Multi-mode Hybrid Electric Vehicle Research Articles

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Non-square internal model control for mode transition of hybrid electric vehicles with multiple time delays

Mode transitions of multi-mode hybrid electric vehicles need careful coordination among the engine torque, motor torque, and clutch transmitted torque. However, the three torques have different actuation delays which make it difficult to ensure mode transition performance. A non-square internal model controller (IMC) is proposed in this paper for mode transition during which the input number is greater than the output number and each input has a different actuation delay. At first, the Moore-Penrose generalized inverse matrix is introduced to solve the inverse of the non-square matrix and make the IMC applicable. Secondly, the all-pole method is adopted to approximate the three delay models. Based on these specialized techniques, the tracking controller and anti-disturbance controller of the IMC are derived. Experiment results verify the effectiveness of the proposed controller.

Engine Start‐Up Torque Coordinated Control for Multimode Hybrid Electric Vehicle Equipped With a Two‐Speed Dedicated Hybrid Transmission

A multimode hybrid electric vehicle (HEV) equipped with a dedicated hybrid transmission (DHT) can be generally divided into pure electric mode with engine shutdown and hybrid mode with engine operation. Engine start‐stop control is the key to achieving a transition between these two modes. To address the issues of engine start‐up torque coordination control and torque interference, first, the start‐up requirements of DHT‐HEV engines in different driving scenarios are summarized, a multistage dynamic model for DHT‐HEV engine start‐up is established, and the key issues of engine start‐up torque coordination control are analyzed. Second, an engine start‐up torque coordinated control strategy with torque interference and parameter uncertainty is proposed, a robust model predictive controller (RMPC) is designed to adjust clutch pressure, a finite time disturbance observer (FTDO) is designed to obtain torque interference during the engine start‐up process, and a sliding mode observer (SMO) is designed to obtain vehicle driving resistance torque. Finally, a simulation and hardware‐in‐the‐loop (HIL) test are conducted to verify the performance of the relevant observers, torque coordination control, and interference compensation control during the engine start‐up process.

Bi‐objective Trade‐Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy–Pareto Algorithm for a Multimode Hybrid Electric Vehicle

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV.

Coordinated clutch actuation for drivability improvement and energy management of a novel multi-mode hybrid electric vehicle and HIL validation

This article presents a novel architecture of a particular class, i.e., multi-mode hybrid electric propulsion systems that utilize two planetary gear sets, multiple clutches, two electric motors, and an internal combustion engine. Although this class of propulsion systems offers architectural compactness, improved fuel economy, and operational versatility, it faces a drivability issue arising from simultaneous clutch engagement and disengagement during mode shift events. Such drivability issues cannot be addressed by baseline motor-torque compensation-based coordinated control that is typically employed to address the drivability issues in power-split and parallel hybrid propulsion systems. Therefore, this article proposes an enhanced version of the coordinated control that includes the clutch’s torque capacity control. The enhanced coordinated control module augments with the energy management system, making the mode shift schedule drivable and near-optimal. A real-time hardware-in-the-loop test bench is developed to accurately evaluate the enhanced coordinated control’s impact on the energy management system’s performance. Test bench results demonstrate that the enhanced coordinated control is more effective than the baseline coordinated control in attenuating the propulsion system’s perturbations during mode shift events and making the overall mode shift schedule drivable. The enhanced energy management system consumes 7%–8% more fuel while transforming the mathematically optimal mode shift schedule into a drivable one.

