HEV Powertrain Research Articles

Investigation on the influence of clutches on the EVT based HEV powertrain by efficient DP-PMP

Investigation on the influence of clutches on the EVT based HEV powertrain by efficient DP-PMP

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.

Drivability Improving Control During Mode Transition Process of Through-the-Road Hybrid Electric Vehicles

Drivability control in mode transition process of through-the-road hybrid electric vehicles (TTR HEVs) is focused in this article. There is more than one path of energy flow from the power sources to the wheels in TTR HEVs. The torque coupling is achieved through the road. The operation modes and transitions show more diversity than other HEVs. These distinguishing features imply the necessity of remodeling the TTR HEV powertrains for drivability control. In addition, how to handle the uncertainty resulting from clutch actuation, internal combustion engine (ICE) response lag, and modeling error is still a tough issue. To overcome these difficulties, first, operation modes and transition process of the concerned TTR HEV are discussed. The dynamic models of the front and rear powertrain are derived. Second, considering clutch torque change, ICE torque error, and model uncertainty, sliding mode controller (SMC) is designed to synchronize the rotate speed of the clutch driving and driven disks. Third, in order to reduce the reaching time as well as suppress the chattering near and on the sliding surface, a fuzzy sliding mode controller (FSMC) is proposed. Compared simulation results show that the proposed FSMC can improve vehicle drivability effectively.

Performance Analysis of a Hybrid Electric Vehicle with Multiple Converter Configuration

The use of electric vehicles and their various configurations is seen as a major alternative in efforts towards reducing pollutant emissions from motor vehicles that continue to use fossil fuels. Electric transport technology presents more efficient means of energy conversion in vehicles: electric (EV), hybrid (VH), and hybrid electric (HEV) vehicles. For example, the energy storage system in the latter can be made up of ultracapacitors (UCs), batteries (Bs), and fuel cells. This work focuses on HEVs powered by batteries and ultracapacitors. In particular, the multiple converter configuration (C-CM) for the HEV powertrain system is analyzed using electric models of the vehicle powertrain components. To analyze the multiple converter configuration, parameters of a vehicle taken from the literature and the electrical model of the configuration were developed. With the above, the proposed configuration was evaluated before driving cycles (CITY II and ECE) and the configuration performance was compared with respect to other configurations. In the C-CM model, limitations in the choice of the number of Bs and UCs were observed in the powertrain depending on the maximum power of both energy sources and vehicle load demand. The results show that more energy is extracted from the batteries in the ECE cycle than in the CITY taking into account that the batteries are used as the main power source. C-CM results compared to other configurations show that energy extracted from batteries in the CITY is the same across all configurations. While energy consumption is lower in the ECE, C-CM results were not very significant compared to other configurations. However, the C-MC has the advantage of having better power flow control due to having two converters, thus improving HEV safety.

Improving Efficiency of Hybrid Electric Vehicle Using Matrix Converters

The paper deals with a novel enhanced connection of AC/AC powertrain for HEV hybrid vehicles. The substantial contribution of such a connection is the absence of 4QC auxiliary converter needed for autonomous and hybrid operational modes and its compensation by power-lesser 0×5 matrix converter. The main advantages of a simplified connection are beside smaller auxiliary converter sizing and possible better efficiency of the HEV powertrain. So, powertrain operation in autonomous traction accu-battery modes uses direct 0×5 configuration of traction 3×5 MxC matrix converter and in hybrid modes of ICE engine and accu-battery, besides traction 3×5 MxC matrix converter, uses the auxiliary 0×5 matrix converter. Modeling and simulation using Matlab-Simulink environment of traction powertrain configuration in autonomous modes are presented in the paper, as well as all simulation experiment results.

Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle

One of the major issues for Parallel Hybrid Electric Vehicle (Parallel HEV) powertrain is the torsional vibration in the process of start condition, which is unavoidable. This article targets at reducing the damage caused by the torsional vibration with the method of Multi-Objective Optimization (MOO). The dynamic model of the parallel HEV powertrain is established by lumped mass method. Five design variables are selected from 19 parameters by the process of Design of Experiment (DOE), and are optimized by multi-objective downhill simplex optimization algorithm. Pareto Frontier is used to describe the relationship between the two objective functions, and one of the optimization data serves as the basics data for the powertrain modification. Finally, the results of optimization before and after optimization are compared by the test bench. Experimental results under the start condition show that the maximum torque of the optimized powertrain is decreased within the safe range, and the problem of shaft breaking on the originally powertrain is solved.

Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.

Design and Analysis of CAN Communication Network Applied in Hybrid Electric Vehicle

This paper briefly presents a Parallel Serial Hybrid Electric Vehicle structure combined with NAC institute, constructing a CAN Communication Network of Hybrid Electric Vehicle’s Powertrain and also defining the Communication protocol of the control Network. The Network constructed is tested on bench and prototype vehicle and by using CANstress, CANoe and some other Automotive network development tools, actual test and performance analysis have been conducted. The experiment result illustrates that the control network system arrives a high stable performance and real-time with low bus load and little error frames under high transmission speed and interference environment, and can meet requirement of HEV Powertrain of stability, real-time and accurate control. Besides that, we also constructs a Diagnostic Network, which fulfills the unified diagnostic services (UDS) defined by ISO14229.

