Potential Of Hybrid Electric Vehicles Research Articles

Co-Optimization of Speed Planning and Power Management for Automated HEVs in Unstructured Road Scenarios

With the development of intelligent and connected vehicle technology, the simultaneous optimization of powertrain performance and vehicle motions provides an unprecedented perspective to further explore the energy-saving potential of hybrid electric vehicles (HEV). However, it is still very challenging because of the complex driving environment and large computational burdens, and hence, the co-optimization strategy under unstructured scenarios has rarely been studied. For unstructured road driving scenarios, this paper proposes a hierarchical optimal control system combing speed planning and energy management for an autonomous HEV. In the first layer, a vehicle stability constraints system is designed with consideration of both external road characteristics and vehicle dynamics to limit the longitudinal speed, preventing the vehicle from excessive yaw, sideslip, and rollover during a complex, unstructured road trip. Then, the improved gray wolf optimizer is designed to efficiently generate optimal speed and SOC trajectories to guide the vehicle motion behavior and power split while ensuring driver safety. In the second layer, a mechanical implementation system is applied to fulfill the control system framework. Finally, the performance of proposed co-optimization is comprehensively verified in terms of fuel economy, vehicle maneuverability, computational effectiveness, and adaptability. Compared with the sequential optimization, the improvements by co-optimization range from 9.38% to 13.03% in fuel economy and from 0.31% to 3.15% in vehicle maneuverability under different test cycles, while ensuring real-time capability.

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.

Data-driven predictive energy management and emission optimization for hybrid electric buses considering speed and passengers prediction

The energy-saving and emission reduction potential of hybrid electric vehicles are of great significance to the environment’s sustainable development. The trade-off between energy consumption economy and environmental friendliness is essential. To promote efficiency while reducing the emission of hybrid electric buses (HEB), we propose a novel predictive energy management strategy with passenger number prediction and exhaust emission optimization. An integrated prediction of vehicle speed and passenger number based on deep learning is proposed to predict future power demand accurately. An emission penalty is introduced into the objective function. Then, the impacts of passenger number prediction and the penalty on energy consumption and exhaust emissions are discussed. Simulation results show that the deep neural network predictor performs better in predicting speed and passenger number than Markov chain and radial basis function neural network predictors. The proposed energy management’s energy efficiency reaches 97.02% of global dynamic programming and 2.49% higher than that of instantaneous optimal control. With the exhaust emission optimization, CO2, CO, NOx, and HC emissions are reduced by 6.22%, 10.51%, 6.3%, and 4.83%, respectively, while the energy consumption cost is only increased by 1.34%. The proposed approach is verified to be environmentally friendly and energy-saving.

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs.

Energy based method to analyse fuel saving potential of hybrid vehicles for different driving cycles

Fuel saving potential of hybrid electric vehicles (HEVs) depends mainly on driving cycle and on sizing of powertrain components. Since a complete driving cycle, representing the whole life usage of a vehicle, is very long it is time consuming to predict the fuel saving potential, especially if many different types of HEVs should be analyzed. This paper presents an energy based method to quickly screen different types of HEVs for many and long driving cycles, in order to find interesting candidates for deeper and more accurate analysis. The technique used also allows to derive the fuel consumption analytically, and thus it is a very effective tool to explain the main fuel savings mechanisms of different types of HEVs and how they are influenced by the driving cycle. Some of the simplifications will lead to errors, but since the sign of the main errors are known it is still easy to draw several clear conclusions using the method.

Optimisation and control of a hybrid electric car

The paper examines the potential of hybrid electric vehicles and, in particular, a hybrid electric passenger car. Two operating objectives are identified, one for energy saving and the other for substituting petroleum fuel by electrical energy. The way in which the power train control and component rating can be optimised to meet these particular operating objectives is discussed. In the final part of the paper the performance of the optimised hybrid vehicles are compared with both IC engine and electric vehicles and the petroleum substitution design is shown to warrant further development.