Powertrain Operation Research Articles

Research on switching control based sliding mode coordination strategy of hybrid electric vehicle

The transient mode transition from pure electric driving to hybrid driving in a hybrid electric vehicle (HEV) involves multiple stages, each characterized by different models and control objectives, demanding diverse levels of controller robustness and convergence performance. Addressing the complexities of multi-stage transitions in a parallel HEV, the paper proposes a dynamic coordination strategy based on the switched control system concept. Distinct sliding mode controllers (SMC) are designed for each transition stage, tailored to the specific characteristics of powertrain operation. At the engine starting and speed synchronization stages, the global integral sliding mode control (GISMC) is integrated with a novel anti-saturation reaching law to ensure the system resides in the sliding mode, enhancing global robustness. During the engine speed regulation stage, the nonsingular terminal sliding mode control (NTSMC) facilitates rapid engine speed tracking for reduced mode transition time. By combining GISMC for global robustness and NTSMC for accelerated convergence, the strategy is designed to enhance mode transition quality at different stages. Simulation results demonstrate a 7.3% reduction in mode transition time and a 60.7% decrease in jerk compared to the conventional SMC strategy. In hardware-in-the-loop tests, the proposed control strategy proves effective in improving mode transition quality.

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Speedy Hierarchical Eco-Planning for Connected Multi-Stack Fuel Cell Vehicles via Health-Conscious Decentralized Convex Optimization

<div>Connected fuel cell vehicles (C-FCVs) have gained increasing attention for solving traffic congestion and environmental pollution issues. To reduce operational costs, increase driving range, and improve driver comfort, simultaneously optimizing C-FCV speed trajectories and powertrain operation is a promising approach. Nevertheless, this remains difficult due to heavy computational demands and the complexity of real-time traffic scenarios. To resolve these issues, this article proposes a two-level eco-driving strategy consisting of speed planning and energy management layers. In the top layer, the speed planning predictor first predicts dynamic traffic constraints using the long short-term memory (LSTM) model. Second, a model predictive control (MPC) framework optimizes speed trajectories under dynamic traffic constraints, considering hydrogen consumption, ride comfort, and traffic flow efficiency. A multivariable polynomial hydrogen consumption model is also introduced to reduce computational time. In the bottom layer, the decentralized MPC framework uses the calculated speed trajectory to figure out how to allocate the power optimally between the fuel cell modules and the battery pack. The objective of the optimization problem is to reduce hydrogen consumption and mitigate component degradation by focusing on targets such as the operating range of state of charge (SoC), as well as battery and fuel cell degradation. Simulation results show that the proposed decentralized eco-planning strategy can optimize the speed trajectory to make the ride much more comfortable with a small amount of jerkiness (−0.18 to 0.18 m/s<sup>3</sup>) and reduce the amount of hydrogen used per unit distance by 7.28% and the amount of degradation by 5.33%.</div>

Prediction-based Optimization of Electric Powertrain Operation Using Silent Testing of Automated Driving Functions

Prediction-based Optimization of Electric Powertrain Operation Using Silent Testing of Automated Driving Functions

Predictive Thermal Management for an Electric Vehicle Powertrain

The design of thermal management strategies for electric vehicle powertrains is an important task, since these strategies influence performance, energy consumption, safety and durability of the powertrain operation. In the present paper, a predictive thermal management strategy for the powertrain cooling system is developed. The aims are to minimize the energy consumption of the cooling system, to increase the efficiency of the electric motors and to operate them in a temperature area of safety and durability. The thermal management strategy is realized by a real-time capable nonlinear model predictive controller, incorporating a dynamical model for the powertrain of the battery electric vehicle under consideration. The usage of this model-based approach ensures a fast adaption of the control strategy to different vehicle architectures. Since predictions of the disturbances acting on the system are uncertain in general, the control strategy is extended by a stochastic model predictive control approach to handle uncertainties in the disturbance prediction. The effectiveness of the resulting strategies is demonstrated using an accurate simulation model. Special attention is given to the possible energy savings, the robustness with respect to uncertainties, as well as the computational effort of the algorithms.

