Graph deep-learning method for integrated topological and parametric optimization of hybrid electric vehicle powertrain

In the optimization designs for hybrid electric vehicle powertrain is usually a single objective and it is difficult to get a superior vehicle performance. In order to solve the problem, an optimization model of power-split hybrid electric vehicle powertrain is built with the optimization objectives of vehicle fuel economy and emission characteristics. Design variables are composed of the planetary speed ratio and the final drive speed ratio. The optimization results are measured using the simulation model in the CRUISE. The vehicle fuel economy and emission characteristics of the power-split hybrid electric vehicle are improved.

The bond graph method is used to model dynamics of various one-mode and two-mode series-parallel configurations of hybrid electric vehicle power trains. Proper handling of causalities results in minimum-realization models convenient for various simulation, analysis, and control design studies. The same, unified model structure can be used to describe the dynamics of all considered hybrid power trains. Several alternative variants of the power train dynamics model are also derived for various structures of driveline submodel.

The current legislation requires the increasing levels of vehicle powertrain electrification or hybridization to fulfill the limits of green-house gas emissions. Parallel hybrid electric vehicle (HEV) powertrain topologies are among the frequently used layouts, because of their easy applicability on an existing conventional powertrain by the addition of hybrid modules with mild, full, or plug-in capability. A more “HEV-tailored” approach merges a dedicated hybrid transmission (DHT), an ICE, a gearbox, and one or more electric motors more closely together, reducing HEV powertrain’s mechanical complexity and costs. The current paper investigates a multi-mode HEV with a DHT and two electric machines in optional electric serial or parallel operating modes. Great challenge for the multi-mode HEV topology with DHT is the component sizing: ICE, electric machines, and battery with optimized capacity, and yet fulfilling several roles. On one hand, satisfactory responsiveness on a dynamic driving demands (vehicle acceleration capabilities, top speed etc.). On the other hand, energy consumption achieving better levels than standard parallel HEV topologies. The other challenge for a multi-mode HEV powertrain – similarly as for all HEV powertrains – is a development and optimization of a powertrain supervisory control, based on energy management strategy. These challenges – overall DHT layout, component sizing, and energy management – closely interact and, therefore, must be optimized together. The paper addresses all these challenges using multi-parametric optimization toolchain, that combines a parametric HEV model, energy management strategy, and optimization software. The optimization software includes various optimization strategies: genetic algorithms, gradient based methods, or their combinations. The parametric HEV model is built in GT-Suite 0D/1D/3D multi-physics CAE system simulation software, that also includes different energy management strategies, either locally or globally optimal, considering the whole driving cycle. The toolchain is then used to evaluate the optimal component sizing for two different vehicle classes and fuel economy in a homologation driving cycle and in some real-world driving scenarios. All results are compared to in-house benchmark method.

Hybrid powertrain has become the standard configuration of some automobile models. The test system of hybrid vehicle powertrain was developed based on virtual instrument, using electric dynamometer to simulate the work of engines, to test the motor and control unit of the powertrain. The test conditions include starting, acceleration, and deceleration. The results show that the test system can simulate the working conditions of the hybrid electric vehicle powertrain under various conditions.

Optimization of Power Train and Control Strategy of a Hybrid Electric Vehicle for Maximum Energy Economy

The production of energy for traction in classical vehicles is performed by petrol or oil combustion. The consequence is green gas production. Paper deals with different power trains of electric hybrid and electric vehicles. Electric hybrid vehicles diminish the fuel consumption by giving to the internal combustion engine (ICE) better working conditions with respect to ICE revolutions and demanded power. The kinetic braking energy recovery is an important feature too. Electric vehicles are usually called “green vehicles”. It is true that they do not produce green gas on places where they are driving but it is also true that their traction energy has to be produced with another energy technology used in electric plants. The efficiency of the vehicle powertrain is important. The energy for transportation by vehicles in the world is great. Therefore it is very important what energy production technology for this traction purposes will be used.

Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios

In order to provide a new power train for electric hybrid vehicles that is simple in structure and lower in cost with higher fuel efficiency. A new power train for electric hybrid vehicle was designed with only one motor, one engine and a two speed ratio gear box. It has six different operation modes and can meet the vehicle driving requirements，and can provide pure electric drive, independent engine drive and hybrid drive with two speed ratios.