USE OF GT-SUITE TO STUDY PERFORMANCE DIFFERENCES BETWEEN INTERNAL COMBUSTION ENGINE (ICE) AND HYBRID ELECTRIC VEHICLE (HEV) POWERTRAINS

ABSTRACTHybrid Electric Vehicles (HEVs) are receiving a great deal of interest around theworld due to not only their promise of higher energy efficiency and reduced highwayemissions, but also their ability to overcome the range limitations inherent in a purelyelectric automobile. In a hybrid powertrain, energy is stored as a petroleum fuel andin an electrical storage device, such as a battery pack, and is converted tomechanical energy by an internal combustion engine (ICE) and an electric motor(EM), respectively. The EM is used to improve energy efficiency and vehicleemissions while the ICE provides extended range capability.Computer simulation is a valuable tool for analyzing hardware components andpredicting vehicle performance with different powertrain configurations. In this worka traditional ICE operated vehicle is compared to several hybrid versions of the samevehicle, all modeled using GT-Suite. A variety of standard driving cycles areconsidered, among them the Federal Test Procedure (FTP) for city driving, theHighway Fuel Economy Test (HWY), the high acceleration aggressive drivingschedule (US06) that is often identified by the Supplemental FTP, and the NewEuropean Driving Cycle (NEDC). This study considers a rule-based energymanagement strategy for power splitting in the hybrid powertrain models. ICE onlyand hybrid modes are compared based on average as well as instantaneousperformance. The overall fuel economy, energy consumption and losses in the ICEand HEV powertrain models are monitored and compared based on averageperformance, and a comprehensive energy analysis is performed to track energysources and sinks. The paper results reveal the benefits of HEVs in terms of reducedfuel energy consumption and improved fuel economy.

Plug-in HEV with CVT: configuration, control, and its concurrent multi-objective optimization by evolutionary algorithm

Plug-in Hybrid Electric Vehicle (Plug-in HEV) has dramatic improvements in fuel economy and emission reduction. It is most important to decide its optimal configuration, energy management strategy, powertrain sizes, and control logic parameters. For multi-objective optimization, we present a concurrent optimization methodology based on an optimal Plug-in HEV powertrain configuration with continuous variable transmission (CVT). The novelty is using evolutionary algorithm in conjunction with an instantaneous optimal energy management strategy. Simulation results indicate the proposed method can significantly reduce fuel consumption and emissions by simultaneously optimizing the propulsion system parameters as well as the energy control parameters.

Optimized energy management control for the Toyota Hybrid System using dynamic programming on a predicted route with short computation time

Among the general problematic of the HEV power trains, the most critical point is the determination of the power-split ratio between the mechanical and the electrical paths, known as the energy management strategy (EMS). Many EMS are proposed in the literature, and can be grouped in two categories: the local optimization EMS and the global optimization EMS. The local optimization category corresponds to the EMS based on human expertise and the knowledge of the power train components efficiency maps. Thus, the local optimization EMS manages the power train operations by referring to predefined rules. The drawback of such strategies is that it brings an instantaneous fuel consumption optimization, and does not fully optimize the fuel consumption over the whole trip. Therefore, additional fuel savings are still possible. This paper presents an overall optimized predictive EMS for the Toyota Hybrid System (THS-II) power train of the Prius. The proposed EMS is based on Dynamic Programming (DP), where the prior knowledge of the route is required in order to predetermine the power-split ratio and optimize the fuel consumption for the whole predicted route. The DP EMS proposed for the THS-II power train is designed with a very short computation time, intended to be implemented in real-time applications. The potential of this DP-controller in reducing fuel consumption on regulatory cycles are computed and compared to a rule-based controller and to the Prius published fuel consumption results. Finally, the fuel reduction enhancements of the DP-controller are computed for real road tests achieved on a MY06 Prius in Ile-de-France, by comparing to the associated observed consumption measurements.

Dynamic modeling of the electro-mechanical configuration of the Toyota Hybrid System series/parallel power train

The hybridization of the conventional thermal vehicles nowadays constitutes a paramount importance for car manufacturers, facing the challenge of minimizing the consumption of the road transport. Although hybrid power train technologies did not converge towards a single solution, series/parallel power trains with power-split electromechanical transmissions prove to be the most promising hybrid technology. In fact, these power trains show maximum power train overall efficiency and maximum fuel reduction in almost all driving conditions compared to the conventional and other hybrid power trains. This paper addresses the model and design of the electro-mechanical configuration of one of the most effective HEV power trains: case study of the 2nd generation Prius. It presents the simulation work of the overall operation of the Toyota Hybrid System (THS-II) of the Prius, and explores not only its power-split eCVT innovative transmission system but also its overall supervision controller for energy management. The kinematic and dynamic behaviors of the THS-II power train are explained based on the power-split aspect of its transmission through a planetary gear train. Then, the possible regular driving functionalities that result from its eCVT operation and the energy flow within its power train are outlined. A feed-forward dynamic model of the studied power train is next proposed, supervised by a rule-based engineering intuition controller. The energy consumption of the THS-II proposed model has been validated by comparing simulation results to published results on European, American and Japanese regulatory driving cycles.

Convex Optimization of Charging Infrastructure Design and Component Sizing of a Plug-in Series HEV Powertrain

AbstractWith the topic of plug-in HEV city buses, this paper studies the highly coupled optimization problem of finding the most cost efficient compromise between investing in onboard electric powertrain components and installing a charging infrastructure along the bus line. The paper describes how convex optimization can be used to find the optimal battery sizing for a series HEV with fixed engine and generator unit and a fixed charging infrastructure along the bus line. The novelty of the proposed optimization approach is that both the battery sizing and the energy management strategy are optimized simultaneously by solving a convex problem. In the optimization approach the power characteristics of the engine-generator unit are approximated by a convex, second order polynomial, and the convex battery model assumes quadratic losses. The paper also presents an example for a specific bus line, showing the dependence between the optimal battery sizing and the number of charging stations on the bus line.