An optimal-pressure control strategy for hydrostatic wheel loader

Mobile machines consume a large amount of fossil fuel each year; therefore, it is critical to reduce their fuel consumption. A wheel loader is a typical mobile machine of great research interest. Most compact wheel loaders are usually equipped with hydrostatic transmissions. To reduce the fuel consumption of the powertrain, a min-rpm strategy where the engine speed is maintained as low as possible is usually applied. It is a rule-based control strategy, however, cannot achieve minimum engine fuel consumption. Therefore, an optimal-pressure control strategy is proposed for hydrostatic wheel loaders, where the minimum fuel consumption is achieved by selecting optimal system pressure at each time step. To show the advantages of the proposed strategy, the powertrain operation and engine fuel consumption are compared with the min-rpm strategy through simulation studies. To further verify the effectiveness of the proposed strategy, comparisons are conducted using three loading cycles (30s, 57s and 90s). Results show the engine fuel savings in three loading cycles, with fuel saving up to 27% in the 90s loading cycle.

A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings.

Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles.

Energy Efficient Platooning of Connected Electrified Vehicles Enabled by a Mixed Hybrid Electric Powertrain Architecture

The platooning of electrified vehicles can improve vehicle performance by enhancing energy efficiency and safety. Nevertheless, studies focusing on control strategies for platoons usually ignore or over-simplify the powertrain in the vehicle dynamics model. Advanced powertrain control strategies for electrified vehicles show that efficient operation of the powertrain depends on the current vehicle status, such as the torque demand, vehicle speed, and battery state of charge. In addition, the operation of individual powertrains can affect platoon-level performance, and the relationship between powertrain operation and vehicle status can be highly nonlinear. In studies of platoons, the evaluation of vehicle performance usually requires the vehicle to converge to a static operating point or to follow a simple operating pattern. Ideal cases can help in the understanding and improvement of vehicle performance but may be less beneficial for applications in realistic cases that are more complex, e.g. in military operations. In this study, a platooning optimization problem is formulated for optimal performance between two locations, given the terrain grade and soil properties along the path. A high-fidelity powertrain model is constructed and embedded in a detailed platoon model. The drive schedule is specifically designed for a platoon of vehicles with an innovative hybrid electric powertrain to achieve minimum total energy consumption. Results show that the approach is able to reduce energy consumption and headway keeping errors.

A Mathematical Model of Operation of a Semi-Trailer Tractor Powertrain

The mathematical modelling is an inseparable part of mechanical engineers' activities. It is applied in design, optimization, verifying, as well as modifying of products. The approach of creating virtual models and their analyzing without having real products is applied in several domains. A mathematical model is given by equations of motion. The main objective of this work is to present the way how a mathematical model of a car powertrain operation is derived and applied in practical applications. A semi-trailer tractor with a standard powertrain was chosen as a reference car. The derived equations of motion are written in a matrix form and subsequently they are solved by means of a technical programming language Matlab. Parameters of the solved tractor mechanical system come from the real data and they are supplemented by the empirical data.

Maximum-distance race strategies for a fully electric endurance race car

This paper presents a bi-level optimization framework to compute the maximum-distance stint and charging strategies for a fully electric endurance race car. Thereby, the lower level computes the minimum-stint-time Powertrain Operation (PO) for a given battery energy budget and stint length, whilst the upper level leverages that information to jointly optimize the stint length, charge time and number of pit stops, in order to maximize the driven distance in the course of a fixed-time endurance race. Specifically, we first extend a convex lap time optimization framework to capture multiple laps and force-based electric motor models, and use it to create a map linking the charge time and stint length to the achievable stint time. Second, we leverage the map to frame the maximum-race-distance problem as a mixed-integer second order conic program that can be efficiently solved to the global optimum with off-the-shelf optimization algorithms. Finally, we showcase our framework on a 6 h race around the Zandvoort circuit. Our results show that a flat-out strategy can be extremely detrimental, and that, compared to when the stints are optimized for a fixed number of pit stops, jointly optimizing the stints and number of pit stops can increase the driven distance of several laps.

Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.

Improving the Efficiency of HEV Electronic Applications Using CAN-BUS Communication

A new energy management technique with aid of Controller Area Network (CAN) bus for hybrid electric vehicle (HEV) is proposed in this research. HEV is a type of hybrid vehicle that combines with an electric propulsion system a conventional internal combustion engine system. The electric powertrain operation is intended to achieve greater fuel economy than a conventional vehicle. To accurately distribute power from one of the battery sources, the Energy Management System determines the reference speed for the electric motor drive and the internal combustion engine. The Proportional and Integral (PI) controller is used to maximize the gain of the primary and secondary controllers for maximum overshoot and resolution time. By optimizing the value, using the Naked Mole Rat optimization technique, the efficiency is improved. The CAN bus is a multi-master communication protocol characterized by the deterministic resolution, high efficiency and simple implementation. We introduce a secure technology that helps to automotive CAN protocol which is often used in HEV networks. The simulation results show that the proposed approach achieves an increased efficiency of 97%.

