Hybrid Electric Vehicle Powertrain Research Articles

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

An efficient optimal sizing strategy for a hybrid electric air-ground vehicle using adaptive spiral optimization algorithm

This paper proposes an efficient optimal sizing strategy for hybrid electric air-ground vehicles. First, to optimize the cost and energy consumption while maintaining driving performance, major design parameters, such as the number of battery packs, power of the engine-generator set, and air/ground electrical motors, are chosen as optimization variables. Second, an adaptive spiral optimization algorithm is proposed in the presented optimal sizing strategy. Third, unique update and punish mechanisms are designed to prevent the sizing process from being stuck in local minima, leading to improved searching efficiency. The simulation is carried out in MATLAB/Simulink using the Bogacki-Shampine solver. And the relevant coding work is implemented in MATLAB. Optimization results show that, compared to the initial sizing, the proposed strategy improves cost, energy consumption, ground-driving, and flying performance by 15.16%, 4.68%, 11.78%, and 1.43%, respectively. Finally, the proposed algorithm outperforms the original spiral optimization algorithm, enhanced genetic algorithm, and adaptive particle swarm optimization algorithm in terms of improving the objective function by 1.9%, 1.18%, and 2.04%, respectively. Theoretical insights for a hybrid electric air-ground vehicle powertrain sizing problem might be provided by the proposed strategy.

Decentralized Optimal Energy Efficiency Improvement Strategy for Large-Scale Connected HEVs

In this paper, the energy efficiency improvement optimization strategy is explored for large-scale hybrid electric vehicles (HEVs) in a connected environment. Both reducing vehicle speed fluctuation and increasing high efficiency working conditions of HEV powertrain are beneficial for fuel economy improvement. A hierarchical optimization strategy is designed in this paper, where the speed consensus problem is considered in the upper layer and an energy management problem is considered in the lower layer. To deal with optimization of large-scale HEVs, mean field game (MFG) is employed for speed consensus. Meanwhile, model predictive MFG-based control scheme is developed with consideration of distribution predication error caused by the uncertainties of road and traffic. With connection of vehicle to everything (V2X), the real-time distribution can be calculated in the big data center and sent back to individual HEV for model predictive MFG-based controller. Simulations are conducted to show the effectiveness of the proposed strategy.

Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints

Advanced computer-aided engineering tools are urgently needed to foster the development of electrified road vehicles that would enable abating fuel consumption and pollutant emissions of the transport sector. Concerning plug-in hybrid electric vehicles (HEVs), implementing an energy management strategy that can rapidly estimate near-optimal powertrain control trajectories while effectively dealing with broaded battery state-of-charge (SOC) window utilization and smooth HEV driving requirements still needs extensive development. To overcome the highlighted drawback, this paper introduces a formulation of the slope-weighted energy-based rapid control analysis (SERCA) algorithm which can rapidly identify near-optimal plug-in HEV control trajectories while complying with SOC boundaries and limiting the number of thermal engine activations and gear shifts. The HEV numerical model is introduced first, followed by formulating the optimal plug-in HEV control problem with smooth driving constraints and describing the dedicated SERCA based control approach. A performed case study demonstrates that SERCA can identify smooth driving constrained near-optimal HEV control trajectories for a 1.5 hours-long real-world driving mission within two minutes on a desktop computer, while a global optimal control approach such as dynamic programming (DP) is found to require more than 10 hours to perform the same task. On the other hand, compared with the global optimal reference provided by DP, the increase in estimated plug-in HEV operative cost in terms of fuel and electrical energy consumption associated to SERCA is always contained within few percentage points. The proposed methodology can accelerate HEV powertrain design and on-board supervisory controller development procedures.

Spark-ignition engine speed profile optimization for maximizing the net indicated efficiency and quantitative analysis of the optimal speed profile

To meet the tightened fuel economy or greenhouse gas emission standards in the transportation sector, many studies focus on the vehicle powertrain. Because hydrocarbon-based fuel is mainly used, an increase in the internal combustion engine efficiency is essential to meet the future regulations. This study discusses the improvement of spark-ignition engine efficiency through intracycle speed profile modulation. An intracycle speed profile modulating strategy could be adopted in the series hybrid electric vehicle powertrain architecture because the internal combustion engine shaft is not directly connected to the vehicle driveshaft. Under the wide-open throttle condition, the net indicated efficiency with the optimized profile increases by 1.3 %p compared to the peak efficiency achievable in conventional operation over 1,000–5,000 rpm and increases by 5 %p compared to the conventional operation with the same cycle average speed. This leads to the conclusion that the engine speed modulation in a spark-ignition engine would be largely effective near knock-limited operating conditions of a conventional engine. The efficiency gain is achieved by improving various performance parameters, conventionally in a trade-off relation. Combustion phasing is improved to the level of knock-free operating condition. In the optimal speed profile, the intake and compression process speed profiles are modulated to suppress knocking. The expansion process speed profile is mainly influenced by heat loss, and the gas exchange process speed profile is related to the pumping work. These propositions lay the foundational knowledge for further investigation into the topic in the future, including the hardware implementation.

