Hybrid Electric Vehicle Powertrain Research Articles

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

KPI-related monitoring approach for powertrain system in hybrid electric vehicles

This paper introduces a novel monitoring method related to key-performance-indicators (KPIs), specifically tailored for the hybrid electric vehicle (HEV) powertrain system. The proposed method establishes a new KPI that better reflects the performance of the HEV powertrain system. Through the application of partial least squares and contribution plot method, it excels in minimizing data scale and precisely monitoring HEV powertrain faults. Diverging from current methodologies, this method demands minimal prior knowledge and solely relies on previously observed HEV data. The simulation results demonstrate the method's capability to detect engine and motor overheating faults while accurately diagnosing their root causes. In summary, this innovative method marks a significant departure from existing approaches, providing a robust and powerful tool for state monitoring in the HEV powertrain system.

A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions

ABSTRACT This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.

Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.

Active torsional vibration suppression of hybrid electric vehicle drive system based on optimal harmonic current instruction calculation method

Torsional vibrations in the drive system of a hybrid electric vehicle powered by an engine are generated by cyclic torque fluctuations. These fluctuations stem from the combustion torque and the reciprocating inertial torque of the cylinder in the reciprocating piston engine. To address this issue, a real-time engine transient torque model that considers the thermodynamic characteristics of each component is established. A method was designed to identify the torsional vibration of a powertrain based on the engine torque, electric motor torque, and powertrain model. Building on this foundation, a method was proposed to calculate the optimal harmonic current instruction of the electric motor to mitigate torsional vibration in the Hybrid electric vehicles (HEV) powertrain. Furthermore, a proportional integral-resonance (PIR) controller was designed for the injection of an electric motor reference harmonic current. Finally, a method was proposed for suppressing the torsional vibration in HEV drive systems that incorporate active harmonic current injection. The simulation and test results indicate that the proposed method can effectively diminish the amplitudes of the drive system's 2nd, 4th, 6th, and 8th harmonic torque, ensuring stable operation of the powertrain.

Aging-aware optimal power management control and component sizing of a fuel cell hybrid electric vehicle powertrain

The current study presents a comprehensive approach for optimizing the power distribution control and design of a Fuel Cell Hybrid Electric Vehicle (FCHEV) equipped with a Battery-Ultracapacitor Hybrid Energy Storage System (HESS) using a multi-objective evolutionary algorithm called interactive adaptive-weight genetic algorithm (i-AWGA). The method aims to maximize the vehicle’s driving range and the lifetimes of the fuel cell stack and battery while minimizing hydrogen fuel consumption and HESS size. The energy management strategy involves fuzzy logic controllers to distribute the power demand between the fuel cell and HESS and between the battery and ultracapacitor pack. Under the combined standardized cycle in which the optimization was developed, the optimized FCHEV configuration achieved a driving range of 444 km, hydrogen consumption of 0.9009 kg/100 km. Furthermore, the optimal configuration demonstrated robustness in real-world driving conditions, exhibiting improved energy efficiency, driving autonomy, and power sources lifespan. A cost–benefit analysis was also carried out, in which the optimized configuration was evaluated in terms of cost of ownership, achieving 31.28 US$/km, which means the substantial reduction of up to 63.59% in the invested cost-to-autonomy ratio as compared against other electrified vehicle powertrain topologies. Overall, this study offers a promising approach for designing efficient, cost-effective, and environmentally friendly FCHEVs with improved performance and durability.

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.

Design and Analysis of Energy Transition in Hybrid Electric Vehicle Power Train Systems

Design and Analysis of Energy Transition in Hybrid Electric Vehicle Power Train Systems

Fault detection and separation of hybrid electric vehicles based on kernel orthogonal subspace analysis

Driving quality and vehicles safety of hybrid electric vehicles (HEVs) are two hot-topic issues in automobile technology. Nowadays, research focuses to more intelligent and convenient HEVs fault detection methods. This paper will focus on the fault detection of HEV powertrain system with a data-driven algorithm. Orthonormal subspace analysis (OSA) is a newly proposed data-driven method which adds the ability of fault separation. Nonetheless, the linear OSA algorithm cannot effectively detect powertrain system faults, since these faults present complex nonlinear characteristics. A new kernel OSA (KOSA) method is proposed to transform the nonlinear problem into a linear problem through the mapping of kernel function and the dimensionality reduction technique of OSA. Testing results on a nonlinear model and real samples of XMQ6127AGCHEVN61 HEV show that KOSA address the nonlinear problems and it performs better than OSA and kernel principal component analysis (KPCA)

Complex Evaluation of Heavy-Duty Truck Hybridization and Electrification Options

Abstract Parallel hybrid electric vehicle (HEV) powertrain topologies are easily applicable on an existing conventional powertrain, and are frequently used in passenger vehicles, with a goal to reduce the overall fleet CO2 emissions, either with mild, full, or plug-in capability. However, for the heavy-duty trucks, the powertrain electrification progresses more slowly. Therefore, the goal of this paper is to evaluate three different hybridization options, together with two electrification options, in comparison with conventional powertrain combined with 5.9 L 6-cylinder diesel internal combustion engine in a heavy-duty 7.5-ton application. All vehicle variants are evaluated in eight vehicle driving cycles replicating different heavy-duty use-cases at different cargo levels, also considering the economical aspect of these different electrification options, calculating the payback periods for each powertrain option. The energy management control strategy, that determines the power split between the ICE and electric motor for HEV variants is an optimal one, based on Pontryagin’s Minimum Principle. All models are programmed in-house in Python 3.9.0.

An active multiobjective real-time vibration control algorithm for parallel hybrid electric vehicle

To improve the contradiction between vehicle dynamic performance and ride comfort by using the motor active output torque compensation method to reduce the torsional vibration of the hybrid electric vehicle (HEV) powertrain, an active multiobjective real-time vibration control (AMRVC) algorithm based on model prediction control (MPC) is proposed in this paper, and the current mainstream parallel HEV is taken as the research object to verify the proposed algorithm. Firstly, the torsional vibration model of the parallel HEV powertrain is established, simplified, and verified according to the experimental data, which provides the basis for the multimode adaptability and the application of the controller. Then, based on the state space equation of the torsional vibration system, the MPC controller considering the balance between ride comfort and power performance is designed, which can meet the multiobjective and real-time control effect of the hybrid power system while realizing the active vibration control. Finally, the effectiveness and real-time performance of the AMRVC algorithm are verified by multimode simulation analysis and hardware-in-the-loop (HIL) experiment. The simulation results show that the AMRVC algorithm based on MPC has good real-time application conditions, which can well coordinate the multiobjective requirements of vehicle ride comfort and dynamic performance, and remarkably improve the torsional vibration response of the powertrain by more than 55% in all HEV typical operating modes.

A Moment-of-Inertia-Driven Engine Start-Up Strategy for Four-Wheel-Drive Hybrid Electric Vehicles

During the moment-of-inertia (MoI) start-up process, an engine is cranked to ignite at 800 rpm with better fuel efficiency than at 250 rpm in the belt-driven start. As kinetic energy is employed, the work needed to crank the engine is also reduced. This article aims to develop an MoI-driven engine start-up method for a four-wheel-drive (4WD) vehicle. By modeling a 4WD hybrid electric vehicle (HEV) powertrain, a system dynamics model is developed into a state-space equation. The variable operating conditions of the engine and clutch are identified, and an adaptive model predictive controller (MPC) is designed. The goal of multioptimization is to control the engine speed trace, minimize the front and total axle torsion speed, and execute the torque demand. The optimization outputs are the engine torque set points, front electric motor (EM) torque set points, clutch nominal force set points, and rear EM set points. By predicting the engine fluctuation torque and considering the power source component constraints, an optimization algorithm is developed. This strategy is simulated and verified on the vehicle and powertrain models. The results indicate that the proposal achieves distinct fuel consumption improvement with an optimized drivability performance.