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

Research on Establishment of Vehicle Energy Distribution Model and Energy Consumption Optimization Based on Electric Hybrid System

In order to improve the adaptability and accuracy of the system average efficiency model in energy consumption analysis of working conditions, this paper presents a vehicle energy distribution model based on the layout and powertrain operation features of the electric hybrid system, and presents a vehicle energy consumption optimization method for control strategy and hardware quality optimization based on the guidance of the energy distribution model.

Real-time estimation of junction temperature in IGBT inverter with a simple parameterized power loss model

System-level thermal modelling of electric drive systems requires simple thermal models coupled with each other by the operation parameters of the drive systems. This paper presents a simple parameterized power loss model for insulated gate bipolar transistor (IGBT) inverters, in which variables are relevant to the powertrain operation conditions and are routinely monitored. Combined with a two-impedance thermal model of the inverter with parameters fitted to the results of computation fluid dynamics (CFD) simulations, the resulted real-time model can provide accurate estimations of the junction temperature. The present power loss model combined with the simple two-impedance thermal model is very suitable to be used in system-level real-time thermal monitoring of an integrated electric drive system.

Energy-Efficient Connected and Automated Vehicles: Real-Time Traffic Prediction-Enabled Co-Optimization of Vehicle Motion and Powertrain Operation

Connected and automated vehicles (CAVs) can bring energy, mobility, and safety benefits to transportation. Energy savings can be achieved by solving a mathematical optimization problem for a lookahead horizon using previewed traffic information enabled by connectivity. However, it is challenging to predict shortterm traffic, especially for mixed-traffic scenarios, where both connected and unconnected vehicles are on the road.

Two-Stage Synthetic Optimization of Supercapacitor-Based Energy Storage Systems, Traction Power Parameters and Train Operation in Urban Rail Transit

The stationary supercapacitor energy storage system (SCESS) is one of effective approaches for the utilization of train's regenerative braking energy in urban rail systems. In this paper, the capacity configuration of SCESSs, the no-load voltage of substation, the control of onboard braking resistors and train operation diagrams are considered comprehensively. Based on the equivalent circuit model, the effects of traction power system parameters on the energy transmission between powering trains, braking trains and SCESSs are analyzed, and the relationships between the train operating parameters and system energy consumptions are revealed. A multi-variable synthetic optimization method is proposed to optimize the SCESS capacity, train operation diagrams and traction power system parameters collaboratively. In order to reduce the search space and improve the efficiency of the optimization algorithm, a hierarchical multi-objective optimization model is established, based on which the design variables and the control variables are optimized iteratively. And as the traffic density has a great impact on the regenerative braking energy distribution of the system, the optimization objective function fully considers the frequency distribution of headways throughout the day. Combined the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) with the traction power flow simulator, the flowchart of the two-stage optimization algorithm is designed, and the pareto set of the multi-objective problem is obtained. The advantages of reducing system energy consumptions and configuration cost under the proposed algorithm are verified based on case studies of Beijing Batong Line.

Component-in-the-Loop Testing of Automotive Powertrains Featuring All-Wheel-Drive

The article is dedicated to the methodology of designing component-in-the-loop (CiL) testing systems for automotive powertrains featuring several drivelines, including variants with individually driven axles or wheels. The methodical part begins with descriptions of operating and control loops of CiL systems having various simulating functionality—from a “lumped” vehicle for driving cycle tests to vehicles with independently rotating drivelines for simulating dynamic maneuvers. The sequel contains an analysis that eliminates a lack of clarity observed in the existing literature regarding the principles of building a “virtual inertia” and synchronization of loading regimes between individual drivelines of the tested powertrain. In addition, a contribution to the CiL methodology is offered by analyzing the options of simulating tire slip taking into account a limited accuracy of measurement equipment and a limited performance of actuating devices. The methodical part concludes with two examples of mathematical models that can be employed in CiL systems to simulate vehicle dynamics. The first one describes linear motion of a “lumped” vehicle, while the second one simulates vehicle’s trajectory motion taking into account tire slip in both the longitudinal and lateral directions. The practical part of the article presents a case study showing an implementation of the CiL design principles in a laboratory testing facility intended for an all-wheel-drive hybrid powertrain of a heavy-duty vehicle. The CiL system description is followed by the test results simulating the hybrid powertrain operation in a driving cycle and in trajectory maneuvering. The results prove the validity of the proposed methodical principles, as well as their suitability for practical implementations.