Optimal Hybridization of Conventional ICE Vehicles

Most commercially available hybrid electric vehicle (HEV) drivetrains are made of small internal combustion (IC) engines and large electric drives to improve fuel economy. They usually have higher cost than the conventional IC-engine-based vehicles because of the high costs of the electric drives. This paper proposes a hybridized powertrain composed of the original full-size engine of the vehicle and a universally optimum size parallel electric drive. The dynamic programming (DP) algorithm was used to obtain the sensitivity of the maximum miles per gallon (MPG) values versus the power rating of the electric drive. This sensitivity was then analyzed to determine the optimal window of the electric drive power ratings. This was proven to be universal for all passenger cars of various masses and engine powers. The fuel economy and vehicle performance of this HEV was compared with those of the 2019 Toyota Corolla, a conventional IC-engine-based vehicle, and the 2019 Toyota Prius, a commercially available HEV. The results showed that the proposed universally optimized HEV powertrain achieved better fuel economy and vehicle performance than both the original ICE and HEV vehicles, at low additional vehicle cost.

Dynamical analysis of series hybrid electric vehicle powertrain with torsional vibration: Antimonotonicity and coexisting attractors

In this paper, nonlinear dynamics of the torsional vibrations of the Series Hybrid Electric Vehicle (SHEV) powertrain are explored. The mathematical equation of the SHEV is obtained by considering both internal and external excitations. We examine the effects of rotational speed, load fluctuation, and road fluctuation on the dynamics of the proposed model. Preliminary analysis based on Hamiltonian energy shows that there is sufficient energy to maintain continuous oscillation behaviors. Our study shows that the interaction between internal and external perturbations originates an important set of phenomena, including chaos, when varying system parameters. More interestingly, the exciting phenomenon of multistability is investigated for the first time on the proposed SHEV model. Furthermore, when increasing excitation force amplitude in some specific regions, an interesting scenario of antimonotonicity is found. • Nonlinear dynamics of the torsional vibrations of the Series Hybrid Electric Vehicle (SHEV) powertrain are explored. • Both internal and external excitations are considered in the mathematical model. • The Hamiltonian energy shows that there is sufficient energy to maintain continuous oscillation behaviors. • The exciting phenomenon of multi-stability is investigated on the proposed SHEV model. • Anti-monotonicity and period-doubling bifurcations are also reported when adjusting the excitation force amplitude of the system.

Co-Optimization Scheme for the Powertrain and Exhaust Emission Control System of Hybrid Electric Vehicles Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. However, the optimization design for HEVs is complicated due to the presence of nonlinear dynamics and complicated integration of the HEV systems. Moreover, there is a trade-off between fuel optimization and emission reduction. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. The proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. Moreover, the ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. At first, the Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from a number of real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model employed for the optimization for both powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models, which are learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using the NN model, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

Exploitation of a Particle Swarm Optimization Algorithm for Designing a Lightweight Parallel Hybrid Electric Vehicle

The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehicle-level energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor.

Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Reservoir computing refers to a computational framework based on recurrent neural networks that can process time-series data. In an echo state network (ESN), which is a type of reservoir computing framework, the reservoir consists of a recursive network of artificial neurons with nonlinear activation functions. A model predictive control (MPC) technique can determine the control signals by solving the optimization problem of a system using the finite-time domain of each control period. However, real-time optimization cannot be achieved unless the optimal control problem can be solved within the next control period. To overcome this limitation, we propose a new control method based on MPC that explicitly incorporates the predicted disturbance of a time-varying trajectory using ESN to achieve the active vibration control of hybrid electric vehicle (HEV) powertrains. Once the ESN has been trained, the associated MPC explicitly satisfies the constraints over a moving horizon without further training. Instead of completing the real-time optimization within the control period, ESN predicts the future disturbance and applies it to the MPC in the future control period. Based on the predicted future disturbance, the system calculates the optimal control signals required for the future. Thus, real-time control can be realized because the optimal signals are determined before the subsequent control period occurs. The proposed method can be implemented in MPC even if the control period is too short to optimize as long as the disturbance can be reasonably measured and predicted. In this study, the simulation approach was demonstrated using the engine start condition in an HEV powertrain. The importance of this study is that the limitation of MPC relevant to real-time optimization can be relaxed by applying our proposed method.

