# Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
## 5 Conclusions  
In this paper, a system-scale multi-objective optimization methodology for driving motors based on the clustering technique under different operating conditions is proposed for electric vehicle applications. The clustering approach has been introduced to identify energy weights and RPs in the torque-speed profile for two vehicle driving cycles. To evaluate the effectiveness of the system-scale optimization design of the driving motor, a comparative study has been carried out based on the semianalytical efficiency model and torque model of IPMSMs, where the motor was optimized for UDDS, HWFET, and the rated operating point. Finally, a prototype of IPMSM with an MPTA-flux weakening controller was investigated to verify the proposed method. From the discussion, the following conclusions could be drawn:  
First, compared with the optimization design under the rated operation point, the solution from the proposed optimization considering driving cycles has a larger sweet region with higher efficiency and lower torque ripple.  
Second, the control parameters are considered in the proposed system-scale optimization as well as the motor parameters, which can ensure the system's dynamic performances and are valuable for engineering batch production. Third, the proposed clustering optimization of driving motors considering inverter control strategy could well be applied for the rapid development of serialized motors in other applications, e.g., ship propulsion motors and robot motors.  
The future work will be extended to more structural forms of  
**Funding:** This research was funded by the Natural Science Foundation of China, grant number 52277065, and the National Key R&D Program of China, grant number 2020 YFA0710500.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l l l} \(B_{\rm r}\) & Radial magnetic density component & \(P_{\rm loss}\) & Motor power loss \\ \(B_{\rm t}\) & Tangential magnetic density component & \(P_{\rm out}\) & Motor output power \\ \(d\) & Total level number & \(P_{\rm Fe}\) & Iron loss \\ \(f\) & Magnetic field alternates frequency & \(P_{\rm Cu}\) & Copper loss \\ \(i_{\rm s}\) & Stator phase current & \(R_{\rm s}\) & Phase resistance \\ \(J\) & Moment of inertia & \(T_{\rm s}\) & Sampling period \\ \(k_{\rm s}\) & Fill factor of winding & \(T_{\rm r}\) & Motor torque ripple \\ \(K_{\rm c}\) & Eddy current loss coefficient & \(u_{\rm lim}\) & Motor phase voltage limitation \\ \(K_{\rm e}\) & Additional loss coefficient & \(w\) & Energy weights \\ \(K_{\rm y}\) & Hysteresis loss coefficient & \(W\,_{\rm m}\) & Co-energy \\ \(K_{\rm sp}\) & Proportion coefficient of speed controller & \(W_{\rm m}\) & Stored energy \\ \(K_{\rm si}\) & Integration coefficient of speed controller & \(z_{i}\) & Data point of sample \(i\) \\ \(K_{\rm ip\_d}\) & Proportion coefficient of d-axis current controller & \(l_{i}^{(t)}\) & Centroid of sample \(i\) in the \(t^{\rm th}\) iteration \\ \(K_{\rm ii\_d}\) & Integration coefficient of d-axis current controller & \(S_{i}^{(t)}\) & Cluster of sample \(i\) in the \(t^{\rm th}\) iteration \\ \(K_{\rm ip\_q}\) & Proportion coefficient of q-axis current controller & \(P_{\rm O(i)}\) & Average motor performance of factor \(O\) at level \(i\) \\ \(K_{\rm ii\_q}\) & Integration coefficient of q-axis current controller & \(\delta\) & Angle between the current vector and the d-axis \\ \(n_{\rm max}\) & Motor maximum speed & \(\theta\) & Rotor position \\ \(n_{\rm N}\) & Motor rated speed & \(\lambda\) & Flux linkage \\ \(p\) & Pole pair number & \(\lambda_{\rm pm}\) & Permanent magnet flux linkage \\ \(p_{\rm t}\) & Overall mean of motor performance data & \(\Omega\) & Rotor mechanical angular velocity \\ \end{tabular}