# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 3 Efficiency Optimization Control Based on Current Angle Look-Up Table
### Current Angle Optimization Flow Based on Improved Golden Section Search Method  
From the above results, it seems that the change rule of the optimal \(\beta\) is that when at the same load torque, the optimal \(\beta\) of the EDS efficiency decreases with the increase of the speed. Therefore, a large number of working points in the full operating range of EDS were analyzed in this paper to verify this rule, and the results proved that it was. Therefore, this paper proposes a \(\beta\) optimization method based on GSS, which can improve the speed of optimization and reduce the time to find the optimal set of \(\beta\)/is in the full operating range. Its process is as follows:  
1. According to the external characteristic diagram of the motor, select each working point on the red curve in Figure 8 as two groups of working conditions.
2. Change the current angle, then the current angle curves \(\beta_{nm-min}\)/\(\beta_{nm-max}\) that ensure the efficiency of red curve maximum can be obtained and recorded in \(T_{o}\)/\(n_{m}\)/\(\beta\)/\(i_{s}\) look-up table so as to reduce the search area in GSS.
3. Limit the current angle between \(\beta_{nm-min}\) curve and \(\beta_{nm-max}\) curve, then use GSS to optimize \(\beta\) under other speed conditions at Te(k),..., Te(k+n) (as shown by the points on the green line in Figure 8). The flowchart of GSS is illustrated in Figure 9.
4. Record the set of \(\beta\)/\(i_{s}\) corresponding to the maximum efficiency under whole working condition to \(T_{e}\)/\(n_{m}\)/\(\beta\)/\(i_{s}\) look-up table, which is illustrated in Figure 10.  
Figure 8: Schematic diagram chart of current angle \(\beta\) optimization.  
Figure 10 shows the optimal current angle \(\beta\) and corresponding stator current \(i_{s}\) of EDS in the full working area. In Figure 10a, under the condition of low speed and high torque of the motor, the value of \(\beta\) is close to \(0.5\pi\). At this time, \(i_{d}\) was close to \(0\) A, which was close to the valve in MTPA (\(i_{d}=0\) A) control, but there was room for optimization obviously, which shows that the control strategy proposed in this paper had superiority over MTPA. Under the condition of low speed and low torque, the MTPA control could no longer meet the optimal efficiency, so the stator current \(i_{s}\) needed to be deflected to the left to increase \(\beta\) so as to increase the amplitude of \(i_{d}\). When the motor entered the flux weakening region, optimal \(\beta\) continued to decrease while \(i_{s}\) continued to deflect to the left to meet the limit circle of voltage caused by the increased speed. In general, the optimal \(\beta\) decreased with the increase of motor speed and increased with the increase of motor torque. In Figure 10b, \(i_{s}\) increased with the increase of torque; meanwhile, there was a small increase when the speed increased. This is because the increase of speed will make the voltage limit circle of the motor narrow and \(i_{s}\) have to deflect to the left which will cause the increase of its amplitude, then the motor can meet the torque requirements. This can be seen in Figure 4.  
Figure 10: Optimal \(\beta\) and \(i_{s}\) three-dimensional look-up table under whole working condition: Optimal current angle \(\beta\) (**a**); corresponding stator current \(i_{s}\) (**b**).  
Figure 9: Schematic diagram of golden section search method.