Optimal Powertrain Research Articles

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

A multi‐criteria decision‐making approach for assembling optimal powertrain technology portfolios in low GHG emission environments

AbstractEnvironmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the “right” powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision‐support approach for assembling optimal powertrain technology portfolios while making decision‐makers aware of the trade‐offs between the achievable profit, the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines “a posteriori” decision‐making with multi‐objective optimization. In an application case, we feed the outlooks of selected market studies into the proposed decision‐support system. The result is a visualization and analysis of the current real‐world decision‐making problem faced by many automotive companies. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35% of vehicles equipped with an electric motor exists.

In-Vehicle Test Results for Advanced Propulsion and Vehicle System Controls Using Connected and Automated Vehicle Information

<div class="section abstract"><div class="htmlview paragraph">A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.</div><div class="htmlview paragraph">This paper documents an extensive simulation and experimental campaign that provides a systematic quantification of fuel economy and mobility benefits resulting from leveraging Vehicle-to-Everything (V2X) connectivity, longitudinal (Level 1+) automation, and advanced cylinder deactivation to improve the propulsion system and vehicle efficiency. A Vehicle Dynamics and Powertrain (VD&PT) optimization framework, developed jointly by The Ohio State University and BorgWarner, utilizes advanced route information available from the navigation system and GPS, and Vehicle-to-Infrastructure (V2I) communication when available to minimize the cumulative fuel consumption over a driver-selected route.</div><div class="htmlview paragraph">To assess the benefits of these technologies in real-world conditions, a Monte Carlo simulation framework was developed to quantify the impact of Signal Phase and Timing (SPaT), variability in traffic conditions, and behavior of different drivers on the fuel consumption and vehicle travel time. Further, verification through in-vehicle testing was conducted by reconstructing a subset of these scenarios on a proving ground.</div></div>

In-Vehicle Validation of Heavy-Duty Vehicle Fuel Savings via a Hierarchical Predictive Online Controller

<div class="section abstract"><div class="htmlview paragraph">This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping. This paper begins by outlining the controller development progression from a previously published engine-in-the-loop study to the in-vehicle driver-in-the-loop testing highlighted in this work. The purpose of this field testing is to quantify the level of agreement between field data and prior results, particularly noting the influence of noise, disturbances and unmodeled dynamics. Also detailed are the steps to effectively implement the predictive horizons in experimentation with human rather than automated driver inputs by replacing the conventional speedometer with an augmented driver display. The driver display seeks to maintain driver awareness and route preview simultaneously. Additional features of the instrumented truck testing protocol include detailed route maps on speed limits, intersections, traffic and road grade; highly accurate fuel flowrate measurements calibrated via gravimetric methods; and closed-loop control between the predictive controller, driver, and vehicle controller area network (CAN). These combined capabilities allow for model validation, investigation of the effects of human-in-the-loop on controller performance, and identification of unmodeled dynamics and disturbances. The results show that the driver advisory implementation matches fuel consumption predictions from software-in-the-loop simulations and engine-in-the-loop studies to within 2-9%. Following the study of in-vehicle performance and variability, these results also validate, using real road tests, the up to 21% fuel savings achieved by using the hierarchical control scheme with a combination of multiple chassis and powertrain optimization technologies.</div></div>

Comparison of Cycle Reduction and Model Reduction Strategies for the Design Optimization of Hybrid Powertrains on Driving Cycles

Decision-making is a crucial and difficult step in the design process of complex systems such as the hybrid powertrain. Finding an optimal solution requires the system feedback. This can be, depending on the granularity of the models at the component level, highly time-consuming. This is even more true when the system’s performance is determined by its control. In fact, various possibilities can be selected to deliver the required torque to the wheels during a driving cycle. In this work, two different design strategies are proposed to minimize the fuel consumption and the cost of the hybrid powertrain. Both strategies adopt the iterative framework which allows for the separation of the powertrain design problem and its control while leading to system optimality. The first approach is based on model reduction, while the second approach relies on improved cycle reduction techniques. They are then applied to a parallel hybrid vehicle case study, leading to important cost reduction in reasonable delays and are compared using different metrics.

Optimization of Powertrain in EV

The method for preliminary powertrain design is presented in the paper. Performance of the EV is realized by motor torque–speed curve and gear ratio optimization. The typical two-zone mechanical characteristic of a PMSM traction motor is included in the optimization program. The longitudinal vehicle model is considered in the paper. Some examples try to show the calculation possibilities in application to existing vehicles: Tesla Model S and Mini Cooper SE.

Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle

Connected and automated vehicles present significant opportunities for energy saving and efficiency, especially for hybrid electric vehicles. Information such as estimated trip duration, path, time, and ambient conditions can be utilized to predict the future thermal and traction loads for a connected vehicle. One of the most energy-intensive sub-systems of a hybrid electric or fully electric vehicle is cabin heating in cold climates. In this work, an innovative co-optimization platform is developed to optimize: (i) cabin heating using combined electrical resistance heating and engine heat assist, (ii) multi-mode powertrain operation during charge depletion, and (iii) exhaust aftertreatment system thermal management to minimize catalyst light-off fuel penalty. To this end, a model-based cabin heating and powertrain optimization platform is created and tested using extensive experimental data from a plug-in hybrid electric vehicle. Vehicle’s trip duration is estimated using vehicle connectivity data that is then used to forecast cabin heating and powertrain power demands. The results show the proposed integrated cabin and powertrain thermal management can lead to 10 % to 26% vehicle energy saving by testing for the United States Urban Dynamometer Driving Schedule and one real world drive cycle with varying elevations. In addition, the effect of variability of ambient temperature (−15 to 2 °C) on energy savings is studied using Monte Carlo simulations.

Cooling System Energy Consumption Reduction through a Novel All-Electric Powertrain Traction Module and Control Optimization

In this work, the problem of reducing the energy consumption of the cooling circuit for the propulsion system of an all-electric vehicle is approached with two different concepts: improvement of the powertrain efficiency and optimization of the control strategy. Improvement of the powertrain efficiency is obtained through a modular design, which consists of replacing the electric powertrain with several smaller traction modules whose powers sum up to the total power of the original powertrain. In this paper, it is shown how modularity, among other benefits, also allows reducing the energy consumption of the cooling system up to 54%. The energy consumption of the cooling system is associated with two components: the pump and the fan. They produce a so-called auxiliary load on the battery, reducing the maximum range of the vehicle. In conventional cooling systems, the pump and the fan are controlled with a thermostat, without taking into account the energy consumption. Conversely, in this work a control strategy to reduce the auxiliary loads is developed and compared with the conventional approach, showing that the energy consumption of the cooling system can be reduced up to 27%. To test the control strategy, numerical simulations have been carried out with a 1-D model of the cooling system. On the other hand, all the thermal loads of the components have been calculated with a vehicle simulator, which is able to predict the vehicle’s behavior under different driving cycles.

Extended Horizon ECMS Control of PHEVs With 2D Electricity Price Adaptation Policy

This article proposes new improvements to the ECMS-based control of plug-in hybrid electric vehicles (PHEVs) using macro-level trip forecast information. First, the dynamic programming (DP) optimization is applied to a power-split PHEV model to obtain the optimal powertrain trajectories for benchmarking the energy economy of the proposed controllers. An instantaneous energy cost minimization process is then formulated to compute the optimal mix of fuel and electricity, subject to the state and input constraints. To improve the speed and accuracy of the energy management system, the Nelder-Mead simplex search algorithm is used for the instantaneous optimizations. To account for the battery capacity limitation, an adaptive electricity pricing policy is introduced to control battery discharge rate based on the remaining trip distance, average speed, and elevation forecasts. The parameters of the proposed policy are optimized for a group of 5 urban and highway drive cycles to obtain a robust controller which can serve a wide spectrum of driving patterns. To further improve the energy economy, an extended-horizon optimization algorithm is proposed to account for control input transitions between consecutive time steps. Finally, a revised speed forecast formula is implemented during the low-SOC intervals to lift battery SOC ahead of high-demand periods. The combination of the proposed schemes enables reaching over 99% average optimality compared to a finely discretized DP optimization for the adopted drive cycles, and above 98% optimality for a test PHEV drive cycle.

Powertrain Optimization for Electric Buses under Optimal Energy-Efficient Driving

State of the art powertrain optimization compares the energy consumption of different powertrain configurations based on simulations with fixed driving cycles. However, this approach might not be applicable to future vehicles, since speed advisory systems and automated driving functions offer the potential to adapt the speed profile to minimize energy consumption. This study aims to investigate the potential of powertrain optimization with respect to energy consumption under optimal energy-efficient driving for electric buses. The optimal powertrain configurations of the buses under energy-efficient driving and their respective energy consumptions are obtained using powertrain-specific optimized driving cycles and compared with those of human-driven unconnected buses and buses with non-powertrain-specific optimal speed profiles. Based on the results, new trends in the powertrain design of vehicles under energy-efficient driving are derived. The optimized driving cycles are calculated using a dynamic programming approach. The evaluations were based on the fact that the buses under energy-efficient driving operate in dedicated lanes with vehicle-to-infrastructure (V2I) communication while the unconnected buses operate in mixed traffic. The results indicate that deviating from the optimal powertrain configuration does not have a significant effect on energy consumption for optimized speed profiles; however, the energy savings from an optimized powertrain configuration can be significant when ride comfort is considered. The connected buses under energy-efficient driving operating in dedicated lanes may reduce energy consumption by up to 27% compared to human-driven unconnected buses.