Multi-objective optimization of a dual mass flywheel with centrifugal pendulum vibration absorbers in a single-shaft parallel hybrid electric vehicle powertrain for torsional vibration reduction

This research deals with an optimization of a dual mass flywheel (DMF) with centrifugal pendulum vibration absorbers (CPVAs) used in a single-shaft parallel hybrid electric vehicle (HEV) powertrain for the reduction of the torsional vibration at the rotor of the electric motor and the total moment of inertia of the DMF and CPVAs. Unlike the previous studies, this research considers the overall behavior of an entire powertrain with an in-line four-cylinder engine and a five-speed automatic transmission for the optimization of the DMF with CPVAs. For this purpose, the multibody dynamics model of the HEV powertrain is constructed and used to analyze the transient response of the system for a given gas pressure map and acceleration condition of the vehicle. Then, a multi-objective genetic algorithm is applied to determine the Pareto optimal front that yields the design parameter values of the DMF and CPVAs effective in the torsional vibration reduction and inertia minimization. The result of the optimization is compared with the HEV powertrain system with a flywheel only, and shows that the vibrations transmitted to the motor are significantly reduced in all the solutions of the Pareto front. By using the method proposed in this research, various parameter combinations of the DMF and CPVAs can be considered in the early design process.

Multistability analysis and nonlinear vibration for generator set in series hybrid electric vehicle through electromechanical coupling.

The non-linear analysis of undesired vibrations observed on hybrid electric vehicle (HEV) powertrains is hardly developed in the literature. In this paper, a mathematical modeling of the vibrations observed at the level of the electromechanical coupling between the internal combustion engine and the generator in the series architecture of HEVs, named (SHEVs), is established using the Lagrangian theory. The stability and instability motions of this SHEV are perfectly detailed using amplitude-frequency response curves. An analysis of the electromagnetic torque amplitude of the new SHEV demonstrates the presence of multistability with the coexistence of two or three different types of attractors. In addition, this new SHEV model has other dynamic regimes of chaotic and periodic oscillations. Coexisting bifurcations with parallel branches, hysteresis, and period-doubling are also discovered. A unique contribution of this work is the abundance and complicated dynamical behaviors found in such types of systems compared with some rare cases previously reported on HEV powertrain models. The simulation results obtained using non-linear analysis tools sufficiently demonstrate that the objectives of this paper are achieved.

EVALUATION OF PLUG-IN PARALLEL HEV TOPOLOGIES USING OPTIMAL CONTROL METHODS AND VEHICLE DYNAMICS SIMULATION


 
 
 
 Hybrid electric vehicle (HEV) powertrains with parallel topologies are among the frequently used layouts, because of their easy applicability on an existing conventional powertrain, by the addition of hybrid modules with mild, full, or a plug-in capability. This paper investigates three of such parallel HEV topologies: P2, P3, and P4; all in a plug-in variant, to find-out which one performs best. Apart from the topology consideration, one of the other common questions or challenges in HEV development is the ICE concept selection. To address it, the paper combines the three HEV topologies with three technologically different internal combustion engines, all with the same power outputs. Then, all the powertrain and ICE combinations are tested in homologation driving cycles and vehicle dynamics simulation test – different acceleration tests – giving a holistic methodology suitable for thorough HEV topology evaluation, identifying all possible hybridization benefits. To find the maximum CO2 potential, it is convenient to exclude the effect of the energy management control strategy on the CO2 result in a charge sustaining driving cycle; therefore, to use some optimal control method. For this task, the paper compares the Equivalent Consumption Minimization Strategy, that realizes a Pontryagin’s minimum principle against the Dynamic Programming optimal control method, which is based on Bellman’s principle of optimality. Both control methods are available as a part of GT-Suite 0D/1D/3D multi-physics CAE simulation software, that is used in our whole study.
 
 
 

Novel strategies to reduce engine emissions and improve energy efficiency in hybrid vehicles

Hybrid electric vehicles (HEV) can provide a same power as gasoline and diesel-powered vehicles while also reducing fuel consumption by 40–50%. However, due to a complex system of HEV, the power control strategies are used to improve the work efficiency, fuel economy and to reduce exhaust emissions. In the present work, four parameters are studied with the main focus on the optimization of power system of a parallel hybrid vehicle. These parameters are the influence of fuzzy logic algorithm on hybrid electric vehicle power train that is acceleration performance, climbing performance, engine efficiency and exhaust gas emissions. First, the vehicle’s power train is optimized using the Advanced Vehicle Simulator (ADVISOR). Secondly, by ensuring acceleration and climbing performance, the vehicle manufacturing cost is reduced by reducing the weight of the vehicle. Thirdly, two driving road test conditions, Extra-Urban Driving Cycle (EUDC) and Urban Dynamometer Driving Schedule (UDDS) are used to enhance the fuel consumption efficiency of the vehicle. Using a fuzzy logic algorithm for random operations, the operating range of an internal combustion engine is controlled as much as possible in the most efficient range. Through the control of 121 different algorithms, the operating efficiency of the powertrain is optimized to reduce exhaust emissions and improve fuel efficiency. The results showed that the proposed strategy can reduce fuel consumption by 5%, CO by 50% and improved the operating efficiency of engine by 15%. Not only this control strategy optimizes the efficiency of powertrain, but also the efficiency of the internal combustion engine. Moreover, the motor and battery pack itself can be optimized. These hybrid vehicles can minimize the fuel demand and are the best substitute for the classic internal combustion engines.

