## 4 Optimizing Power Flow in EV Powertrains
### Multispeed Discrete Transmission
In the work, an AMT was used and its baseline gear ratios were set at 10.00 and 5.20 for the overall gear ratio 1 (G1) and 2 (G2), respectively. For the shifting strategy, the motor speed of 3000 RPM is set as the reference for the driving due to its high efficiency in that speed, and G1 is reserved for low vehicle speed (0 to 25 km/h), and G2 is reserved for high vehicle speed (65 to 120 km/h). Between 25 to 65 km/h, the suitable ratio was selected based on the motor efficiency and power output at a particular vehicle speed, while the baseline buffer zone of 40% was set between the upshifting and the downshifting lines to avoid too frequent gear shifting. Subsequently, the baseline ratios and shifting's buffer zone were optimized using two methods: gradient descent and pattern search. Simulated under NEDC (to reflect flat road condition) and Economic Commission for Europe (ECE) Extra Urban driving cycle (to reflect gradient road), the optimized model produced a 4% and 7.5% reduction in the power consumption as compared to the baseline model, respectively. Next, the performance of the optimized model was compared against a conventional gearshift model. The conventional gearshift model consists of the same ratios as the optimized model, but it uses a conventional gearshifting strategy based on throttle. The comparison showed that the optimized model led to almost 18% energy saving over the conventional model for the gradient road driving cycle (ECE Extra Urban). However, for the flat road driving cycle (NEDC), the conventional model was slightly more efficient at about 3 to 4%. These results highlighted the contribution of two different gearshifting strategies \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Literature, Year** & \multicolumn{2}{c}{**Summary of the Works**} & \multicolumn{2}{c}{**Significant Findings**} \\ \hline \multirow{8}{*}{[PERSON] et al., 2019 [29]} & - & Optimizing the gearshifting strategy for an electric bus using four-speed AMT. & - & Highlighting the significance contribution of optimum gearshifting strategy that can allow the motor to operate efficiently while avoiding too frequent gearshifting. \\  & - & Data for the driving cycle were obtained experimentally. & - & Also highlighting the gap in optimizing the gear ratios for further improvement in the powertrain efficiency. \\  & - & The actual four-speed AMT was tested on test bench for shifting time and efficiency. & & In the two other strategies, however, significant losses suffered in the transmission due to the slipping in the clutches and brakes. \\ \hline \hline \multirow{8}{*}{[PERSON] et al., 2020 and 2018 [30, 31]} & - & Proposing novel two-speed transmission for EV using planetary gearset. & - & Highlighting the importance of proper shifting strategy to minimize jerking during gearshifting. \\  & - & Geanshifting mechanism using clutches and brakes actuated by DC motors with screw and nut. & - & Biggest jerking occurred in the shifting strategy to maintain the constant input torque, lowest jerking occurred in the other two strategies. \\  & - & Transmission model was developed and simulated for gearshifting strategies & - & Higlighting the importance of proper shifting strategy to minimize jerking during gearshifting. \\  & - & Transmission model was developed and simulated for gearshifting strategies & - & Biggest jerking occurred in the shifting strategy to maintain the constant input torque, lowest jerking occurred in the other two strategies. \\  & - & maintain constant transmission input torque, to maintain constant transmission output torque. & - & In the two other strategies, however, significant losses suffered in the transmission due to the slipping in the clutches and brakes. \\  & - & Performance of the strategy was evaluated in terms of acceleration time and jerking. & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._in optimizing powertrain efficiency for driving cycles involving diverse road gradients.