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In this study, a control map generation strategy for hybrid electric vehicles based on machine learning (ML) with optimization data was studied using a multimode hybrid electric vehicle. The optimization data from dynamic programming were used to produce the control maps by employing different ML methods, including Gaussian na&#x00EF;ve Bayes, linear discriminant analysis, decision tree, k-nearest neighbors, and support vector machine. Since control map domains separated into several domains can exhibit unrealistic control behavior during engine on-off and hybrid mode shift processes, control maps separated into the same number of domains were used for the simulation study among the different ML methods. The demand torque and power maps by ML training were used for simulations of representative driving test cycles from the Environmental Protection Agency. The results with ML methods indicate that operating domains in the torque and power maps were separated for different driving modes, while they were not clearly separated in the results of DP optimization. Further, the results with the control maps for demand power exhibited slightly improved fuel efficiency compared to the maps for demand torque. This study is meaningful because a control map generation strategy based on ML was not only studied in order to observe the possibility of utilizing DP optimization results for real vehicles, but different types of ML methods were also analyzed and discussed to find appropriate methods for vehicle control map generation in terms of demand torque and power operating points.

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization