Driver Torque Demand Research Articles

Improving powertrain efficiency through torque modulation techniques in single and dual motor electric vehicles

Battery Electric Vehicles (BEVs) typically experience reduced powertrain energy efficiency under low-torque operating conditions. This issue can be mitigated by torque modulation, i.e., alternating driver torque demand between zero and an appropriate value. This paper aims to maximize the efficiency improvement of torque modulation and investigate its potential in two powertrain topologies: single and dual motor powertrains. To this end, optimal modulation strategies are proposed for both powertrains, considering overall powertrain energy losses and specific powertrain characteristics. Additionally, the adverse impact on driver comfort and powertrain durability due to the pulsating torque is examined. The results suggest that the proposed optimal modulation strategies can enhance powertrain efficiency while maintaining acceptable levels of driver comfort and powertrain durability. In addition, complementary modulation and torque distribution can be applied in the dual motor powertrain to provide further energy-saving potential and reduced impact on driver comfort.

A comparative study of driver torque demand prediction methods

AbstractThe performances of energy management systems or electric vehicles and hybrid electric vehicles are highly dependent on the forecast of future driver torque/power request sequence that affects vehicle efficiency and economy. Since the behaviour of the driver is challenging to model/predict by first‐principles models, modern artificial intelligence algorithms would represent feasible methods for approaching this problem in real‐world automotive systems. This work provides a comparative study and analysis of performances of different data‐driven torque prediction strategies. The studied and compared torque demand prediction techniques are exponentially varying model, linear regression, shallow and deep neural networks, and least square support vector machine‐based approaches. The prediction performance and computational cost of these techniques are evaluated and reported, and the possibility of exploiting these techniques in real‐world scenarios is also discussed.

Electric torque assist and supercharging of a downsized gasoline engine in a 48V mild hybrid powertrain

48V systems enable not only mild hybrid functionalities such as recuperation or torque assist by a belt-driven starter generator (BSG), but also electrification of accessories and the engine boosting system. To maximize the powertrain efficiency, a proper layout of the electrified system and an optimized distribution of the electric power during transient operation is essential. In this study, a vehicle co-simulation of a conventional powertrain with a downsized turbocharged gasoline engine is extended by a 48V system with an electric compressor (eC) and a BSG. The control functions of the eC and BSG are based on a state-of-the-art vehicle application and calibrated for transient operating conditions. The engine model, which is built using a one-dimensional crank angle resolved approach in GT-POWER, has been validated with measurement data and is used to predict the interaction between the eC and the engine air path. The investigations using the simulation platform show that the 48V eC and the BSG can significantly improve the fuel effïciency if the electric energy consumption is initially neglected. However, when considering the electric energy consumption within the vehicle co-simulation, efficient operation is particularly depending on driver torque demand, the battery state-of-charge and charging effïciency. Hence, intelligent operating strategies are necessary to take advantage of the better torque response and improve fuel consumption at the same time.

Receding horizon optimal control of HEVs with on‐board prediction of driver's power demand

To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.

Optimal starting control of zero-synchronous shock AMT based on torque compensation.

This article first puts forward the shortcomings of current AMT starting control in the preface. Then, in the second chapter, the dynamic analysis of the starting process was carried out, and the three requirements of starting control were clarified: dynamic requirements, smoothness requirements and low slippage requirements; it was clarified that dynamic requirements were the main control objectives of starting control, smoothness Performance requirements and low clutch wear requirements are constraints.In the third chapter, the optimal control principle of the AMT starting is proposed; the clutch target engagement amount is determined according to the power requirements, the engine speed control strategy and the engine target speed are determined according to the low slip wear requirements, according to the requirements of ride comfort, the clutch engagement speed and synchronous compensation torque are determined, and an optimal starting control method with zero synchronous shock based on torque compensation is proposed. Based on this, the optimal starting controller with three control loops is designed in Chapter 4, it realizes the functions of closed-loop control of driver torque demand, closed-loop control of sliding impact degree, closed-loop control of zero synchronous impact, and closed-loop control of engine speed with low sliding power.Finally, the vehicle test and simulation verification in Chapter 5 confirms that the control method proposed in this paper can effectively meet the starting control goal and eliminate the synchronization shock simply and efficiently.

ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs

In hybrid electric vehicles, the energy economy depends on the coordination between the internal combustion engine and the electric machines under the constraint that the total propulsion power satisfies the driver demand power. To optimize this coordination, not only the current power demand but also the future one is needed for real-time distribution decision. This paper presents a prediction-based optimal energy management strategy. Extreme learning machine algorithm is exploited to provide the driver torque demand prediction for realizing the receding horizon optimization. With an industrial used traffic-in-the-loop powertrain simulation platform, an urban driving route scenario is built for the source data collection. Both of one-step-ahead and multi-step-ahead predictions are investigated. The prediction results show that for the three-step-ahead prediction, the 1st step can achieve unbiased estimation and the minimum root-mean-square error can achieve 100, 150 and 160 of the 1st, 2nd and 3rd steps, respectively. Furthermore, integrating with the learning-based prediction, a real-time energy management strategy is designed by solving the receding horizon optimization problem. Simulation results demonstrate the effect of the proposed scheme.

Regenerative Hydraulic Assisted Turbocharger

Engine downsizing and down-speeding are essential in order to meet future U.S. fuel economy (FE) mandates. Turbocharging is one technology to meet these goals. Fuel economy improvements must, however, be achieved without sacrificing performance. One significant factor impacting drivability on turbocharged engines is typically referred to as “turbolag.” Since turbolag directly impacts the driver's torque demands, it is usually perceptible as an undesired slow transient boost response or as a sluggish torque response. High throughput turbochargers (TC) are especially susceptible to this dynamic and are often equipped with variable geometry turbines (VGT) to mitigate some of this effect. Assisted boosting techniques that add power directly to the TC shaft from a power source that is independent of the engine have been shown to significantly reduce turbolag. Single unit assisted turbochargers are either electrically assisted or hydraulically assisted. In this study, a regenerative hydraulically assisted turbocharger (RHAT) system is evaluated. A custom-designed RHAT system is coupled to a light duty diesel engine and is analyzed via vehicle and engine simulations for performance and energy requirements over standard test cycles. Supplier-provided performance maps for the hydraulic turbine, hydraulic turbopump were used. A production controller was coupled with the engine model and upgraded to control the engagement and disengagement of RHAT, with energy management strategies. Results show some interesting dynamics and shed light on system capabilities especially with regard to the energy balance between the assist and regenerative functions. Design considerations based on open-loop simulations are used for sizing the high-pressure accumulator. Simulation results show that the proposed RHAT turbocharger system can significantly improve engine transient response. Vehicle level simulations that include the driveline were also conducted and showed potential for up to 4% fuel economy improvement over the FTP 75 drive cycle. This study also identified some technical challenges related to optimal design and operation of the RHAT. Opportunities for additional fuel economy improvements are also discussed.

A Backlash Compensator for Drivability Improvement Via Real-Time Model Predictive Control

Nonlinear dynamics in the transmission and drive shafts of automotive powertrains, such as backlash, induce significant torque fluctuations at the wheels during tip-in and tip-out transients, deteriorating drivability. Several strategies are currently present in production vehicles to mitigate those effects. However, most of them are based on open-loop filtering of the driver torque demand, leading to sluggish acceleration performance. To improve the torque management algorithms for drivability and customer acceptability, the powertrain controller must be able to compensate for the wheel torque fluctuations without penalizing the vehicle response. This paper presents a novel backlash compensator for automotive drivetrain, realized via real-time model predictive control (MPC). Starting from a high-fidelity driveline model, the MPC-based compensator is designed to mitigate the drive shaft torque fluctuations by modifying the nominal spark timing during a backlash traverse event. Experimental tests were conducted with the compensator integrated into the engine electronic control unit (ECU) of a production passenger vehicle. Tip-in transients at low-gear conditions were considered to verify the ability of the compensator to reduce the torque overshoot when backlash crossing occurs.

Stochastic Predictive Energy Management of Power Split Hybrid Electric Bus for Real-World Driving Cycles

The energy conversion efficiencies among different sources of power split hybrid electric vehicle rely on the energy management strategy. In this paper, the energy management of a power split hybrid electric bus (HEB) is described as the predictive control problem based on the linear control-oriented model of the HEB. In order to ensure that the engine can output the desired power, the fuzzy PI controller, which can realize the optimal engine speed tracking, is further designed. Two real-world driving cycles are analyzed and formulated to evaluate the vehicle fuel economy under transient practical conditions. On this basis, the driver torque demand and vehicle velocity in the prediction horizon are derived with the stochastic one-step Markov chain. Finally, the hardware-in-the-loop (HIL) simulation platform is built to explore the validity of the controller. Compared with the adaptive equivalent fuel consumption minimization strategy, HIL test results demonstrate the real-time capability and benefits of the proposed approach in optimizing the energy management of HEB.

Flatness-based Model Predictive Control for the Fuel Optimization of Hybrid Electric Vehicles

In this paper a nonlinear model predictive controller for the optimization of thehybrid electric vehicle fuel efficiency is introduced. The idea is to use the concept of differential atness to determine the evolution of system's state across the prediction horizon without numerical integration. The entire nonlinear optimization problem is expressed as a function of a fictious output, called the at output and its first derivative. The optimal distribution of the requested torque between propulsion devices is then determined by a static optimization. The atness-based model predictive controller (FMPC) is compared to a linear time-varying MPC (LTV-MPC) which applies the linearized model of the plant to predict the future state and output trajectories. Both strategies are evaluated on standard driving cycles. To proof the concept it is assumed that the velocity profile and driver torque demand are known and exact over a predefined prediction horizon