Optimal Eco-Routing for Hybrid Vehicles With Powertrain Model Embedded

Exploiting the full potential of hybrid electric vehicles (HEVs) requires suitable (i) route selection and (ii) power management. Due to coupling of the two subproblems, an integrated optimization problem is desired, i.e., optimizing simultaneously the route selection and the split between combustion engine and electric motor over the entire route selection. The resulting optimal route and vehicle operation can be used as a basis for a subordinate vehicle controller. We present an eco-routing approach that embeds a hybrid (mechanistic/data-driven) model of the HEV powertrain in an integrated routing and power management optimization problem. Formulating the integrated routing problem with the hybrid model yields a mixed-integer bilinear program which we reformulate and solve a mixed-integer linear program using a state-of-the-art solver. The results show the validity of the developed hybrid powertrain model and demonstrate that the eco routing approach with the powertrain model embedded can be applied to large-scale problems. We consider optimization for minimal travel time and minimum fuel consumption. The latter results in fuel demand reductions up to 70 %. Alternatively, we minimize the fuel consumption while constraining the travel time to a maximum value resulting in up to 50 % fuel demand reductions. The highest fuel demand reductions are achieved in urban environments. The entire framework is written in python and provided as an open-source version (MIT License) under <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://git.rwth-aachen.de/avt-svt/public/optimal-routing</uri> that can readily be applied.

Optimization for the minimum fuel consumption problem of a hybrid electric vehicle using mixed-integer linear programming

In this study, a mixed-integer linear programming (MILP) formulation for obtaining the operating points of hybrid electric vehicle (HEV) powertrains is proposed. This study focuses on linearizing the nonlinear terms, such as an engine fuel consumption map, or bilinear terms represented by the energy of the motor. To address this problem, a conventional piecewise linear (PWL) method and a multi-layer perceptron (MLP) regression approach are adopted. Although the optimal solution cannot be determined using a PWL approximation for the fuel consumption map, it can be obtained using an MLP regression. Furthermore, the PWL method achieves better results than the MLP approach in terms of the accuracy of its bilinear approximation. Obtaining the optimal solution using MILP helps in acquiring a Lagrange multiplication of the design variables by solving the dual problem, which allows an efficient design revision strategy to be obtained.

Active damping control of HEVs using Ansys and Matlab/Simulink software

This paper presents Parallel Hybrid Electric Vehicles (HEVs) powertrain design as well as a motor-based control approach that is designed to control or reduce driveline oscillations by introducing a Proportional-Integral-Derivative (PID) controller and a Fuzzy logic sliding mode controller. Because the torque of the electric motor can be decreased or increased more quickly than that of the Internal Combustion Engine (ICE), the vibration increases significantly. To solve this problem, an electric motor control-based Active Damping Control (ADC) strategy is employed to assure smooth driveline function and provide seamless driving experience for the driver. First, the basic level modeling of a hybrid electric powertrain in Ansys Simplorer environment is created and the performance was studied during the certification drive cycle. Thus, the main components of the powertrain– traction motor, battery and ICE – are researched, and basic models were built. The components were developed based on the Ansys software by using an automotive system level behavioral HEV library with VHDL-AMS language built in Ansys Simplorer environment. In addition, comparison of both controllers was presented. The simulation results show that using the ADC reduces more than 30 % of the driveline oscillations, thereby improving the drivability of HEVs.

Design Method for Hybrid Electric Vehicle Powertrain Configuration with a Single Motor

Existing design methods for hybrid power system configurations obtain new solutions based on experience, structure improvement or optimization, exhaustive searching, and the screening of schemes at the expense of less innovation and less efficiency. Furthermore, these methods lack mechanisms involving automotive theory to guide powertrain configuration design. In this study, a design method of configuration with a single motor based on basic schemes of speed and torque decoupling was proposed from the perspective of the hybrid electric vehicle fuel-saving mechanism. First, the coupling characteristics of speed and torque in the basic scheme were analyzed from four perspectives. Thereafter, new configurations that meet operation requirements were derived via configuration reconstruction, which combined the better basic schemes with brakes, clutches, and transmissions. A multidimensional evaluation and screening method based on dynamic performance, economic performance, and adaptability was built. A comparison of S-4 with Toyota Hybrid System, which was performed to demonstrate the feasibility of the design method, revealed that both configurations perform similarly in terms of economic performance, but the dynamic performance of the S-4 is greater by approximately 50%. The times required to attain 100 km/h from 0 km/h for THS and S-4 are 13.5 s and 6.69 s, respectively.

Modeling Transient Control of a Turbogenerator on a Drive Cycle

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables). Transient verification with WAVE-RT was conducted on WLTP and NEDC drive cycles, estimating dynamic energy recovery and fuel consumption improvement. Hints are given for a more advanced MIMO (Multiple-Input, Multiple-Output) control system architecture and calibration.&lt;/div&gt;&lt;/div&gt;

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains.

Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles

The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement.

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.