Application of Simulation Software in Optimization of Powertrain of Pure Electric Vehicle

— Pure electric vehicles are a research hotspot in today's new energy vehicle industry. Its core is the electromechanical coupled drive system (drive motor and gearbox), and its performance affects the overall performance of pure electric vehicles. The application of industrial software for electromagnetic design, electrothermal design, electromagnetic vibration and noise optimization of drive motors, heat transfer analysis, fatigue analysis, and noise optimization analysis of gearboxes has become important technologies for designing and manufacturing the core components of electromechanical coupling drive systems.This article analyzes the current status and existing problems of the development of simulation software for the core components (drive motor and gearbox) of the electromechanical coupling drive system of electric vehicles, and proposes solutions to key technical problems, and proposes the development direction of unified simulation of drive motor and gearbox on the electromechanical coupling platform

Torsional Vibration Attenuation Characteristics and Stiffness Identification of Flexible Coupling in Vehicle Power Train

Flexible coupling is one of the crucial components for vibration attenuation used in vehicle power train. Vibration attenuation characteristics and stiffness identification of flexible coupling are profoundly studied aiming at one vehicle power train. Firstly, the dynamics model of each crucial transmission component in power train is constructed. And the torsional vibration model of power train is established according to the concentrated mass method. The effects of coupling stiffness on vibration responses of power train are thoroughly analyzed based on system concentrated mass dynamics model. Secondly, the sensitivities of natural frequency and main forced vibration response parameters are calculated. The coupling stiffness is proved to be a sensitive parameter. Finally, taking the Geislinger coupling as an example, the damping and stiffness characteristics are acquired according to the parameter identification method based on the quantity of test data. The results provide the theory basis for the dynamics optimization of power train.

Adaptive energy management in automated hybrid electric vehicles with flexible torque request

Rapidly-evolving technologies for vehicle electrification and automation offer increased opportunities to enhance the performance and efficiency of energy management strategies for automated hybrid electric vehicles (A-HEVs). In this context, an adaptive energy management approach based on an equivalent consumption minimization strategy (ECMS) framework is developed to optimize gearshift commands and torque distribution for an automated parallel HEV. This methodology utilizes the emerging idea of flexible torque request by considering drivability and fuel economy simultaneously. The gearshift map is extracted from optimal results with ECMS, as treated in the dynamic programming (DP)-based strategy, to avoid frequent gearshift events thereby considering both drivability and fuel economy. An adaptive energy management strategy with flexible torque request is then reformulated by a modified ECMS, seeking better performance for the powertrain optimization. As a result, utilizing the flexible torque request, the torque distribution and gearshift commands are jointly optimized in the same framework. A sensitivity study for different parameters is explored, and adaptation laws of the main parameters are also devised for the proposed approach. Finally, simulations are performed in two driving cases to demonstrate the effectiveness of the proposed method. Results confirm that the proposed methodology produces a promising fuel efficiency, relative to the one with fixed torque request, while ensuring good drivability and traffic efficiency.

Comparative evaluation of powertrain concepts through an eco-impact optimization framework with real driving data

The assessment of the ecological impact of different powertrain concepts is of increasing relevance considering the enormous efforts necessary to limit the global warming effect due to the man-made climate change. Within this contribution, we adopt existing methods for the optimization of electric and hybrid electric powertrains using a vehicle simulation environment and derive a method to identify the ecological potential of different powertrain concepts for a set of technological parameters in the reference year 2030. By optimizing the parametrization for each powertrain concept and by adapting the respective operating behaviour specifically to minimize the ecological impact, a reliable and unbiased comparison is enabled. We use our optimization environment with the Real Ecological Impact as objective function to compare different powertrain concepts on driving profiles that are based on real driving data recorded in Germany. Despite the fact that all of the considered driving profiles contain trips of similar length, their respective optimized powertrain concepts are different. Plug-In Hybrid vehicles achieve the greatest potential for long-range capable vehicles and are least sensitive to different driving profiles.