Research on Drive Mode for an Independent Eight In-Wheel Motor Drive Vehicle

This paper describes a control strategy of drive modes switch for an independent eight in-wheel motor drive vehicle. There are three drive modes, and they are eight-wheel drive with ASR mode, eight-wheel drive mode and four-wheel drive mode. The control strategy is designed to improve the integrated performance of the vehicle, such as safety, dynamic performance and economic performance, but the previous research can only improve one of them. When the road adhesion coefficient is very small, the vehicle will drive in eight-wheel drive with ASR mode to ensure the vehicles safety, when the driver torque demand is very big, the vehicle will drive in eight-wheel mode to get better dynamic performance and when the driver torque demand is very small, the vehicle drive in four-wheel mode to get better economic performance. The vehicle switches among the three modes according to the driving condition. Simulations of the new control strategy were carried out on two different driving conditions. The results showed that an improvement in safety or economic performance was achieved with the control strategy.

Hybrid Electric Haulage Trucks for Open Pit Mining

Hybrid Electric Vehicles improve fuel economy by taking advantage of the Internal Combustion Engine (ICE) peak efficiency operating envelope and using an energy storage system to supply drive power when the ICE has lower efficiency. To achieve this, a hybrid design requires an ICE, a generator/motor, motor controllers, and an electric energy storage system (battery) which can be connected-up in a variety of ways such as, Parallel, and Series-Parallel configurations.Multiple strategies have been developed for hybrid electric vehicles energy management which make decisions based on inputs such as battery state of charge, driver torque demand, vehicle speed, and transmission gear. For example, as the state of charge of the battery decreases, it becomes more costly to use the electricity and hence the control system tends to transition the power source from battery to fuel.Although hybrid-electric haulage trucks have been implemented, energy storage has not been a feature of these systems. These trucks are typically arranged in Series configuration where the engine is completely decoupled from the wheels and used to provide electric power through a generator which powers electric motors on each of the wheels. The absence of a battery is an example of lost opportunity for fuel economy improvements through regenerative breaking and engine-off operation.This paper discusses the fuel economy question with respect to road geometry data and future speeds, a condition that can be determined for an autonomous haulage system with relative ease. Real-time access to such data can be put to advantage to maximize fuel economy. With a look-a-head system, even if the state of charge is low, the truck can continue to use the electric system if a downhill stretch is known to be ahead, so the system knows it can charge-up using regenerative breaking.

MOTION PLANNING CONTROL OF THE AIRPATH OF AN S.I. ENGINE WITH VALVE TIMING ACTUATORS

We address the control of the airpath of a turbocharged S.I. engine equipped with Variable Valve Timing actuators (VVT). Compared to standard configurations, the engine does not possess any external EGR (Exhaust Gas Recirculation) loop. Rather, VVT are used to produce internal EGR, providing similar beneficial effects in terms of emissions reduction. The airpath dynamics takes the form of a single mono-dimensional air balance in the intake manifold. In this equation, the VVT act as a disturbance by impacting on the air mass flow through the inlet valves. This impact can be estimated from real-time measurements. We use this information in a motion planning based control strategy by, successively, turning the driver's torque demand into a trajectory generation problem for the air mass contained in the intake manifold, and then deriving an intake manifold pressure trajectory. Supportive simulation results show the relevance of this approach and suggest ways of further improvements.

Control of a variable-geometry turbocharged and wastegated diesel engine

This paper addresses the problem of controlling a turbocharged diesel engine so as to minimize NOx and smoke emissions while ensuring that the driver's torque demands are met. A diesel engine equipped with a variable-geometry turbocharger (VGT) and an exhaust gas recirculation (EGR) valve is considered. The technical challenges in the control design task include the multivariable non-linear dynamics of the system and the unavailability of key states for feedback. A control strategy based on non-linear control synthesis is developed and shown accurately to control the air-fuel ratio (AFR) and the burned gas fraction in the intake manifold, F1, to desired values in the presence of changing operating conditions. The variables F1 and AFR are shown to be crucial for feedback. Since neither of these variables can be measured, an observer based on flow and pressure sensor measurements is developed for their real-time estimation. Lyapunov theory is used to show that the developed observer is asymptotically stable. Simulation results confirm the performance of the observer and the observer-based feedback controller. The importance of the developed observer extends beyond the application discussed in this paper. It could be useful for a wide variety of different control and diagnostic applications in diesel engines.

Control of variable geometry turbocharged diesel engines for reduced emissions

The emission control problem for an automotive direct injected compression ignition (diesel) engine equipped with exhaust gas recirculation (EGR) and a variable geometry turbocharger (VGT) is considered. The objective is to operate the engine to meet driver's torque demand and minimize NO/sub x/ emissions while at the same time avoiding visible smoke generation. It is demonstrated that the steady-state optimization of engine emissions results in operating points where EGR and VGT actuators are in effect redundant in their effect on the variables that most directly affect the emissions. A multivariable feedback controller is proposed which accounts for this actuator redundancy. Furthermore, it coordinates the two actuators to fully utilize their joint effect on engine emission performance. Experimental results confirm good response properties of the proposed controller.