The main aim of this paper is to study the potential impacts in hybrid and full electrical vehicles performance by utilising continuously variable transmissions. This is achieved by two stages. First, for Electrical Vehicles (EVs), modelling and analysing the powertrain of a generic electric vehicle is developed using Matlab/Simulink-QSS Toolkit, with and without a transmission system of varying levels of complexity. Predicted results are compared for a typical electrical vehicle in three cases: without a gearbox, with a Continuously Variable Transmission (CVT), and with a conventional stepped gearbox. Second, for Hybrid Electrical Vehicles (HEVs), a twin epicyclic power split transmission model is used. Computer programmes for the analysis of epicyclic transmission based on a matrix method are developed and used. Two vehicle models are built-up; namely: traditional ICE vehicle, and HEV with a twin epicyclic gearbox. Predictions for both stages are made over the New European Driving Cycle (NEDC).The simulations show that the twin epicyclic offers substantial improvements of reduction in energy consumption in HEVs. The results also show that it is possible to improve overall performance and energy consumption levels using a continuously variable ratio gearbox in EVs.

A dual planetary gear hybrid electric vehicle is introduced in the paper. The speed and torque relations between different components of the power split hybrid system are systematically established using the lever analogy. Based on the electric power balance, the influence of the ratio between the engine speed and output speed of the power coupling system on the hybrid electric vehicle powertrain transmission efficiency is revealed, and the transmission efficiency optimization strategy is further proposed. Since some engine operation points are distributed in the low-efficiency region, the engine torque control strategy is designed using fuzzy control algorithm. The fuzzy membership function and fuzzy rules are optimized by particle swarm optimization to reduce the bad influence of expert experience and knowledge on the derivation of optimal engine torque. The vehicle powertrain and the control strategy models are established based on AVL/Cruise and MATLAB/Simulink platforms, and the joint simulation is conducted. Simulation results show that the proposed optimal energy management strategy can optimize the operation points of the engine. The final value of the battery state of charge is kept within a reasonable range, and the equivalent fuel consumption of the whole vehicle is reduced by 10.26% compared with that the transmission efficiency optimization control strategy.

<p>This paper presents the modelling and control of a hybrid electric vehicle powertrain. The powertrain, which has a series-parallel hybrid topology, was used as a case study. Mathematical models were developed for the internal combustion engine, electric motors, batteries and vehicle dynamics. A computational model was implemented in Matlab/Simulink and validated against experimental data, showing good agreement for fuel consumption. A rules-based control strategy was developed to approximate the logic used in the real vehicle. Global powertrain optimisation was then conducted using dynamic programming to minimise fuel consumption. Two cases were analysed, one optimising only the torque distribution and another also optimising the operating points of the internal combustion engine. The optimal control resulted in 9.5% and 10% lower fuel consumption than the non-optimal strategy, demonstrating the potential for consumption reduction. The results illustrate the importance of optimising multiple degrees of freedom in the powertrain and not only confining the engine to its optimal operating line. This study provides a methodology for developing optimal control strategies for hybrid vehicles using easily implementable tools. The findings highlight the importance of synergizing technology with informed driving habits to support global sustainability goals.</p>

The authors provide a copious literate review on powertrain design optimization for electric vehicle (EV) powertrains. The review shows a gap in the state of science regarding the consideration of all customer relevant requirements. Existing approaches either focus on performance and energy consumption or concentrate on vehicle dynamics. Based on this insight, the authors analyze customer requirements and derive all vehicle properties that are influenced by the powertrain concept. They find that costs, longitudinal performance, lateral dynamics, off-road capabilities and energy consumption are key vehicle properties. The powertrain concept affects all these properties and thus determines the satisfaction of customer requirements. Subsequently the authors describe a vehicle simulation model that represents all predefined customer requirements in dependence on the powertrain concept design.

Powertrain cooling concept selection process for hybrid electric vehicles

This work presents a methodology for modeling, designing, and scaling the electromagnetic, mechanical, and thermal characteristics of the main components in an electric powertrain (excluding the battery), i.e., a power electronics converter, an electrical machine, and mechanical transmission. Additionally, a framework for estimating the cost of these components is described. This framework takes into consideration not only the material cost, but also all major manufacturing steps required for the production of a component and their dependence on expected production volumes. Moreover, a procedure to optimize the design of an electric powertrain, taking advantage of the aforementioned models, is proposed. Finally, the powertrain for a passenger electric vehicle is optimized using the proposed methodology, and the results are compared in terms of the electrical machine topology, powertrain concepts, cost, weight, gear ratio(s) in the transmission, and overloading capabilities.

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.