Multi-mode hybrid electric vehicle routing problem

Hybrid electric vehicles (HEVs) are environmental-friendly vehicles that use a combination of the electric engine and internal combustion engine in their propulsion systems to reduce the fuel consumption and emission. In this paper, we consider a fleet of HEVs in logistics operations and introduce the Hybrid Electric Vehicle Routing Problem (HEVRP).Since we allow HEVs to operate in different drive modes, we refer to this problem as the Multi-Mode HEVRP (MM-HEVRP). We first model the problem as a mixed-integer linear program, where the objective function minimizes the total cost of the distances traveled at different modes.Since the problem is not tractable, we develop a matheuristic approach to solve it. The proposed approach combines Variable Neighborhood Search with mathematical programming. We test the performance of the proposed approach by solving benchmark instances generated for the Hybrid Electric Vehicle-Traveling Salesman Problem (HEV-TSP) and comparing our results with those published in the literature. In addition, we generate new MM-HEVRP data by modifying HEV-TSP benchmark instances.We solve the small-size MM-HEVRP instances using CPLEX and compare our solutions with the optimal solutions. The numerical results show that the proposed matheuristic is able to achieve high-quality solutions with reasonable computation times. Furthermore, we address the large-size instances and present a sensitivity analysis to provide further insights.

Energy optimization for intelligent hybrid electric vehicles based on hybrid system approach in a car‐following process

AbstractConsidering the hybrid dynamical characteristics of multimode hybrid electric vehicle during a car‐following process, a novel energy management strategy based on hybrid system theory is proposed in this article. Firstly, nonlinear powertrain model and longitudinal dynamics of a single‐shaft parallel hybrid electric vehicle is built, and hybrid characteristics combined with continuous dynamics and discrete events during operating mode transition are described. Secondly, the nonlinear powertrain model and characteristics are approximated by the piecewise affine method, the mixed‐logic dynamic modeling method is introduced and system variables are defined. Then the hybrid dynamical model for multimode hybrid electric vehicle in a car‐following process is established with the hybrid system description language. Thirdly, according to the principle of receding horizon control, a hybrid model predictive controller is designed and applied for optimizing the multiobjective energy management problem in the car‐following process. Finally, the proposed control strategy is compared with a rule‐based control strategy, and the simulation results show that the proposed control strategy achieves fuel efficiency improvement while ensure the dynamic performance, driving safety and ride comfort during the car‐following process, and the designed controller meets the requirement of deep fusion of car‐following and energy management.

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

In this study, a control map generation strategy for hybrid electric vehicles based on machine learning (ML) with optimization data was studied using a multimode hybrid electric vehicle. The optimization data from dynamic programming were used to produce the control maps by employing different ML methods, including Gaussian naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbors, and support vector machine. Since control map domains separated into several domains can exhibit unrealistic control behavior during engine on-off and hybrid mode shift processes, control maps separated into the same number of domains were used for the simulation study among the different ML methods. The demand torque and power maps by ML training were used for simulations of representative driving test cycles from the Environmental Protection Agency. The results with ML methods indicate that operating domains in the torque and power maps were separated for different driving modes, while they were not clearly separated in the results of DP optimization. Further, the results with the control maps for demand power exhibited slightly improved fuel efficiency compared to the maps for demand torque. This study is meaningful because a control map generation strategy based on ML was not only studied in order to observe the possibility of utilizing DP optimization results for real vehicles, but different types of ML methods were also analyzed and discussed to find appropriate methods for vehicle control map generation in terms of demand torque and power operating points.

Systematic Design and Optimization Method of Multimode Hybrid Electric Vehicles Based on Equivalent Tree Graph

Multimode hybrid electric vehicles (HEVs) have been widely applied due to its high efficiency and excellent overall performance. However, the introduction of multimode HEVs makes it difficult to choose reasonable design factors (architectures, component parameters, and control strategies) and determine correct mode shift rules, which undoubtedly enhances design complexity. To improve the design efficiency and reduce screen difficulty of optimal multimode configurations, a systematic design method is proposed to generate, screen, and optimize multimode HEVs with a single planetary gear. Based on equivalent tree graph method, the novel architectures are first generated. Then, considering mode shift rules and component parameters, requirements of speed and torque in corresponding main mode and minimum shift rule graph of working modes are taken as constraints to complete configuration screening. Finally, the equivalent consumption minimization strategy considering the proposed mode shift rules is put forward to evaluate and optimize energy management. The numerical results show that fuel economy of optimized configurations is improved by at least 22.1%, which proves the effectiveness of proposed systematic design method. Furthermore, it reveals that the multimode configuration can achieve better fuel economy when adopting the appropriate mode shift rule and designing the right arrangement and number of clutches and brakes.

