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Lim, Dong-Kuk,Jung, Sang-Yong,Yi, Kyung-Pyo,Jung, Hyun-Kyo Institute of Electrical and Electronics Engineers 2018 IEEE transactions on industrial electronics Vol.65 No.2
<P>For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.</P>
A Mixed SOC Estimation Algorithm with High Accuracy in Various Driving Patterns of EVs
Lim, Dong-Jin,Ahn, Jung-Hoon,Kim, Dong-Hee,Lee, Byoung Kuk The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.1
In this paper, a mixed algorithm is proposed to overcome the limitations of the conventional algorithms, which cannot be applied in various driving patterns of drivers. The proposed algorithm based on the coulomb counting method is mixed with reset algorithms that consist of the enhanced OCV reset method and the DCIR iterative calculation method. It has many advantages, such as a simple model structure, low computational overload in various profiles, and a low accumulated SOC error through the frequent SOC reset. In addition, the enhanced parameter based on a mathematical analysis of the second-order RC ladder model is calculated and is then applied to all of the methods. The proposed algorithm is verified by experimental results based on a 27-Ah LiPB. It is observed that the SOC RMSE of the proposed algorithm decreases by about 9.16% compared to the coulomb counting method.
An Optimal Design Strategy for a Thomson Coil Actuator
Dong-Kuk Lim,Sang-Yong Jung,Hyun-Kyo Jung,Jong-Suk Ro 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.1
arc eliminator allows a surge current bypass rapidly into the earth to facilitate the circuit breaking process and increase the stability of the load circuit. Thomson coil actuator (TCA) is a type of actuator that can function as an arc eliminator. The TCA has a simple structure and significantly rapid speed compared to the other types of actuators. In this paper, significant variables, which have a dominant effect on the performance of the TCA, are investigated in detail. Using these variables and an optimization algorithm, an optimal design strategy for the TCA is proposed in this research. The efficacy of the proposed optimal design strategy and the feasibility of the application of the designed TCA for a high power circuit breaker as the arc eliminators are validated through the experiment.
Dong-Kuk Lim,Jong-Min Ahn,Ji-Chang Son 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1
In this paper, territory particle swarm optimization (TPSO) is proposed for the optimal design of surface mounted permanent magnet synchronous motor (SPMSM) for high altitude long endurance (HALE) unmanned aerial vehicle (UAV). The proposed algorithm exploits the concept of a territory to the particle swarm optimization (PSO) to solve the problem that the conventional PSO cannot fi nd the multimodal solutions. Also, by combining the stochastic method, PSO, with the deterministic method, pattern search method, it is possible to fi nd optimal solutions quickly and accurately. The superiority of the proposed algorithm is verifi ed through comparison in the test functions with niching genetic algorithm, which is well known multimodal optimization algorithm. The TPSO is applied to the optimization design of outer-rotor SPM for HALE UAV, and the optimal design with reduced cogging torque has been derived.
Optimal Design of an Axial Flux Permanent Magnet Synchronous Motor for the Electric Bicycle
Dong-Kuk Lim,Yong-Sun Cho,Jong-Suk Ro,Sang-Yong Jung,Hyun-Kyo Jung IEEE 2016 IEEE transactions on magnetics Vol.52 No.3
<P>Design of an electric machine such as the axial flux permanent magnet synchronous motor (AFPMSM) requires a 3-D finite-element method (FEM) analysis. The AFPMSM with a 3-D FEM model involves too much time and effort to analyze. To deal with this problem, we apply a surrogate assisted multi-objective optimization (SAMOO) algorithm that can realize an accurate and well-distributed Pareto front set with a few number of function calls, and considers various design variables in the motor design process. The superior performance of the SAMOO is verified by comparing it with conventional multi-objective optimization algorithms in a test function. Finally, the optimal design result of the AFPMSM for the electric bicycle is obtained by using the SAMOO algorithm.</P>
Dong-Kuk Lim,Dong-Kyun Woo,Han-Kyeol Yeo,Sang-Yong Jung,Jong-Suk Ro,Hyun-Kyo Jung IEEE 2015 IEEE transactions on magnetics Vol.51 No.3
<P>To design electric machines, the motor performance, cost, and manufacturing have to be considered. Hence, researchers have called this the multi-objective optimization (MOO) problem in which the goal is to minimize or maximize several objective functions at the same time. In order to solve the MOO problem, various algorithms, such as nondominated sorting genetic algorithm II and multi-objective particle swarm optimization, have been widely used. When these algorithms are applied to the electric machine design, much time consumption is inevitable due to many times of function evaluations using a finite-element method. To solve this problem, a novel surrogate-assisted MOO algorithm is proposed. Its validity is confirmed by comparing the optimization results of test functions with conventional optimization methods. To verify the feasibility of its application to a practical electric machine, an interior permanent magnet synchronous motor is designed.</P>
Dong-Kuk Lim,Kyung-Pyo Yi,Sang-Yong Jung,Hyun-Kyo Jung,Jong-Suk Ro IEEE 2015 IEEE transactions on magnetics Vol.51 No.11
<P>To optimize an interior permanent magnet synchronous motor (IPMSM) design for a fuel cell electric vehicle, a new surrogate-assisted multi-objective optimization (MOO) algorithm is proposed in this paper. The proposed algorithm is a multi-objective algorithm (MOO) that can account for three kinds of objectives such as the torque amplitude, torque ripple, and magnet usage simultaneously to improve the power transmission and to reduce the noise, vibration, and cost for various design variables. While the conventional MOO algorithms have a series that requires many function evaluations, especially considering many objectives and design variables, the proposed algorithm can create an accurate and well-distributed Pareto front set with few function evaluations. In comparison with the conventional MOO algorithms, the outstanding performance of the proposed algorithm is verified. Finally, the proposed algorithm is applied to an optimal design process of an IPMSM.</P>
A Mixed SOC Estimation Algorithm Using Enhanced OCV Reset and the DCIR Iterative Calculation Reset
Dong-Jin Lim,Jae-Gu Kim,Jung-Hoon Ahn,Dong-Hee Kim,Byoung-Kuk Lee 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6
In this paper, a mixed algorithm is proposed to improve SOC estimation accuracy for large-capacity Li-ion battery by using advantages of coulomb counting method, DCIR reset method, and enhanced OCV reset method. Because each method has drawbacks of accumulated SOC error during the operation in EVs, the optimal mixed algorithm is presented. Also, the weighted current value IDC based on the mathematical analysis of the second-order RC ladder model is calculated and then it is applied to methods respectively. The proposed algorithm is verified by the experimental results based on the 27 Ah LiPB. As a result, the SOC RMSE of the proposed algorithm is decreased about 2.22% by compared with the coulomb counting method.
A Mixed SOC Estimation Algorithm with High Accuracy in Various Driving Patterns of EVs
Dong-Jin Lim,Jung-Hoon Ahn,Dong-Hee Kim,Byoung Kuk Lee 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.1
In this paper, a mixed algorithm is proposed to overcome the limitations of the conventional algorithms, which cannot be applied in various driving patterns of drivers. The proposed algorithm based on the coulomb counting method is mixed with reset algorithms that consist of the enhanced OCV reset method and the DCIR iterative calculation method. It has many advantages, such as a simple model structure, low computational overload in various profiles, and a low accumulated SOC error through the frequent SOC reset. In addition, the enhanced parameter based on a mathematical analysis of the second-order RC ladder model is calculated and is then applied to all of the methods. The proposed algorithm is verified by experimental results based on a 27-Ah LiPB. It is observed that the SOC RMSE of the proposed algorithm decreases by about 9.16% compared to the coulomb counting method.