An Edge Computing Framework for Powertrain Control System Optimization of Intelligent and Connected Vehicles Based on Curiosity-Driven Deep Reinforcement Learning

For the ongoing revolution in developing intelligent and connected vehicles (ICVs), there is a lack of research for powertrain control systems using the latest artificial intelligence and vehicle-to-everything technology that have already been widely adopted in the autonomous driving systems. In this context, recent development of deep reinforcement learning (DRL) and one of the latest computing frameworks are coupled to facilitate an onboard-based intelligent powertrain control. Taking the boost control of a diesel engine equipped with variable geometry turbocharger as an example, the results show that the final control behavior indicated by the cumulated rewards is improved by 50.43% and the learning efficiency is improved by 74.29% for the proposed curiosity-driven DRL algorithm, compared with the same structure DRL algorithm with classic random exploration policy. In addition, unlike most of the DRL-based powertrain optimization algorithms, which have only been applied to single-machine architecture, this work manages the proposed DRL algorithm in parallel and, more importantly, from an edge computing perspective. This, in addition to greatly speeding up the algorithm training, can also realize a good balance of control accuracy and generality depending upon the selected training scenario. Moreover, unlike most of the cloud computing frameworks, which require low network latency, the proposed architecture can achieve a similar final control performance even if good network communication is not allowed. Compared with other existing powertrain control methods, the proposed algorithm is able to approximate a global powertrain control optimization autonomously in a connected manner, making it attractive to current ICVs with advanced automated driving and traditional powertrain control.

Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost

Proton Exchange Membrane Fuel Cell (PEMFC) system, as a highly efficient, zero pump-to-wheel emission and quick refuelling energy conversion device, is particularly suited for heavy-duty transportation applications. An optimally designed hybrid powertrain with a battery energy storage system and optimal operation control can extend the operating life of the PEMFC system and reduce its lifecycle cost. This work focuses on the minimization of the lifecycle cost of a PEMFC hybrid electric propulsion system through the joint powertrain system design and control optimizations. The approach models and predicts the cost of hydrogen fuel determined by the energy efficiency of the powertrain, as well as the costs associated with the performance degradation and life-shortening of the PEMFC and the battery under a given vehicle operation profile. A nested, tri-level optimization problem is formulated for identifying the optimal powertrain component sizes and energy management parameters, and an advanced surrogate-based global optimization method is used to solve the complex optimization problem. The advantage of the new approach is revealed through the optimal design and control development of a representative mining truck operating under a real-world operation profile, reducing its mining tonnage specific cost by ten percent over its lifecycle. The new integrated design and control optimization method improved energy efficiency, reduced environmental pollutants, and lowered the lifecycle cost of fuel cell mining trucks, contributing to broader applications of the PEMFC technology.

Distributed optimal energy consumption control of HEVs under MFG-based speed consensus

This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus. Large scale vehicles in a traffic flow is targeted instead of individual vehicles, and it is assumed that the propulsion power of vehicles is hybrid electric powertrain. The control scheme is designed in the following two stages. In the first stage, in order to achieve speed consensus, the acceleration control law is designed by applying the MFG (mean-field game) theory. In the second stage, optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint. The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.

Electric Machine Design Tool for Permanent Magnet Synchronous Machines and Induction Machines

The rising mobility demand of today’s society leads to an increasing strain of noise and pollutant emissions on people and the environment. An increasing environmental awareness and the scarcity of fossil fuels are increasingly placing alternative-powered vehicles in the focus of politics, research and development. Electric vehicles represent a promising solution to this problem. The electric machine represents a design control lever for the optimization of the electric powertrain with regard to efficiency, power, weight and size. Therefore, accurate and realistic machine design tools for the design of electric machines are becoming increasingly important. In this paper, the authors present an electric machine design tool for electric machines using MATLAB® in order to enable an automated machine design. The electric machine design tool is published under an LGPL open source license.

Real-Time Implementation of Optimal Energy Management in Hybrid Electric Vehicles: Globally Optimal Control of Acceleration Events

Abstract Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.

Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement

Hybrid electric propulsion has served as an advantageous powertrain solution for improving the performance, fuel efficiency and emissions of passenger vehicles using well-established fueling infrastructure of gasoline and diesel, and with low conversion cost. Natural gas (NG) presents as a promising alternative fuel to replace diesel for heavy-duty vehicles with prominent potentials of fuel cost and emissions reduction, but its wide adoption still faces a number of technical challenges, including weaker engine performance, and sharply increased hydrocarbon and carbon monoxide emissions at certain engine operation states. This work introduces a new approach to integrate the NG-diesel dual-fuel compression ignited (CI) engine, hybrid electric powertrain system, and optimal powertrain control and energy management to consider vehicle performance, fuel efficiency and CO2/HC/CO/NOx emissions simultaneously to address these issues. The mining truck’s hybrid powertrain system and component models, including NG engine fuel efficiency and emission models, have been introduced to support the work. An advanced surrogate model based global optimization method is extended to solve the computational intensive, black-box optimization problem for optimal power control and energy management. Implementation of the optimization results into control rules for real-time operation, and HIL simulation results to demonstrate the benefits of the newly introduced method are presented using a case study on a representative mining truck and its operations. The study formed the foundation for further research in this area.