circuit [18; 19; 20; 21; 22; 23; 24]. Others use side-channel methods to identify appliances and their operating status by the electromagnetic [25; 26] or acoustic signals [27; 28] generated when the appliance is in operation. However, most of these methods require extra hardware, which makes them difficult for large-scale deployment, and some have limited monitoring distances. Some battery monitoring methods [29; 30] use sensors to obtain information such as the voltage and current of the battery to monitor the battery. Ref. [31] trains an LSTM model with voltage, temperature, and other information to monitor and protect the battery. Ref. [32] predicts the battery status through an electrochemical model. These methods require extra hardware devices to obtain information such as current and voltage, which increases the system's cost. To solve this problem, in this paper, we propose _SingMonitor_, a scheme to remotely monitor e-bike charging health using mobile devices' microphones. _SingMonitor_ can monitor the charger or battery for faults during daily charging, slowing battery capacity loss and thus preventing potential fire hazards. We create the _SingMonitor_ system based on observations that: (1) The charging e-bike can inject unique current signals into the power grid. Currently, e-bikes commonly use three-stage battery chargers [33]. At different charging stages, the power factor correction (PFC) circuit inside the e-bike charger can inject different feedback currents, i.e., PFC signals, into the power grid; (2) The PFC signals can transmit through the power grid. As shown in Figure 1, PFC signals can be propagated along wires throughout the power grid. A detailed explanation can be found in Section 3.3; (3) The PFC signals can drive the power supply at the other end of the grid to generate sounds. When the power supply of another appliance connected to the same distribution system receives this signal, it will emit a sound under electromagnetic forces; (4) and the sounds can be captured by nearby mobile devices and used to monitor the e-bike's charging health. By analyzing this sound signal, _SingMonitor_ can obtain the current charging stage of the e-bike, as well as the charge duration. After comparing with the normal charging pattern (see Section 3.1), the system can determine whether there is a problem with the charger and battery, thus monitoring the e-bike's charging health. In developing the proposed _SingMonitor_, we encountered several challenges: (1) Interference of noises. The PFC signals are exposed to background noise interference from the power grid, environment, the power supply's internal components, and the sound acquisition equipment during propagation and conversion to sound signals.