@article{scholars16356,
            note = {cited By 10},
          volume = {61},
          number = {10},
             doi = {10.1016/j.aej.2022.01.027},
           title = {A review of free piston engine control literature{\^a}??Taxonomy and techniques: A review of free piston engine control},
            year = {2022},
         journal = {Alexandria Engineering Journal},
       publisher = {Elsevier B.V.},
           pages = {7877--7916},
        keywords = {Electric generators; Electric machine control; Engine pistons; Free piston engines; Fuzzy logic; Hybrid vehicles; Motion control; Taxonomies, Control techniques; Engine control; Free-piston engine; Hybrid engines; Internal combustion; Linear electric machine; Linear generators; Operation parameters; Piston motion; Piston motion control, Genetic algorithms},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123918717&doi=10.1016\%2fj.aej.2022.01.027&partnerID=40&md5=fe72cfd05f02b99d9152dd255798f9a8},
        abstract = {The free piston engine linear generator (FPELG) is a simple structure engine with only two main parts, i.e., the linear generator and a free piston engine, which has less friction, low emission, high thermal efficiency, and multi-fuel engine. However, the pistons move freely; thus, piston motion control (PMC) is a crucial technical challenge that affects both the performance and stability of FPELG. This review addresses different control techniques and operation parameters of FPELG. Through this review, a new taxonomy method is proposed based on two main groups of control strategies, i.e., the linear-FPE and others-FPE control. The linear-FPE control is classified into the PMC, linear electric machine control, switching control, and combined control. According to this taxonomy, a selection of previous studies was thoroughly analyzed to identify new research directions related to FPELG. The statistical analysis and observations demonstrate that very few studies have used advanced control techniques, e.g., fuzzy logic, neural network, and PID with the genetic algorithm controls, for the FPELG. Some operation parameters require further investigation. Therefore, based on this review, researchers can explore new research directions and select a suitable technique to solve PMC issues of FPELG. This review thus constitutes a useful reference for researchers. {\^A}{\copyright} 2022 THE AUTHORS},
            issn = {11100168},
          author = {Raheem, A. T. and A. Aziz, A. R. and A. Zulkifli, S. and Rahem, A. T. and Ayandotun, W. B.}
}