Commercialization of Lithium Battery Technologies for Electric Vehicles

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

To fulfill reliable battery management in electric vehicles (EVs), an advanced State-of-Charge (SOC) estimator is developed via machine learning methodology. A novel genetic algorithm-based fuzzy C-means (FCM) clustering technique is first used to partition the training data sampled in the driving cycle-based test of a lithium-ion battery. The clustering result is applied to learn the topology and antecedent parameters of the model. Recursive least-squares algorithm is then employed to extract its consequent parameters. To ensure good accuracy and resilience, the backpropagation learning algorithm is finally adopted to simultaneously optimize both the antecedent and consequent parts. Experimental results verify that the proposed estimator exhibits sufficient accuracy and outperforms those built by conventional fuzzy modeling methods.

Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

With the popularity of electrical vehicles, the lithium-ion battery industry is developing rapidly. To ensure battery safe usage and to reduce its average lifecycle cost, accurate state of charge (SOC) tracking algorithms for real-time implementation are required for different applications. Many SOC estimation methods have been proposed in the literature. However, only a few of them consider the real-time applicability. This paper classifies the recently proposed online SOC estimation methods into five categories. Their principal features are illustrated, and the main pros and cons are provided. The SOC estimation methods are compared and discussed in terms of accuracy, robustness, and computation burden. Afterward, as the most popular type of model-based SOC estimation algorithms, seven nonlinear filters existing in literature are compared in terms of their accuracy and execution time as a reference for online implementation.

An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

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

Electric machines have broadly been used in many industries including the transportation industry. With the evolving trend of electrification in transportation, electric machines with higher power density and higher efficiency are demanded and, thus, more stringent thermal management requirements are needed for electrified vehicle applications. This study comprehensively presents various important aspects of thermal management in electric machines with the main focus on transportation applications. Design considerations, challenges, and methods for enhanced thermal management are discussed. Fundamental thermal properties of common materials are presented and sources of losses in various parts of machines are explained. Furthermore, typical cooling techniques and thermal analysis approaches for electric machines are reviewed in detail. This study will serve as a reference guideline for machine designers, who are interested in thermal management, and for thermal researchers working on electric machines.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-est.2015.0050

Thermal management of electric machines

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

With the era of modern vehicle electrification, electric machines with high traction torque–speed output and compact volume are highly desired. This paper presents a family of switched reluctance machine configurations that are composed of double rotors and one stator integrated in one machine housing. The machines are potentially more compact and lower cost, while providing two independent mechanical outputs suitable for hybrid electric vehicle transmissions. The detailed design process of a double-rotor switched reluctance machine (DRSRM) is presented, comprising of analytical calculations, finite-element field analysis, and full-drive system simulation. Additionally, some optimizations are applied to maximize the machine performance and minimize the machine weight and volume. A scaled prototype machine is designed and built according to chosen vehicle drive cycles to evaluate and validate the DRSRM prototype.

Yinye Yang

Papers

Making the Case for Electrified Transportation

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

Thermal management of electric machines

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

Double-Rotor Switched Reluctance Machine (DRSRM)