The Design of Control Strategy for Blended Series-Parallel Power-Split PHEV – a Simulation Study

Electric Vehicles (EVs) have been extensively researched to reduce the fuel consumption and tailpipe emission. The series-parallel power-split Plug-in Electric Vehicle (PHEV) has been considered as one of the most suitable candidates. It contains both an internal combustion engine (ICE) and an electrical storage system (ESS) to achieve a better driving performance. The energy management system (EMS) is significant for a PHEV to improve the efficiency of the whole system. Electric vehicle mode (EV), charging depletion (CD) and charging sustaining (CS) modes will be discussed to build a control strategy in this study. This control strategy will be implemented with the state of charge (SoC) to show its impact through a simulation study

Plug-in hybrid electric vehicle emissions impacts on control strategy and fuel economy

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, at the expense of increased tailpipe emissions due to multiple cold start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.The dissertation research focuses on the design of a vehicle supervisory control system for a pre-transmission parallel PHEV powertrain architecture. Energy management strategies are evaluated and implemented in a virtual environment for preliminary assessment of petroleum displacement benefits and rudimentary drivability issues. This baseline vehicle supervisory control strategy, developed as a result of this assessment, is implemented and tested on actual hardware in a controlled laboratory environment over a baseline test cycle. Engine cold start events are aggressively addressed in the development of this control system, which lead to enhanced pre-warming and energy-based engine warming algorithms that provide substantial reductions in tailpipe emissions over the baseline supervisory control strategy.The flexibility of the PHEV powertrain allows for decreased emissions during any engine starting event through powertrain torque shaping algorithms that eliminate high engine torque transients during these periods. The results of the dissertation research show that PHEVs do have the potential for substantial reductions in fuel consumption, while remaining environmentally friendly. Tailpipe emissions from a representative PHEV test platform have been reduced to acceptable levels through the development and refinement of vehicle supervisory control methods only. Impacts on fuel consumption are minimal for the emissions reduction techniques that are implemented, while in some cases, substantial fuel consumption reductions are observed

Development of an Adaptive Efficient Thermal/Electric Skipping Control Strategy Applied to a Parallel Plug-in Hybrid Electric Vehicle

In recent years automobile manufacturers focused on an increasing degree of electrification of the powertrains with the aim to reduce pollutants and CO2 emissions. Despite more complex design processes and control strategies, these powertrains offer improved fuel exploitation compared to conventional vehicles thanks to intelligent energy management. A simulation study is here presented aiming at developing a new control strategy for a P3 parallel plug-in hybrid electric vehicle. The simulation model is implemented using vehicle modeling and simulation toolboxes in MATLAB/Simulink. The proposed control strategy is based on an alternative utilization of the electric motor and thermal engine to satisfy the vehicle power demand at the wheels (Efficient Thermal/Electric Skipping Strategy-ETESS). The choice between the two units is realized through a comparison between two equivalent fuel rates, one related to the thermal engine and the other related to the electric consumption. An adaptive function is introduced to develop a charge-blended control strategy. The novel adaptive control strategy (A-ETESS) is applied to estimate fuel consumption along different driving cycles. The control algorithm is implemented on a dedicated microcontroller unit performing a Processor-In-the-Loop (PIL) simulation. To demonstrate the reliability and effectiveness of the A-ETESS, the same adaptive function is built on the Equivalent Consumption Minimization Strategy (ECMS). The PIL results showed that the proposed strategy ensures a fuel economy similar to ECMS (worse of about 2% on average) and a computational effort reduced by 99% on average. This last feature reveals the potential for real-time on-vehicle applications

Hybrid Electric Vehicle Energy Management Strategy with Consideration of Battery Aging

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Finding the appropriate equivalence factor often required prior knowledge of the entire drive cycle. While using the appropriate equivalence factor might miss the opportunities for fuel savings under certain conditions. Therefore, an adaptive control law of equivalence factor called Catch Energy Saving Opportunity (CESO) has been introduced in this work to make the proposed aging ECMS real-time implementable. In order to better understand the impact of the developed optimal strategies on battery aging in HEVs, systematic analysis has been performed to find relations between fuel economy, battery aging and the optimization decisions when using ECMS. Therefore, the varies equivalence factors, state of charge constraints and battery temperatures are observed and analyzed under different Combined Drive-cycles (CDs). The CDs are formulated to test the energy management strategy and battery aging with weights on city and highway drive. In addition, rule-based control in charge-depletion mode aimed to improve battery aging has been simulated in a HEV truck. The simulation results show that, the fuel consumed and battery aging degradation during varied operation could be significantly improved by using a simple control rule in charge-depletion mode. This further indicates the benefits of implementing a battery aging term which impacts the control decision in charge-sustaining ECMS. Based on the analysis results, an aging ECMS has been developed by adding a battery aging term as a cost to the battery. The simulation results showed that this optimal energy management strategy improves battery aging significantly with little or no penalty in fuel economy. In addition, aging CESO ECMS, a real-time optimal strategy, has been developed based on the proposed aging ECMS. The simulation results show that aging CESO ECMS improvs upon the basic aging ECMS performance

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

Featured Application This work is useful for researchers interested in the study of energy management systems for hybrid electric vehicles. In addition, it is interesting for institutions related to the market of this type of vehicle. The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs