Plug-in Hybrid Electric Powertrain Research Articles

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine

The dedicated hybrid engines (DHEs) with dual-mode combustion technology can drastically reduce the fuel consumption and emissions while guarantee the power density. This paper aims to investigate the optimal control of such DHE-based plug-in hybrid electric vehicles (PHEVs) under real driving conditions, with minimum fuel penalties caused by transient engine dynamics. For this purpose, the benefits brought by artificial intelligent control and traffic preview in terms of energy efficiency can be combined with the advantages of advanced combustion engine. This paper presents a hierarchical energy management strategy (HEMS) to realize the synergy of global and instantaneous optimization. At the cloud level of HEMS, dynamic programming is applied to obtain optimal combustion mode and state of charge reference trajectories in a receding horizon. At the powertrain level, deep reinforcement learning with a ranking-prioritized experience replay algorithm is used to output optimal engine power and combustion mode for the energy management. To evaluate the proposed strategy, a dual-mode engine with homogeneous charge compression ignition and spark ignition systems is tested and mapped, with which the PHEV is modeled in GT-Suite and Matlab/Simulink. Comprehensive experiments are carried out to verify the optimality, generalization and robustness based on a standard driving cycle and a real-world driving cycle in China with GPS data recorded. The results show that the HEMS avoids frequent switching of combustion modes and outperforms the conventional methods by more than 4% and 10% in terms of fuel economy and NOx emissions, respectively, with random initial and terminal conditions.

Simulating the battery price and the car-mix in key electro-mobility markets via model coupling

ABSTRACT This paper describes the modelling process of soft-linking two system dynamics models of the automotive ecosystem: the Powertrain Technology Transition Market Agent model and the Transport, Energy, Economics, Environment model. The objective of this work is to explore future battery electric and plug-in hybrid electric powertrain deployment in key electro-mobility markets: China, European Union, India, Japan, Norway and the United States. In the “Alternative” scenario, a combined stock of almost 130 million electric cars in 2030 is simulated. Yet, conventional car technology continues to dominate the car-mix. By soft-linking the two models and adding information from the electric bus market, a more realistic representation of the historical battery price evolution is obtained. Due to the uncertain evolution of the battery price, sensitivity analyses are performed. We conclude that the battery price remains a crucial explanatory variable for annual electric car sales in simulation exercises.

Model-Based Optimization of a Plug-In Hybrid Electric Powertrain with Multimode Transmission

Plug-in hybrid electric vehicles are developed in order to reduce the fuel consumption and the emission of carbon dioxide. Besides the series, parallel and power split configurations are commonly used for conventional hybrid electric vehicles, and multimode transmissions are used for plug-in hybrid electric vehicles, which are able to switch between different modes like parallel or series operation of the combustion engine and electric motor. Several concepts have already been discussed and presented. These concepts comprise novel structures and multi-speed operation for the combustion engine and the electric motor, respectively. For improving the fuel and energy consumption, model-based optimizations of multimode transmissions are performed. In the first step of the optimization, the optimal number of gears and transmission ratios, as well as the corresponding fuel and energy savings, are estimated. Based on these results, a new multimode transmission concept with two-speed transmissions for the combustion engine and the electric motor has been developed. The knowledge of the concrete concept enables the further optimizations of the transmission ratios and the transmission control. In order to prove the benefit of the new and optimized transmission concept, powertrain simulations have been carried out. The new powertrain concept is compared to a powertrain concept with single-speed transmissions for the internal combustion engine (ICE) and electric motor operation. The new transmission concept enables a significant improvement of the fuel consumption.

Well-to-Wheel analysis of fossil energy use and greenhouse gas emissions for conventional, hybrid-electric and plug-in hybrid-electric city buses in the BRT system in Curitiba, Brazil

This study estimates Well-to-Wheel (WTW) fossil energy use and greenhouse gas (GHG) emissions for six types of city buses with conventional, hybrid-electric and plug-in hybrid-electric powertrains, including two-axle, articulated and bi-articulated chassis in the BRT (Bus Rapid Transit) system in Curitiba, Brazil. Particular emphasis is put on the operation phase (Tank-to-Wheel, TTW) of the city buses using the Advanced Vehicle Simulator (ADVISOR). The simulations are based on real-world driving patterns collected from Curitiba, comprising 42 driving cycles that represent city bus operation on seven BRT routes with six operation times for each.Hybrid-electric and plug-in hybrid-electric two-axle city buses use 30% and 75% less WTW fossil energy per distance compared to a conventional two-axle city bus (19.46MJfossil,WTW/km). This gives an absolute reduction of 1115gCO2e,WTW/km in WTW GHG emissions when operating a plug-in hybrid-electric city bus instead of a conventional two-axle city bus (1539gCO2e,WTW/km). However, a conventional bi-articulated city bus can be environment-friendlier than hybrid-electric city buses in terms of WTW fossil energy use and WTW GHG emissions per passenger-distance, if its passenger capacity is sufficiently utilised. Nonetheless, the plug-in hybrid-electric city bus remains the most energy-efficient and less polluting option. Hybrid-electric and plug-in hybrid-electric powertrains offer the possibility to achieve much higher levels of decarbonisation in the BRT system in Curitiba than the blending mandate of 7%vol biodiesel into diesel implemented in Brazil in 2016. In addition, the simulations show that TTW energy use can considerably vary by up to 77% between different operation times, BRT routes and types of city buses.In conclusion, advanced powertrains and large passenger capacity utilisation can promote sustainability in Curitiba’s BRT system. The results of this analysis provide important insights for decision makers both in Curitiba and other cities with similar conditions.

Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing

This paper describes the energy management controller design of a mid-sized vehicle driven by a fuel cell/battery plug-in hybrid powertrain, where an experimentally validated hight temperature polymer electrolyte membrane fuel cell model is used. The power management strategy is derived by the application of the Pontryagin’s Minimum Principle, where the control parameter is adapted by using feedback information on the state of charge and total trip length forecast as a function of a moving average of past information about the driving cycle speed. The strategy we propose aims at achieving a real time sub-optimal solution of the control problem which is cast into the minimization of the consumed fuel. The vehicle is also equipped by an auto-thermal reformer and, in order to minimize the hydrogen buffer size, the control algorithm is subject to constraints on the maximum hydrogen buffer level. A comparative analysis of the proposed strategy against the optimal one is conducted and results are reported. The obtained fuel consumptions are also compared to those obtained by the same vehicle, powered by an internal combustion engine and by a plug-in hybrid electric powertrain.

Two-step optimal energy management strategy for single-shaft series-parallel powertrain

The series-parallel architecture of the plug-in hybrid electric powertrain has attracted wide attentions in recent years for its flexible and highly efficient operating modes. However, despite the improvement of the vehicle fuel economy, it has been gradually facing more challenges on the design of the optimal controller due to the complex structure and the fast depletion of the battery. In this paper, a two-step optimal energy management strategy is proposed for a novel single-shaft series-parallel powertrain. In the first step, an equivalent method is adopted, in which two motors are equivalently regarded as one. After detailed analysis of the operating modes, various objective functions are established to pre-optimize the power split between engine and motor or two motors. In the second step, a stochastic dynamic programming (SDP) is adopted to optimize the power split between the engine and the equivalent motor, and the optimal combination of the operating modes. Then coupled with the pre-optimized results of the first step, the optimal power split among the engine and two motors could be obtained and then constructed as simple lookup-tables, which have great potential for practical applications. Finally, the preliminary test about the real-time performance of the optimal results is developed on the hardware-in-the-loop (HIL) system.

Optimizing the design of a plug-in hybrid electric vehicle from the early phase: an advanced sizing methodology

ABSTRACTA plug-in hybrid electric powertrain, as one of the most promising solutions to increase the fuel economy and to meet the stringent requirements of low emissions urban zones, has been investigated and developed for a light duty commercial vehicle application in this work. The plug-in hybrid electric powertrain combines an advanced small diesel internal combustion engine with a high energy battery pack, capable to assure an extended range in pure electric mode for specific areas, like the low/zero emissions urban zones. Since the right size of the powertrain components is essential to fully exploit the benefits of the hybridization, an advanced methodology has been proposed to optimize the design of the plug-in hybrid powertrain at an early phase. This methodology is based on the genetic algorithm approach for the choice of the powertrain component characteristics, combined with a quasi-optimal energy management strategy that is the Equivalent fuel Consumption Management Strategy (ECMS). The perfor...

Real-time predictive control strategy for a plug-in hybrid electric powertrain

Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.

Component sizing of a plug-in hybrid electric powertrain via convex optimization

This paper presents a novel convex modeling approach which allows for a simultaneous optimization of battery size and energy management of a plug-in hybrid powertrain by solving a semidefinite convex problem. The studied powertrain belongs to a city bus which is driven along a perfectly known bus line with fixed charging infrastructure. The purpose of the paper is to present the convexifying methodology and validate the necessary approximations by comparing with results obtained by Dynamic Programming when using the original nonlinear, non-convex, mixed-integer models. The comparison clearly shows the importance of the gear and engine on/off decisions, and it also shows that the convex optimization and Dynamic Programming point toward similar battery size and operating cost when the same gear and engine on/off heuristics are used. The main conclusion in the paper is that due to the low computation time, the convex modeling approach enables optimization of problems with two or more state variables, e.g. allowing for thermal models of the components; or to include more sizing variables, e.g. sizing of the engine and the electric machine simultaneously.

Economic Assessment of Greenhouse Gas Emissions Reduction by Vehicle Lightweighting Using Aluminum and High-Strength Steel

Summary The life cycle greenhouse gas (GHG) reduction benefits of vehicle lightweighting (LW) were evaluated in a companion article. This article provides an economic assessment of vehicle LW with aluminum and high-strength steel. Relevant cost information taken from the literature is synthesized, compiled, and formed into estimates of GHG reduction costs through LW. GHG emissions associated with vehicle LW scenarios between 6% and 23% are analyzed alongside vehicle life cycle costs to achieve these LW levels. We use this information to estimate the cost to remove GHG emissions per metric ton by LW, and we further calculate the difference between added manufacturing cost and fuel cost savings from LW. The results show greater GHG savings derived from greater LW and added manufacturing costs as expected. The associated production costs are, however, disproportionately higher than the fuel cost savings associated with higher LW options. A sensitivity analysis of different vehicle classes confirms that vehicle LW is more cost-effective for larger vehicles. Also, the cost of GHG emissions reductions through lightweighting is compared with alternative GHG emissions reduction technologies for passenger vehicles, such as diesel, hybrid, and plug-in hybrid electric powertrains. The results find intensive LW to be a competitive and complementary approach relative to the technological alternatives within the automotive industry but more costly than GHG mitigation strategies available to other industries.