Parameter optimization of rule-based control strategy for multi-mode hybrid electric vehicle

Multi-mode hybrid electric vehicle is considered as the best hybrid power solution because it has many operational modes and can achieve a wider and more efficient transmission range. But at the same time, it also brings some problems, such as the choice of operational mode. At present, the most commonly used mode-switching strategy is rule-based control strategy, but it needs to determine the logical threshold value of each control variable in advance. Usually these values are determined by experience, but cannot guarantee that the value obtained is the optimal solution. This paper combines the improved NSGA_II algorithm with the rule-based control strategy and optimizes the logic threshold value by using the improved NSGA_II algorithm to get the optimal logic threshold value. Combining the optimized rule-based control strategy with the minimum equivalent fuel consumption strategy, a real-time control strategy for multi-mode hybrid electric vehicle is proposed. The advantages of the proposed control strategy are proved by an example.

Mode Shift Schedule and Control Strategy Design of Multimode Hybrid Powertrain

Power-split hybrid technologies have been transformed from single mode to multimode to realize even greater savings in fuel and more powerful output. However, the optimization procedure of the mode shift map for multimode hybrid electric vehicles (HEVs) has not been formalized. In this paper, the types of mode shift are first classified as direct/indirect and conditional/unconditional. The optimal shift path with minimum energy variation for the indirect mode shift is formulated as a shortest path problem and solved by Dijkstra’s algorithm. By taking the energy loss during mode shift into consideration, the method for designing a mode shift map is presented from the perspective of the normalized system efficiency. The derived mode shift map is combined with a novel energy management strategy, referred to as normalized efficiency maximum strategy, to formulate an integrated real-time control strategy for the multimode HEV. Finally, by comparing two other commonly used strategies, dynamic programing and equivalent consumption minimization strategy, the proposed control strategy shows great potential in improving fuel economy while maintaining smooth engine operation, thus leading to better ride comfort of the vehicle.

Mode shift map design and integrated energy management control of a multi-mode hybrid electric vehicle

Multi-mode hybrid electric vehicles (HEVs) have become popular in recent years because of their high efficiency and excellent overall performance. However, the introduction of multiple operating modes has led to some issues, e.g., mode shift problems, which are not observed in conventional HEVs with a single mode. Similar to a gear shift map in conventional vehicles, the mode shift map in multi-mode HEVs needs to be properly designed. In this study, the energy management algorithm of a multi-mode HEV is optimized using dynamic programming (DP) considering the mode shift frequency and energy losses. A mode shift map is then extracted from the operating points of the DP results using the support vector machine (SVM). By combining the derived mode shift map and the equivalent consumption minimization strategy (ECMS), a real-time control strategy is proposed for the online energy management of the multi-mode HEV. The results of a case study show the effectiveness of the proposed mode shift map and energy management strategy.

Dynamic modelling and systematic control during the mode transition for a multi-mode hybrid electric vehicle

A smooth mode transition is critical for the driveability of hybrid electric vehicles and is difficult to achieve owing to transient intervention of torques from the engine, the electric machine and the transmission. This paper presents a systematic control strategy during transition from the motor-only mode to the compound driving mode of a multi-mode hybrid electric vehicle using one electric machine. The proposed strategy divides the mode transition process into four consecutive operating phases and employs a fuzzy gain-scheduling proportional–integral–derivative controller as feedback to adapt various non-linearities. Dynamic modelling of the vehicle system emphasizing the dynamics of the engine, the transmission, the clutches and the drive axle is conducted for validation simulation. Simulation results show that the mode transition controlled by the designed fuzzy gain-scheduling proportional–integral–derivative controller is completed in 700 ms with the vehicle jerk below 3.5 m/s3 and where the produced clutch frictional losses are only 56 J. The performance is better than those controlled by a conventional proportional–integral–derivative controller. A sensitivity study shows the proposed controller has good adaptability to disturbances caused by the changing road conditions, the uncertainties in engine system and the noise from clutch actuation.