## 4 Optimizing Power Flow in EV Powertrains
### Multi-Motor Configuration
The main idea here was to split the weight distribution of the powertrain evenly to all wheels, thus enabling the increase of driving flexibility without the application of bigger motor and transmission. Each motor was also coupled to a two-speed AMTdesigned based on planetary gearset, where the appropriate gear ratio was actuated using DC motor and worm gear mechanism. In addition, the gear ratio actuator also featured ball-ramp self-energizing that consisted of translation plate with steel balls and spiral ramps. The purpose of this mechanism was to amplify the clutch engagement force during the gearshifting, resulting in the reduction of DC motor's required power. To simulate the powertrain performance, the AMT's gear ratios as well as its complete parameters were determined based on a transmission-equipped wheel hub motor described in [86], while the gearshifting schedule was developed based on the vehicle speed and the throttle position. To simplify the simulation model, a dog clutch model was used as the surrogate model for the proposed gear ratio actuator. Two shifting approaches were applied--synchronous and asynchronous--where synchronous means that the gearshifting occurred simultaneously for the front and the rear wheels, while asynchronous means the gearshifting was done independently between the front and the rear wheels with a delay of 0.2 s. The key benefit lay in the asynchronous approach, which minimized the jerking thanks to the delay that reduced the torque interruption in the front wheels' gearshifting by compensating it with the torque at the unshifted rear wheels (and vice versa). As a result, the jerking can be kept within the range of 4 m/s\({}^{3}\) to 6 m/s\({}^{3}\). However, the proposed powertrain configuration involved four independent two-speed AMT actuators, which means, although it was highly flexible in terms of gearshifting, it required sophisticated control logic to avoid too frequent shifting and gear hunting. Too frequent shifting and gear hunting, if not properly optimized, will cause driving discomfort and increased losses in the actuators. The latest studies on four motors EV powertrain configuration focused on not only achieving optimum efficiency, but also enhancing driving safety and steering assist [87; 88]. Unlike the powertrain configuration proposed in [83; 84; 85], the four motors EV powertrain introduced in [87; 88] lacked the gearshifting options since it used single speed transmission. However, the omission of gearshifting options increase its simplicity in terms of operation and controls. In [87], the element of driving safety, together with the optimum efficiency in terms of power consumption, can be realized by implementing integrated motors' torque vectoring control strategy in the wheels. The integrated strategy was intended to achieve multiple objectives such as reasonable traction torque distribution on the wheels for yaw stability control and steering assists, proper motor's output torque to enable it to operate at  
\[T_{out} =\frac{R_{out}}{R_{motor1}}T_{motor1}+\frac{R_{out}}{R_{motor2}}T_{ motor2} \tag{1}\]0 Four motors EV powertrain configuration with two-speed AMT [84; 85]. its most efficient range for optimum power consumption, and reducing the wheels' dynamic slip for driving stability as well as optimum power consumption. The proposed integrated strategy was then compared against the conventional axis distribution and maximization of stability margin strategies through simulation under WLTC, where the results showed that the integrated strategy reduced the wheels slip by 14.38% compared to the axis distribution strategy that led to the improved power consumption by 5.37%. The simulation results were then validated experimentally based on a single seat EV prototype that was driven at 60 km/h on a slippery road and then executing standard lane change maneuver. In the experiment, the wheels slip was reduced by 12.75% due to the implementation of the integrated torque vectoring control strategy as opposed to the axis distribution strategy.