**1. Introduction**

With the advent of hybridization concepts into the automotive field, searching for drivetrains which their component dimensions and control parameters are simultaneously designed for optimal objectives has been attained huge attention from the researchers. The hybrid drivetrains comprise several energy sources and components such as electric motors, batteries, power electronics converters and Internal Combustion Engine (ICE). Hence, making concrete design decisions for their topologies is significantly complicated compared to conventional ones in terms of sizing. Furthermore, the design space becomes larger considering complexities caused by indispensable power control parameters and consequently high degrees of freedom due to presence of multiple power sources [1, 2]. This produces a large

search space making it sophisticated for achieving objectives which are often counteracting, but equally important, e.g. satisfactory charge maintenance and fuel consumption minimization [3–6].

Due to their inevitable interrelations, the design levels of drivetrains cannot be performed independently or through standalone sequential framework as it leads one to suboptimal results. This makes the design challenge as a problem, which needs to be addressed in a holistic way meeting various constraints. Along this line, the strong coupling between components sizes of a drivetrain and parameters of its controllers turns the optimal sizing and control design of HEVs into a Bi-level optimization problem. For obtaining an optimal system design, the drivetrain components dimensions and the vehicle energy management strategy (EMS) should be designed in an interconnected and cohesive manner called **integrated optimal design or co-design** leading to minimum drivetrain cost and minimum fuel consumption as main objectives. There are several optimization algorithms and sequences available for integrated design of HEVs such as stochastic, gradientbased, deterministic, and derivative-free optimization methods [7]. The algorithm selection for integrated design of hybrid drivetrains depends on design targets. However, among variety of existing approaches, the metaheuristic algorithms e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA) etc., owing to their derivative-free features, could bring great potential and flexibility toward handling the non-monotonic, non-linear, and highly dynamic nature of HEV design.

In this chapter, an application of continuous Ant Colony Optimization (ACO) as a relatively recent nature-inspired algorithm is presented for integrated design of HEVs focusing on minimization of drivetrain costs besides fuel consumption at the meantime. The design variables include power rating of the components (i.e. battery, ICE, electric motors) and control parameters dealing with power sharing through the components. Various equality and inequality constraints involve in the optimization procedure related to components power sharing limitations, initial and final battery state-of-charge (SoC), maximum and minimum allowable SoC boundaries, and charging rate limitations. To this end, first there is a need to establish a full vehicle model and its corresponding energy management strategy (EMS) which will be performed in Simulink® environment. A modeled passenger vehicle will be coupled into an ACO algorithm scripted in MATLAB to work in tandem for the optimization purpose. The developed framework triggers the integrated design objectives via minimizing sizing and control objective functions while satisfying the design constraints to be eventually compared with an initial non-optimal case. The optimization includes two iterative nested parts linked into each other through an inner loop to consider the optimization objective and constraints for component sizing and control in an integrated and iterative manner as simplified in **Figure 1**.

The present chapter is organized as follows. Section 2 presents the drivetrain architecture of the studied passenger HEV. In Section 3, individual modeling of the vehicle's components, EMS and corresponding descriptions will be elaborated. Section 4 reviews the principles of the used ACO algorithm. Section 5 narrows down

**Figure 1.** *Coordination of the nested integrated design.*

*Application of Ant Colony Optimization for Co-Design of Hybrid Electric Vehicles DOI: http://dx.doi.org/10.5772/intechopen.97559*

the objective functions, optimization constraints and the integration of the simulation into optimization process for the studied application. Section 6 discusses and compares the attained results, and finally Section 7 recapitulates the conclusions. The outcome of this chapter provides useful information related to incorporation of soft-computing, modeling and simulation concepts into optimization-based design of HEVs from all respects for designers and automotive engineers.
