The pedal-assist electric bike is interesting equipment because of using these two power sources [1,2]. Predicting the performance of an electric bike requires recognizing both of these driving forces. Activities have been carried out on the electric propulsion force.And due to the structure of electric propulsion, its performance is more predictable than human propulsion. Therefore, in order to make this energy management more applicable, human driving propulsion must be well known [3]. Jessica E Bourne, et al. [4] studied on health improvements by using e-bikes [4]. The driver's tiredness influences power generation from the human power source.Therefore, the driver might not be able to reach the destination earlier than a specified time. So, the major part of this study is covered by the driver's tiredness issue. The researchers such as Alex OW Natera, et al, [5][6][7] studied environmental factors that affect human fatigue while driving a bicycle [5][6][7][8]. Moreover, there are some fac tors related to human physiques that have an impact on human fatigue also [9,10]. Determining the fatigue threshold for the human is so important to discuss that has been done by Didace Ndahimana, et al. [11]. Considering cooling down and warming up time for the driver and following some basic sport instructions can protect the driver from damage and extreme fatigue [12]. Calculating the burned calories is a way to determine the driver's fatigue while exercising. The calculation process is performed with different means [13].There are different methods to calculate burned calories and determining the driver's tiredness status. Doubly labeled water (DLW) method is an accurate method but it is costly. Because it needs high advanced devices, and, specialties' attendance is required. A calorimeter is a direct way to calculate consumed energy but it is an expensive method. There is an indirect method that is

In this study, a switch controller manages the power‐sharing between the battery and human mode to improve the rider's metabolism and manage the battery SOC. The main idea is to optimize this power source switching element for changing the status to reach a trade‐off between lack of tiredness and keeping the SOC high. Calorie burning is closely related to the rider's physical characteristics. In this paper, these parameters are investigated to calculate calorie burning. When the electric‐powered mode is activated, the SOC level comes down. When the human‐powered mode is activated, the human power source provides energy. The model converts the bicycle speed into the rider's heart rate and then changes it into burned calories based on some equations. These equations are obtained by poly fitting after experiments. This optimization causes 33.5% and 50% burning calorie reduction in Cleaveland and Portuguese driving cycles. Also, in the Portuguese driving cycle, the battery usage percentage decreases 39.56% from to 20.54% after optimization; therefore, the burning calorie decreases 265.84 Kcal to 176.83 Kcal.