# Optimizing Parameters for an Electrical Car Employing Vehicle Dynamics Simulation Program
## 2 Literature Review  
In the field of optimization, numerous methods have been developed and applied to various engineering problems [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Widely used optimization algorithms and techniques are fuzzy logic [28; 29], adaptive neuro-fuzzy inference systems [30; 31], the Taguchi method [32; 33], the grey system theory [26; 34; 35], teaching-learning-based optimization [36; 37], genetic algorithms [38], particle swarm optimization [39], tabu search [40], and simulated annealing [13; 14; 15; 16]. There is extensive literature on the optimization of vehicles using the above-mentioned algorithms and procedures. In [41], a multi-objective optimization is introduced applying the particle swarm algorithm to optimize the drivetrain and control system of a plug-in hybrid vehicle. In [42], a multi-objective optimization is presented using a genetic algorithm to improve acceleration performance, fuel consumption, and to mitigate emission of a vehicle. In [43], a genetic algorithm is applied to optimize the drivetrain of an electric vehicle. In [44], an evolutionary algorithm is applied to solve a multi-objective optimization problem on the drivetrain cost and energy consumption of a vehicle. In [45], an evolutionary global optimization-based, derivative-free, multi-objective genetic algorithm is applied to optimize the component sizing of the drivetrain of a parallel hybrid electric bus. In reference [46], in another application field, a particle swarm algorithm is applied for the optimization of vehicle fleets. In [47], MATLAB/Simulink simulations are applied to examine various powertrain configurations and components for designing a hybrid powertrain. The performance optimization was based on the simulation results. In [48], the optimum matching of an electric vehicle powertrain is presented, setting up the objective functions by maximum grade ability and driving range and the optimum problem is solved by applying simulated annealing. In [49], a vehicle powertrain-mounting system is optimized by applying simulated annealing. In [50], the vibration damping optimization of a vehicle powertrain system using simulated annealing is presented. Finally, in reference [51], a heuristic method is used to optimize the gearshift schedules of electric vehicles. Although the application of such algorithms has an extended literature in the automotive industry, the presented application of simulated annealing to optimize the technical parameters of prototype race cars for various competition tasks is a completely new field of application and we have not found any examples of it in the scientific literature. This concept and idea can effectively help student teams all over the world in preparing for various domestic and international competitions. Since, in our case, to optimize the parameters of our race car [3], the objective function is not available in closed form, its value can only be determined by simulation, so only metaheuristic algorithms can be considered [52]. Of these, we chose simulated annealing, which is an efficient and robust stochastic algorithm, that is characterized by a high convergence speed, and has various applications in different engineering and IT fields [15; 16]. A detailed description of the operation principle of SA can be found in reference [15] and further descriptions in the references [13; 14]. Simulated annealing is an optimization algorithm that can be used to determine the global minimum of an objective function by being able to solve the problem of getting stuck in local minima. The algorithm is based on the physical process of metallurgical annealing, where metal is heated and then slowly cooled. If the system cools very slowly, it can reach thermal equilibrium at each temperature.