Fuzzy logic is used to define a new quantity called the battery working state (BWS), which is based on both battery terminal voltage and state of charge (SOC), to overcome the problem of battery over-discharge and associated damage resulting from inaccurate estimates of the SOC. The BWS is used by a fuzzy logic energy-management system of a plug-in series hybrid electric vehicle (HEV) to make a decision on the power split between the battery and the engine, based on the BWS and vehicle power demand, while controlling the engine to work in its fuel economic region. The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real battery in the loop. Simulation results are presented to demonstrate the performance of the proposed fuzzy logic energy-management system under different driving conditions and battery SOCs. The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging.

Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic

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

Hybrid Electric Vehicles (HEV) combine the power from an electric motor with that from an internal combustion engine to propel the vehicle. The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, the battery will need to be recharged using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, a rule-based energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for a number of predefined drive cycles. The proposed PHEV algorithm results are compared with those for HEV with similar battery capacity as PHEV.

/pdf/a-rule-based-energy-management-strategy-for-plug-in-hybrid-3em70t5bry.pdf

A rule-based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV)

Efficient operation technique has always been one of the common goals for researches both in automobile industrial and academic areas. With the great progress of automobile technology, hybrid electric vehicle/plug-in hybrid electric vehicle (HEV/PHEV) has already become the main achievement of transportation electrification, due to its excellent fuel-saving performance. Energy management strategy (EMS) is an important link during the HEV/PHEV design procedure, which can govern the energy flow between the fuel tank and the electric energy storage by solving the energy distribution problem. As the continuous development of intelligent connected vehicle technology, designing an efficient EMS with vehicle to infrastructure/vehicle to vehicle (V2I/V2V) information for HEV/PHEV is still a challenge and hot issue. This study presents a deep review of the various EMSs for both conventional HEV/PHEV and that using V2I/V2V information, providing a thorough survey of EMSs using different methodologies. In terms of single-vehicle and multi-vehicle scenarios, the EMSs for HEV/PHEV under intelligent transport system is in-depth reviewed. Finally, the challenges for future research are also identified. This study could provide a comprehensive reference for researchers in field of HEV/PHEV.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2019.0606

Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: review and recent advances under intelligent transportation system

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

SOH prediction of lithium battery based on IC curve feature and BP neural network

Plug-in hybrid electric vehicles (PHEVs) differ from hybrid electric vehicles (HEVs) with their ability to use off-board electricity generation to recharge their energy storage systems. In addition to possessing charge-sustaining HEV operation capability, PHEVs use the stored electrical energy during a charge-depleting (CD) operating period to displace a significant amount of petroleum consumption. The choice of CD operating strategy directly influences the benefit derived from the PHEV technology. This paper describes the application of the particle swarm optimization (PSO) algorithm for the optimization of the control parameters in plug-in hybrid electric vehicles (HEV). In this study, based on CD operating strategy, the fitness function is defined so as to maximize the vehicle engine fuel economy (FE). The driving performance requirements are then considered as constraints. The results from the computer simulation show the effectiveness of the approach and improvement in fuel economy (FE) while ensuring that the vehicle performance is not sacrificed.

Particle swarm optimization for plug-in hybrid electric vehicle control strategy parameter

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

This paper presents a novel parameter automation strategy for the particle swarm optimization algorithm; the normal cloud model is used to improve the performance of the particle swarm optimization algorithm. First, the normal cloud model is used to initialize the population; particles are no longer uniformly distributed throughout the search space. Second, one and the same normal cloud is used to nonlinearly, dynamically adjust inertia weight and update two random numbers in velocity update equation. Therefore, three components in the velocity update equation do interact in the PSO search process, which maintains the diversity of the population, provides balance between the global and local search abilities and makes the convergence faster. Experimental results are provided to show that the improved particle swarm optimization algorithm can successfully locate all optima on a small set of benchmark functions. A comparison of the improve algorithm with the standard particle swarm optimization algorithm is also made.

Particle swarm algorithm based on normal cloud

The nonlinearity and double salient pole of the switched reluctance motor make the control diﬃcult and the torque ﬂuctuation is large. To achieve good control performance, this study introduces a novel linearized fractional-order controller with the disturbance observer to regulate the speed and torque of a switched reluctance motor. Based on fractional diﬀerential calculus theory, the fractional-order proportional integral controller is designed. The disturbance observer is introduced to overcome the inﬂuence of nonlinearity on the switched reluctance motor and reduce the torque ﬂuctuation. By using the extended state in the disturbance observer, the disturbances are estimated and compensated to achieve linearization and determinism. The parameter values of the proposed controller are obtained by employing the trial-and-error method. The frequency-domain analysis of the proposed controller shows that it has a good suppression performance for medium- and high-frequency disturbances. Through simulation, the performance of the system is analyzed. The results show good performance of the controller in speed change, current, and torque ﬂuctuation reduction.

/pdf/linearized-fraction-order-control-and-stability-analyses-for-1qyn6gvl.pdf

Linearized Fraction Order Control and Stability Analyses for Switched Reluctance Motor Drives

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