Optimal Powertrain Research Articles

Comparative analysis of powertrain architectures for fuel cell light commercial vehicles in terms of performance and durability

At the present time, the critical climate situation has raised awareness about the importance of developing carbon-free technologies. In this context, fuel cell systems (FCS) have become one of the key technologies in the pathway to decarbonization. Given that road transport is a major contributor to greenhouse gas (GHG) emissions, this paper focuses on a specific segment of this sector: light commercial vehicles (LCVs). The current market situation shows that LCV manufacturers have not yet decided what is the appropriate powertrain architecture for this kind of vehicle. Thus, the current paper studies a wide range of possible FCS-based propulsive system designs, changing the size of the FCS, electric battery and H2 tank. These propulsive system architectures are analyzed concerning the performance of the vehicle, in terms of consumption and range, and the durability of its FCS. The evaluation of these different designs will be highly valuable for the LCV industry and manufacturers, as it allows to understand the optimal powertrain solution. The study demonstrates that a significant increase in range can be achieved with only a minor penalty in hydrogen consumption. Additionally, the research indicates that it is feasible to employ one of the most durable FCS designs while meeting LCV mission requirements with minimal consumption penalty. In conclusion, this paper provides valuable data to the ongoing research in this field, offering a detailed analysis of the impact of H2 consumption, autonomy, and durability of the FCS across various vehicle architectures under typical LCV driving conditions.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

A study of powertrain parameter optimization based on performance requirements of a vehicle platform

Powertrain top down design is a vital process for vehicle platformization and performance development. To obtain the optimal component size of a plug-in hybrid electric vehicle (PHEV) platform, this study proposes an optimization framework with the goal of minimizing performance redundancy. First, we analyze the characteristics of the powertrain architecture, and propose the indicators through performance gradient planning of the vehicle platform. Then we establish the mathematical model of the powertrain, and formulate the optimization problem. Particle Swarm Optimization (PSO) is used to solve the problem and a platform optimization problem with eight vehicle models is performed. Optimization results show that compared to traditional single vehicle powertrain optimization, platform based powertrain optimization can greatly reduce the number of components and save development costs through the sharing of components. With only three electric machines, two engines, two generators, and two sets of transmissions, the performance requirements of the entire platform can be met, and the maximum performance redundancy of different vehicle models is only 0.205. It can be seen that platform based powertrain optimization can greatly reduce vehicle costs and improve profit margins.

A comprehensive design pipeline for FCEV powertrain configuration considering the evolutionary development process and extreme economic performance

Due to stringent emission regulations, the zero-emission hydrogen fuel cell electric vehicles (FCEVs) are gaining significant attention, but their slow power response requires careful selection of auxiliary energy sources and power systems to optimize energy efficiency. This paper presents a comprehensive design pipeline for the FCEV powertrain, encompassing methods for powertrain configuration design, feasible domain design for component parameters, and parameter matching. Initially, a powertrain configuration design method based on evolutionary game theory is introduced, forecasting trends in energy-efficient powertrain configurations using a dynamic cost function that adjusts based on the proportions of different configurations. Next, a feasible domain design method is developed to establish fixed constraints on component parameters, ensuring a fair balance between performance needs and overall costs, including development and lifecycle expenses associated with the powertrain configuration. Subsequently, dynamic programming (DP) is used for parameter matching, assessing the utmost energy-saving potential. The cost function in this method incorporates the degradation cost of powertrain components, underscoring the economic performance of the target FCEV over its lifespan. Finally, simulation assessments indicate that the proposed design pipeline effectively identifies the optimal powertrain configuration for FCEVs, endowing each possible configuration with exceptional economic efficiency across different component parameters.

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

Energy-Oriented Hybrid Cooperative Adaptive Cruise Control for Fuel Cell Electric Vehicle Platoons.

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon.

Thermal and loss modeling of scaled permanent magnet synchronous machines for automotive driving cycles

With the goal of enhancing the efficiency of electric vehicles and improving the driving range, the appropriate dimensioning, so-called “right-sizing,” of the electric machines is a promising approach. This paper presents an efficient approach of scaling the thermal parameters in a low order lumped parameter thermal network (LPTN) model to estimate the temperature of the scaled permanent magnet synchronous machine (PMSM) by taking into account the temperature dependence of the power losses. At an early stage of development, the proposed scaling approach enables a preliminary evaluation of the thermal limit of the PMSM within the optimization of the electric powertrain and offers a notable benefit in that the low order LPTN model can easily be parameterized and scaled based on the experimental measurements. Validation for both axial scaling and radial scaling with factors ranging from 0.8 to 1.2 was carried out on a previously validated Ansys Motor-CAD model for typical automotive driving cycles. The maximum temperature error between the scaling approach and the Ansys Motor-CAD model is less than 3.5°C.

Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization

It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm.

Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency

The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology.

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

Abstract Low fidelity modeling approaches remain attractive due to an unrivaled ability to predict full turbine performance maps quickly compared to high-fidelity approaches such as computational fluid dynamics (CFD), especially in the preliminary design process. As improvements in performance on a component level approach a point of diminishing returns, the ability to efficiently optimize the complete charging system for a given duty is a topic attracting significant research interest. In the case of turbocharging applications, existing engine and powertrain simulations require turbine maps to calculate the turbine performance, which are usually obtained from experimental testing. Unfortunately, the need for extrapolation is unavoidable because of the limited range of testing data available, leading to inaccuracies especially at off-design conditions. To enable intensive modeling and optimization of complete vehicle powertrains for different drive cycles, the current piece of work seeks to combine the advantages of machine learning techniques and physical meanline modeling to facilitate faster, more accurate predictions of complete turbocharger maps. This paper presents a novel methodology for turbocharger turbine rotor and nozzle performance prediction based on hybrid modeling. The turbine rotor and nozzle were parameterized to conduct CFD simulations for a wide variety of turbine geometries, which were used to form a database to train an artificial neural network (ANN). The predicted losses provided by the ANN were then utilized in the meanline code, substituting for the conventional empirical loss models. As well as removing the need for empirical loss models, modifications were undertaken to the meanline approach to further enhance modeling accuracy. First, in order to accurately characterize the stage mass flow capacity, the losses occurring in the nozzle and rotor were subdivided into those occurring before and after the throat. A second novel aspect is that the aerodynamic blockage level at the rotor throat was implemented as a variable rather than a constant value. By training the ANN to predict the variation of blockage with geometry and operating condition, a more accurate depiction of the changing secondary flow fields could be achieved. The capabilities of the hybrid meanline modeling method were evaluated on several unseen test cases. The resulting predictions of efficiency and mass flowrate demonstrated strong correlation with CFD results and experimental test results. The hybrid meanline modeling method therefore displays great potential in wide range radial turbine performance prediction with enhanced accuracy in comparison to traditional approaches.

Optimal Powertrain Sizing of Series Hybrid Coach Running on Diesel and HVO for Lifetime Carbon Footprint and Total Cost Minimisation

This article aims to calculate, analyse and compare the optimal powertrain sizing solutions for a long-haul plug-in series hybrid coach running on diesel and hydrotreated vegetable oil (HVO) using a co-design optimisation approach for: (1) lowering lifetime carbon footprint; (2) minimising the total cost of ownership (TCO); (3) finding the right sizing compromise between environmental impact and economic feasibility for the two fuel cases. The current vehicle use case derived from the EU H2020 LONGRUN project features electrical auxiliary loads and a 100 km zero urban emission range requiring a considerable battery size, which makes its low carbon footprint and cost-effective sizing a crucial challenge. Changing the objective between environmental impact and overall cost minimisation or switching the energy source from diesel to renewable HVO could also significantly affect the optimal powertrain dimensions. The approach uses particle swarm optimisation in the outer sizing loop while energy management is implemented using an adaptive equivalent consumption minimisation strategy (A-ECMS). Usage of HVO fuel over diesel offered an approximately 62% reduction in lifetime carbon footprint for around a 12.5% increase in overall costs across all sizing solutions. For such an unconventional powertrain topology, the fuel economy-focused solution neither achieved the lowest carbon footprint nor overall costs. In comparison, CO2−cost balanced sizing resulted in reductions close to the single objective-focused solutions (5.7% against 5.9% for the CO2 solution, 7.7% against 7.9% for the TCO solution on HVO) with lowered compromise on other side targets (CO2 reduction of 5.7% against 4.9% found in the TCO-focused solution, TCO lowering of 7.7% against 4.4% found in the CO2-focused solution).

Energy efficiency and CO2 emission comparison of alternative powertrain solutions for mining haul truck using integrated design and control optimization

The heavy-duty mining haul truck (MHT) with hundreds tonnage could emit hundreds of times emissions over a passenger vehicle. The heavy burden of emissions and continuous global warming force the mining industry seeking clean powertrain solutions for MHT. This research optimizes several emerging clean powertrain solutions for a typical 240 tonnage MHT using integrated design and control optimization method, and compares the energy efficiency and carbon dioxide (CO2) emission to the benchmark MHT with a diesel engine and diesel-electric drive. The well-established components model and optimal control theory support the integrated optimization under given operation profile. Currently, the battery electric solution could cut 63 percent usage cost incorporated with the lithium-ion battery degradation cost and reduce 21 percent well-to-wheel CO2 emission, making the battery electric solution a competitive candidate. Though the hydrogen fuel cell emits 26 percent less CO2 emission than the diesel counterpart, the high investment cost and degradation cost hinder its wide application. Further improvement of hydrogen production, capital cost and durability of fuel cell, and fast-charging of lithium-ion battery could release huge potential in cleaner MHT. The study provides an approach to quantitatively compare clean powertrain solutions and identify the optimal powertrain parameters for cleaner MHT.

