In order to achieve lower fuel consumption and less greenhouse gas (GHG) emissions, we need higher efficiency vehicles with improved performance. Electrification is the most promising solution to enable a more sustainable and environmentally friendly transportation system. Electrified transportation vision includes utilizing more electrical energy to power traction and nontraction loads in the vehicle. In electrified powertrain applications, the efficiency of the electrical path, and the power and energy density of the components play important roles to improve the electric range of the vehicle to run the engine close to its peak efficiency point and to maintain lower energy consumption with less emissions. In general, the electrified powertrain architecture, design and control of the powertrain components, and software development are coupled to facilitate an efficient, high-performance, and reliable powertrain. In this paper, enabling technologies and solutions for the electrified transportation are discussed in terms of power electronics, electric machines, electrified powertrain architectures, energy storage systems (ESSs), and controls and software.

Making the Case for Electrified Transportation

Traction motors play a critical role in electrified vehicles, including electric, hybrid electric, and plug-in hybrid electric vehicles. With high efficiency and power density, interior permanent magnet (IPM) synchronous machines have been employed in many commercialized electrified powertrains. In this paper, three different IPM rotor design configurations, which have been used in electrified powertrains from Toyota, Nissan, and General Motors, are comparatively investigated. Each topology is redesigned and improved to meet new design requirements based on the same constraints. The designed motors are then compared and comprehensively evaluated for motor performance, torque segregation, demagnetization, mechanical stress, and radial forces. The results suggest that the single V-shaped configuration achieves the best overall performance and is thus recommended as the best candidate.

Design and Comparison of Interior Permanent Magnet Motor Topologies for Traction Applications

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.

Battery Thermal- and Health-Constrained Energy Management for Hybrid Electric Bus Based on Soft Actor-Critic DRL Algorithm

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

Battery-Involved Energy Management for Hybrid Electric Bus Based on Expert-Assistance Deep Deterministic Policy Gradient Algorithm

An overview of electricity powered vehicles: Lithium-ion battery energy storage density and energy conversion efficiency

Automotive electrification has become the centre of current transportation technology especially in the age of surging petroleum fuel prices and rising social awareness of vehicle emissions. A range of electrified powertrains from micro hybrid to full electric have been implemented in a wide variety of vehicle platforms from subcompacts to heavy-duty trucks and buses. The main focus of this paper is to comprehensively review the state-of-the-art electrified powertrains that have been developed and commercialised in the North American automotive industry. Vehicles are categorised based on different powertrain configurations and electrification levels. The powertrain structure and operating modes are also analysed in detail. In addition, a comprehensive database of electrified powertrains and their components is created. Electrified powertrains and the corresponding conventional powertrains are compared in terms of vehicle efficiency, powertrain complexity, and pump-to-wheel CO2 emissions.

State-of-the-art electrified powertrains - hybrid, plug-in, and electric vehicles

This work aims at presenting a design methodology capable of modeling, generating, and testing a large number of multimode power split hybrid electric vehicle transmission designs in a relatively s...

Rapid optimal design of a multimode power split hybrid electric vehicle transmission

The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is generally known for the complexity of its operation. This paper first addresses the challenge of developing an automated on-line near-optimal control strategy for these systems. Particularly, a machine learning logic based on supervised learning is developed for on-line selection of the HEV operating mode, thus the particular set of clutches to be engaged. An efficiency-based approach is then adopted to determine the optimal power-split between powertrain components. Later, the developed strategy finds integration in an optimal design methodology for multimode power-split HEVs considering the effectiveness and ease of on-line controllability. The obtained results are compared with the ones by the traditional HEV design methodology that only considers off-line energy management. The illustrated design methodology with on-line control reveals efficient at identifying the multimode HEV design that demonstrates optimal predicted fuel economy values and ease of on-line controllability simultaneously. Results suggest that the HEV design optimization procedure may produce different outcomes and demonstrate more effective when the evaluation of the on-line controlled operation is integrated.

Integration of On-Line Control in Optimal Design of Multimode Power-Split Hybrid Electric Vehicle Powertrains

Recent studies have addressed the development of optimal control strategies for hybrid electric vehicles (HEVs). Achieving global optimality for fuel economy prediction while minimizing the computational efficiency still is a research and development challenge. This paper aims at presenting a novel technique for managing the energy flows in a power split HEV named slope-weighted energy-based rapid control analysis (SERCA). After presenting the HEV plant model and the optimal control problem, the currently most adopted energy management strategies are analyzed. The SERCA technique is then illustrated, and its operating steps are detailed. The simulation results for the considered HEV energy management strategies in the standard drive cycles subsequently indicate that the SERCA can efficiently achieve near-optimal fuel economy, while limiting the computational costs. This suggests the potential use of SERCA for rapid component sizing of HEV powertrains.

Slope-Weighted Energy-Based Rapid Control Analysis for Hybrid Electric Vehicles

The production of multi-mode power-split hybrid vehicles has been implemented for some years now and it is expected to continually grow over the next decade. Control strategy still represents one of the most challenging aspects in the design of these vehicles. Finding an effective strategy to obtain the optimal solution with light computational cost is not trivial. In previous publications, a Powerweighted Efficiency Analysis for Rapid Sizing (PEARS) algorithm was found to be a very promising solution. The issue with implementing a PEARS technique is that it generates an unrealistic mode-shifting schedule. In this paper, the problematic points of PEARS algorithm are detected and analyzed, then a solution to minimize mode-shifting events is proposed. The improved PEARS algorithm is integrated in a design methodology that can generate and test several candidate powertrains in a short period of time.

Joel Roeleveld

Papers

State-of-the-art electrified powertrains - hybrid, plug-in, and electric vehicles

Rapid optimal design of a multimode power split hybrid electric vehicle transmission

Integration of On-Line Control in Optimal Design of Multimode Power-Split Hybrid Electric Vehicle Powertrains

Slope-Weighted Energy-Based Rapid Control Analysis for Hybrid Electric Vehicles

Mode-shifting Minimization in a Power Management Strategy for Rapid Component Sizing of Multimode Power Split Hybrid Vehicles