Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.

Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective

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

Hybrid electric vehicles (HEVs) are one of the most viable technologies to achieve the goals of energy saving and environmental protection before a breakthrough in battery technology and fuel cell technology. Energy management strategy as a key technology of HEVs is studied extensively and deeply to improve the performance of HEVs and speed up the industrialization of HEVs. This paper quantitatively analyzes and evaluates current research status of energy management strategies for HEVs based on bibliometrics for the first time, through content analysis involving analysis of author keywords and abstracts. Then qualitative analysis is performed for all kinds of energy management strategies that are used in HEVs in detail, essential characteristics involving pros and cons, interconnections and improvement potential among various energy management strategies are revealed from the view of control theory. Finally, latest developing trends in energy management strategies of HEVs are presented to improve the performance of HEVs based on above quantitative analysis and qualitative analysis, covering driving cycle recognition/prediction algorithms, integrated multi-objective, coordinated optimization energy management strategies, good balance between computation complexity and optimization performance of energy management strategies, fair and credible evaluation system of energy management strategies. This paper not only first provides a comprehensive analysis of energy management strategies for HEVs, but also puts forward the emphasis and orientation of future study, which will broaden relevant researchers׳ vision and promote the development of a simple and practical energy management controller with low cost and high performance for HEVs.

A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics

Hybrid and electric vehicles have been demonstrated as auspicious solutions for ensuring improvements in fuel saving and emission reductions. From the system design perspective, there are numerous indicators affecting the performance of such vehicles, in which the powertrain type, component configuration, and energy management strategy (EMS) play a key role. Achieving an energy-efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation, which make the EMS the most crucial aspect of powertrain system design. Accordingly, in the present study, various powertrain systems and topologies of (plug-in) hybrid electric vehicles and full-electric vehicles are assessed. In addition, EMSs as applied in the literature are systematically surveyed for a qualitative investigation, classification, and comparison of existing approaches in terms of the principles, advantages, and drawbacks through a comprehensive review. Furthermore, potential challenges considering the gaps in research are addressed, and directives paving the way toward further development of powertrains and EMSs in all respects are thoroughly provided.

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Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies

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.

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

The coordination between the powertrain and control strategy has significant impacts on the operating performance of hybrid electric vehicles (HEVs). A comprehensive methodology based on Particle Swarm Optimization (PSO) is presented in this paper to achieve parameter optimization for both the powertrain and the control strategy, with the aim of reducing fuel consumption, exhaust emissions, and manufacturing costs of the HEV. The original multi-objective optimization problem is converted into a single-objective problem with a goal-attainment method, and the principal parameters of powertrain and control strategy are set as the optimized variables by PSO, with the dynamic performance index of HEVs being defined as the constraint condition. Computer simulations were carried out, which showed that the PSO scheme gives preferable results in comparison to the ADVISOR method. Therefore, fuel consumption and exhaust emissions of HEVs can be effectively reduced without sacrificing dynamic performance of HEVs.

PSO algorithm-based parameter optimization for HEV powertrain and its control strategy

By considering the effect of the driving cycle on the energy management strategy (EMS), a fuzzy EMS based on driving cycle recognition is proposed to improve the fuel economy of a parallel hybrid electric vehicle The EMS is composed of driving cycle recognition and a fuzzy torque distribution controller The current driving cycle is recognized by learning vector quantization in driving cycle recognition The torque of the engine and the motor is controlled by a fuzzy torque distribution controller based on the required torque of the hybrid powertrain and the battery state of charge The membership functions and rules of the fuzzy torque distribution controller are optimized simultaneously by using particle swarm optimization Based on the identification results of driving cycle recognition, the fuzzy torque distribution controller selects the corresponding membership function and rule to control the hybrid powertrain The simulation research based on ADVISOR demonstrates that this EMS improves fuel economy more effectively than fuzzy EMS without driving cycle recognition

Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition

The energy management strategy renders significant impacts on the operating performance of plug-in hybrid electric vehicles (PHEV), which are similar to the traditional hybrid electric vehicle. A comprehensive methodology based on Particle Swarm Optimization (PSO) is presented in the paper to achieve parameter optimization for the energy management strategy, with a view to reducing the fuel consumption of PHEV. The parameters of energy management strategy are set as the optimized variables by PSO, with dynamic performance index of PHEV being defined as the constraint condition. Computer simulations are hence carried out, which show the PSO scheme gives much preferable results to original energy management strategy, and thereby the fuel consumption of PHEV can be effectively reduced without sacrificing PHEV's dynamic performance.

PSO algorithm-based optimization of plug-in hybrid electric vehicle energy management strategy

Online receding horizon controller based on the principle of predictive control for parallel Hybrid Electric Vehicle is proposed in this paper. First of all, the structure of Hybrid Electric Vehicles is introduced, and the model of batteries and engine, etc is established. Secondly the energy management strategies based on predictive control algorithm is presented. At last the results of simulation experiments are analyzed compared with the rule-based control strategy in different conditions.

Research on predictive control based energy management strategy for Hybrid Electric Vehicle

Effective control strategy (EMS) is very important for high fuel economy, low emission and good driving performance of hybrid electric vehicles. First, the operation mode of parallel hybrid electric vehicles was analyzed and the rule-based EMS was introduced. Then the equivalent fuel consumption minimization strategy (ECMS) was modified by the variation of battery state of charge (SOC) and introduced to optimize the EMS. Finally, this EMS was implemented in JA1015 drive cycle and analyzed by advanced vehicle simulation software CRUISE. The simulation results indicate that the improved EMS reduces fuel consumption effectively by distributing the torque between engine and motor properly, and it also sustains the battery SOC in a proper range

Jian Wu

Papers

PSO algorithm-based parameter optimization for HEV powertrain and its control strategy

Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition

PSO algorithm-based optimization of plug-in hybrid electric vehicle energy management strategy

Research on predictive control based energy management strategy for Hybrid Electric Vehicle

An Improved Energy Management Strategy for Parallel Hybrid Electric Vehicle