Unified Mode-Based Description of Arbitrary Hybrid and Electric Powertrain Topologies

To find the optimal powertrain design for hybrid and electric vehicles in terms of energy efficiency and performance, automatised optimisation tools are often applied. These require a mathematical model of the powertrain configuration, which is usually done on the level of components, such as planetary gear sets (PGSs), clutches and brakes. This, however, leads to a huge design space with lots of functional redundancy. In fact, each functional powertrain mode can be realised by various component combinations. In this paper, a novel mode-based modelling technique for arbitrary hybrid and electric powertrain configurations with up to one internal combustion engine (ICE) and two electrical machines (EMs) is proposed. This allows to easily analyse multi-mode hybrid and electric powertrains on a purely functional basis. Additionally, it reduces mode describing parameters to the minimum amount required. First, an existing graphical representation of hybrid powertrains with one ICE and one EM is analysed and formalised. Based on that, virtual powertrain nodes are defined. These are used to compose all hybrid and electric powertrain modes. Five of them are presented in detail and plotted within a three-dimensional ratio plot. Using a first case study, it is shown how the ratio plot can be used to analyse arbitrary multi-mode powertrains. Within a second case study, it is demonstrated how optimal parameter sets for single-mode powertrain configurations can be identified using the derived model.

Comparative analysis of powertrain optimization for Small electric vehicle based on range and weight for Retro-fitment

Range anxiety starts from the battery pack in an electric vehicle. It is one of the areas, where plenty of research is in progress on the development of battery packs. In Small Electric Vehicle (SEV), especially with retro fitment scenario, the floor-mounted battery pack is difficult. This paper focuses on proposing the battery pack concepts for SEV to maximize the range and weight distribution. In this paper, SEV is proposed for urban mobility requirements and the battery pack is mounted on the front and rear hood, which is based on the volume available for mounting in the SEV. Motor for SEV is taken from literature and the powertrain is optimized based on weight and range of SEV. The battery packs considered in this paper are Lead-acid, Nickel Metal Hydride, Lithium Polymer, Lithium-Sulfur, and Zinc-air. Range estimation is done for all the battery packs. Based on the projected battery pack weight at the front and rear hood, Weight distribution is given higher importance in the optimization of battery packs apart from range including its mounting. The Weight distribution is analyzed by keeping the Body in White (BIW) of SEV as constant and different variants of electric powertrains proposed.

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.

Influence of the Final Ratio on the Consumption of an Electric Vehicle under Conditions of Standardized Driving Cycles

Electric vehicles must improve their electric drive system efficiency and effectively use their limited energy to become a viable means of transportation. As such, these technologies have undergone substantial improvements from their initial conception. More efficient powertrains, together with improved storage technologies, have enabled more extended autonomy. However, from an engineering perspective, these systems are still a key area of research and optimization. This work presents a powertrain optimization methodology, developing energy savings and improving the performance of the electric vehicle by focusing on the differential. The proposed methodology includes a study of the dynamics of the electric vehicle and the generation of a mathematical model that represents it. By simulating the vehicle and varying the final ratio of the differential, a significant optimization for energy savings is obtained by developing a standardized driving cycle. In this case, NEDC, WLTC-2, and WLTC-3 test cycles are used. The results show that a short ratio improves performance, even if this implies a larger torque from the prime mover. Depending on the operating cycle used, an energy-saving between 3% and 8% was registered. An extended energy autonomy and an increment in the life-cycle of the batteries are expected in real driving scenarios.

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.

Design and optimisation of single motor electric powertrains considering different transmission topologies

In this paper, a systematic design approach is presented combining an analytic open-source design method for electrical machines (EM) with a novel transmission design approach tailored for the usage within a holistic powertrain optimisation. This allows a co-optimisation of both the EM and the transmission aiming at a globally optimal powertrain system for battery electric vehicles (BEVs). To systematically synthesise transmissions, basic elements are defined, which can be connected individually. Additionally, a generic transmission loss model is introduced allowing to account for transmission losses based on the design approach. Using this methodology, two one-speed and one two-speed transmission configurations are synthesised and optimised in a case study. Applying a multi-objective genetic algorithm, both EM and transmission parameters are optimised aiming at minimal electrical energy consumption and minimal powertrain cost. The results show that the optimal powertrain configuration is sensitive to both EM and transmission design parameters, demonstrating that a comprehensive co-optimisation is necessary to obtain globally optimal electric powertrain solutions.