The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are obtained, which go beyond the results achieved with solely optimal control for a given topology. Due to the large number of variables for optimization, their diversity, and the nonlinear and multiobjective nature of the problem, a variety of methodologies have been developed. This paper presents a comprehensive analysis of the various methodologies developed and identifies challenges for future research. Starting from a general description of the problem, with examples found in the literature, we categorize the types of optimization problems and methods used. To offer a complete analysis, we broaden the scope of the search to several sectors of transport, such as naval or ground.

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

This paper comprehensively reviews power management strategies (PMS) and component sizing (CS) methods for hybrid vehicles with more than one energy storage system (ESS). The PMS aims to coordinate the power flow among different ESSs while meeting the drive commands and other constraints; whereas, CS is used to optimize the combination of the components to compose a cost-effective powertrain. However, these two aspects are usually coupled such that it is not reasonable to discuss them separately from a system-level design perspective. Therefore, this review briefly discusses the popular PMS, followed by a detailed CS review in different aspects, including the classic and optimization-based with their own subtypes. In addition, several case studies belonging to different optimization structures are also reviewed in detail to demonstrate the features and the main conclusions of each method. As the comparison results show that with the proper CS methods, the hybrid powertrain witness a large fuel saving compared to their conventional counterparts. Furthermore, factors or issues that affect the performance of the PMS and CS methods are discussed. Meanwhile, the future research trends in PMS and CS are elaborated. This study intends to help researchers to have an overview of the state-of-the-art in PMS and CS methods and more importantly, to provide directions for optimal powertrain and power management controller designs for cost-effective and environmental-friendly vehicles.

A review of power management strategies and component sizing methods for hybrid vehicles

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system.

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Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey

Electrified vehicles are considered a promising technology for energy savings and emission reductions. Both powertrain design (configuration design and component sizing) and energy management strategies (EMSs) for electrified vehicles have been studied extensively. However, powertrain design and energy management need to be examined holistically and optimized simultaneously, from a mechatronic viewpoint, for maximizing the potential of electrified powertrains. This article provides a comprehensive, critical review of the current state, and prospects of electrified powertrain design and energy management. The research status in both powertrain design and energy management development is reviewed and discussed. First, the modeling techniques for rapid configuration design are thoroughly reviewed and summarized. Then, the optimization methods for component sizing are elucidated. Next, the classical EMSs are categorized, and several near-optimal strategies used for powertrain design are elaborated. Finally, the current challenges and future trends of electrified powertrain design and control are discussed, which provides a useful reference to researchers in this area.

Powertrain Design and Control in Electrified Vehicles: A Critical Review

Electric hybridisation of vehicles aims at reducing fuel consumption but increases production costs. Hence, automobile manufacturers are confronted with the multi-objective optimisation problem of sizing the drive-train components. In this paper, we evaluated Particle Swarm Optimisation (PSO) for solving this problem. The results showed that PSO performs significantly better than competing methods. Parameter sensitivities indicated that the optimal solution, the vehicle performance constraints, and the preference between fuel consumption and production cost are intimately coupled. Finally, a Pareto analysis confirmed that a relatively small increase in cost accounts for a majority of the total fuel saving potential.

Particle swarm optimisation for hybrid electric drive-train sizing

This paper describes an approach for the optimization of parallel hybrid electric vehicle (HEV) component sizing using particle swarm optimization (PSO) algorithm In this study, the fitness function is defined to minimize the vehicle engine fuel consumption (FC) and emissions The driving performance requirements are then considered as constraints Finally, the optimization process is performed over the test procure TEST CYCLE HYWT, in which a vehicle model named ADVISOR is used as the analysis tool The results from the computer simulation show the effectiveness of the approach and reduction in FC, emissions while ensuring that the vehicle performance is not sacrificed

Application of Particle Swarm Optimization for component sizes in parallel Hybrid Electric Vehicles

The invention discloses a hybrid motorcycle; an engine drives the rear-wheel of the motorcycle through a transmission system, and a motor is arranged on the front-wheel of the motorcycle to form the two-wheel drive of the motorcycle; a first rotating-speed sensor is also arranged on the front-wheel of the motorcycle; an overrun clutch is arranged in the transmission system of the rear-wheel of the motorcycle for realizing the shift of driving way between the motor drive and the engine drive; a hybrid speed-governing handle which is connected with a throttle in the engine is arranged on the handle of the motorcycle; an energy accumulator and a controller are arranged in the motorcycle; after processing the collected signal of the hybrid speed-governing handle and the vehicle condition signal, the controller controls the engine and the motor to drive the motorcycle separately or together, and charges the energy accumulator during recycling energy; a second rotating-speed sensor is arranged on the engine; the invention makes a little change on the prior motorcycle, and the realization is easy.

Mixed dynamical motorcycle

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Application of Particle Swarm Optimization for component sizes in parallel Hybrid Electric Vehicles

Mixed dynamical motorcycle