Integrated Predictive Powertrain Control for a Multimode Plug-in Hybrid Electric Vehicle

Due to the complexity of a multimode plug-in hybrid electric vehicle (PHEV) powertrain, the energy management strategy of the said powertrain is a prime candidate for the application of optimal control methods. This article presents a predictive control strategy for optimal mode selection and powertrain control for a multimode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This article also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using nonlinear model predictive control to create a real-time integrated predictive powertrain controller (IPPC) responsible for all aspects of multimode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multimode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4%–10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation.

Robust Combined Design and Control Optimization of Hybrid-Electric Vehicles Using MDSDO

Identifying a true, system-level optimal solution for hybrid-electric vehicle (HEV) powertrains requires methods such as combined design and control optimization (co-design) to account for the coupling between powertrain design and optimal energy management strategies. Despite the widespread application of co-design techniques to HEV powertrains in recent years, limited research has been done to account for the inherent uncertainties present in these systems. This is problematic as such uncertainties may not only impact HEV powertrain component sizing and control strategies, but also overall vehicle performance and cost-both of which are critically-important in the fiercely-competitive automotive industry. One way to address this issue is to apply a stochastic dynamic system optimization technique known as robust multidisciplinary dynamic system design optimization (R-MDSDO) to the HEV powertrain co-design problem. This method ensures that the HEV powertrain co-design solution, including its system performance and cost, explicitly accounts for random variations within the system and its problem formulation using concepts from robust design optimization. Therefore, in this study, we formulate and solve a robust co-design problem for a power-split HEV powertrain using R-MDSDO to identify the optimal component designs, state trajectories, and control trajectories such that the vehicle powertrain cost is minimized. The robust solution is then compared to the solution from the associated deterministic problem, with the results indicating that accounting for system uncertainty has a significant impact on HEV powertrain co-design.

Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

Multi-objective optimization design and control of plug-in hybrid electric vehicle powertrain for minimization of energy consumption, exhaust emissions and battery degradation

Plug-in hybrid electric vehicles (PHEV) offer an attractive alternative to achieve the ambitious goals set by strong policies focused on near-term air quality and fuel-efficient road transportation. This paper provides a comprehensive study regarding the PHEV’s optimum powertrain design, by means of a multi-criteria analysis carried out by the interactive adaptive-weight genetic algorithm approach. The optimization aims to simultaneously minimize the PHEV’s fuel consumption, exhaust emissions, electric powertrain size, battery state of health, charging time and costs. To achieve these objectives, several PHEV’s design parameters are optimized such as in-wheel electric motors’ torque curves, battery voltage and capacity. The drivetrain is also optimized according to the determination of the best configuration of gearbox and differential gear ratios, taking into account constructive constraints. Furthermore, the fuzzy logic controllers responsible for the engine/electric motors power-split management and gear shifting control are included in the multi-objective optimization in order to define the best membership functions, rules and respective weights. To guarantee robust solutions, the PHEV is optimized under different driving conditions, which is given by the combination of the FTP-75, HWFET and US06 driving cycles. To evaluate the optimum PHEV performance, it is also simulated under the WLTC driving cycle and a real-world driving cycle based on the Campinas city. The best trade-off configuration results in 39.57% decrease in vehicle travel cost along with 43.39% carbon monoxide (CO), 45.13% unburned hydrocarbons (HC) and 72.64% nitrogen oxides (NOx) emissions reduction under the combined driving cycle.

A Design Methodology for Hybrid Electric Vehicle Powertrain Configurations With Planetary Gear Sets

Abstract Increasingly strict emission and fuel economy standards stimulate the researches on hybrid electric vehicle techniques in the automobile industry and one of the most important techniques is the design of powertrain configurations. In this paper, a theoretical design methodology for hybrid electric vehicle powertrain configurations is proposed to find the configurations with excellent performance in a large pool of configurations. There are two main parts in a powertrain configuration, power/coupling devices (engine, electric machine, wheel, and planetary gear set) and mechanical connections between these devices. Different connections will lead to configurations having different performances. This paper divides all connections in configurations into three categories and a novel matrix representation method is developed to express these kinds of connections to reflect system dynamics and physical structure of configurations. With the support of the matrix representation method, configuration selections from large pools can automatically be completed by computer and manual calculation and comparison can be avoided, which saves much energy and time. Finally, the proposed method is vigorously verified by simulations.