## 4 Optimizing Power Flow in EV Powertrains
### Multispeed Discrete Transmission
For improvement, other type of clutch, like a dry clutch system with electro-mechanical actuator, can be implemented, so that the clutching and gearshifting can be precisely controlled based on the motor's torque to minimize jerking and shifting time. Returning to the work explained in [47; 48], a dry clutch was used, and its slip control was optimized using high-order disturbance observer to minimize jerking and shifting time, and the clutch control was then tested experimentally in a small EV during upshifting and downshifting. The dry clutch was actuated by a DC motor. The results were encouraging, with the jerking measured at most around 3 m/s3, which is significantly lower than the widely accepted threshold of 10 m/s3. Nevertheless, the operation of I-AMT involved frequent slipping in the dry clutch,  
Figure 8: The proposed bilateral Harpoon-shift synchronizer by [41; 42; 43]. hence its durability is expected to be compromised. This leads to the possible increase in the maintenance cost against a simpler single-speed transmission EV powertrain. Thus, detailed study in this aspect is crucial to quantify its long-term operation. Further studies on optimizing the gearshifting mechanism were explained in [49], in which the application of torque sensor was proposed in a two-speed DCT so that precise clutch engagement force can be regulated to fit the desired clutch torque for optimum shift quality. The torque sensor allowed precise real time torque measurement which is crucial to regulate the clutch engagement for optimum engagement time with minimum jerking. However, the application of torque sensor required significant cost, which will increase the transmission's cost tremendously. This unfortunately makes the option of implementing the torque sensor in the actual transmission impractical. More advanced studies related to EV powertrain with two-speed discrete transmission focused on the shifting strategy that adapts driver behaviors are described in [51]. Previous studies on the methods to recognize driver behaviors can be read in [52; 53; 54], covering its application in a fuel cell vehicle and HEV, but none of them was conducted specifically for EV powertrains with two-speed transmissions. However, all of the literatures have certain similarities, in the sense that, the throttle opening rate was used as the indicator for the behavior, and then fuzzy logic was applied to predict the suitable driving style corresponding to the modified standard driving cycles based on driving aggressiveness. Subsequently, the baseline driving style (usually established based on conventional practice) was optimized in the literatures by embedding the correcting factor representing the fuzzy logic's output. Therefore, in [51], they also proposed a predictive model based on a fuzzy neural network (FNN) to recognize the driver's intention via the actual throttle opening rate. Simultaneously, the learning vector quantization neural network (LVQNN) method was used to select the appropriate driving cycle by comparing the actual vehicle speed data against three predetermined different driving cycles. These predetermined cycles were obtained offline based on samples generated from the driving cycles of New York City Cycle (NYCC) UDDS and HWFET. Finally, a correcting factor, representing the outputs from FNN and LVQNN, was introduced to the baseline shifting strategy to optimize it for efficiency and driving performance. The baseline shifting strategy was formulated by taking into account the motor's efficiency, throttle opening and battery's SOC at 40% and 70%. Comparison between the baseline shifting strategy with and without the correcting factor, through simulation and dynamometer testing, showed an average efficiency improvement of up to 2%, proving the benefits of adapting driver behaviors in the shifting strategy.