The battery thermal management (BTM) system plays an increasingly important role today in the safety of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as the battery capacity and power ratings keep growing. The BTM system is commonly coupled with the vehicle passenger cabin HVAC system. This integrated thermal system is a major onboard energy consumer, and its complexity brings challenges to its control. With the planned-ahead speed profile and the corresponding power trajectory obtained from the connected and automated vehicle (CAV) technology, predictive control makes a desirable option for the integrated system to maintain battery safety and passenger comfort while lowering energy consumption. However, in order to achieve both high accuracy and low cost in a wide control range, a very long prediction horizon and high sampling rate are both necessary. This will overwhelm the processing capacity of the onboard electronic control unit (ECU) if using conventional predictive control. In this paper, a two-stage predictive control strategy for the BTM and HVAC coupled system is proposed to solve this problem. In stage 1, based on the integrated cooling system efficiency features, a hierarchical and iterative dynamic programming (HIDP) scheme is designed to derive the optimal battery temperature trajectory to reach the set point at the end of the horizon with a modest computation burden. In stage 2, a control-oriented model is constructed for the cooling system and a model predictive controller (MPC) is accordingly built to track the trajectory from Stage 1 while enforcing the energy saving. A high set-point-tracking performance and as high as 10.61% energy saving for the cooling system in the UDDS cycle are verified by simulation results. The real-time implementation capability of the proposed strategy is demonstrated by the vehicle emulator experiments based on hardware in the loop (HIL) and a rapid control prototyping (RCP) platform.

Land transportation over the past two centuries has experienced astonishing advancement. Up until the 1860s, it took more than six months to get from the East Coast to the West Coast of the United States. Today, it may take only three days by automobile. We are even considering flying cars and there are air-taxi startup companies that have announced going public[1]. Vehicle propulsion electrification is at the core of this modern land vehicle revolution. However, the concept of electric vehicle traction is not new.

This article presents the state-of-the-art electric vehicle (EV) charging technologies that benefit from the wide bandgap (WBG) devices, which is regarded as the most significant revolution in the power electronics industry in the past few decades. First, the recent WBG device technology evolution, performance comparison, and reliability issues are introduced. Then, topology development, efficiency and power density boost, and cost reduction brought by the WBG devices for EV charging equipment, such as onboard chargers, fast-charging stations, and wireless chargers, are discussed. A figure of merit (FOM) for evaluating the performance of wireless chargers is also proposed. Finally, the EV charging technology roadmap forecast is presented based on the WBG devices' evolutionary trends.

As a significant part of the next-generation smart grid, electric vehicles (EVs) are essential for most countries to achieve energy independence, secure energy supply, and alleviate the pressure on environmental protection and energy security. Although EVs have grown rapidly, the slow recharge time is still the biggest obstacle to a wider application. While gasoline vehicles can pump enough gasoline in less than ten minutes, which can carry themselves a few hundred miles. However, most of today’s fast-charging techniques take half an hour only to provide very limited miles of electric driving range.

In both normal and fast wireless electric vehicle charging systems, constant current/constant voltage (CC/CV) charging profile, regardless of the variation of the battery state of charge, is one of the most essential characteristics to ensure the battery performance and reliability. The input zero phase angle (ZPA) is able to minimize the system volt-ampere rating, enhance the power transfer capability, and make it easy to achieve soft-switching operation over the full range of battery charging profile. Therefore, the load-independent CC and CV output characteristics with ZPA conditions are necessary for wireless charging systems. However, the existing methods that can achieve these functions either add power switches or need a large number of compensation components, which make the system inefficient, uneconomical, and bulky. In this paper, a unified resonant tuning configuration with minimum passive component counts is proposed to achieve CC and CV outputs at two ZPA operating frequencies. According to the proposed configuration, all the possible inductive power transfer (IPT) and capacitive power transfer (CPT) topologies are analogized. With these topologies, both CC and CV outputs with ZPA are achieved using a minimum number of compensation components and no additional power switches. Among all the simplest IPT topologies, a primary Series-secondary Series and Parallel (S-SP) compensation topology is illustrated to demonstrate the analysis.

The onboard battery self-heaters are employed to improve the performance and lifetime of the automotive lithium-ion batteries under cold climates. The battery performance is determined by the core temperature which is significantly higher than the surface temperature during the fast self-heating, while only the surface temperature can be directly measured. By estimating the core temperature to monitor the self-heating condition, the heating time and the energy consumption can be improved. However, the high-frequency heating current and the time-variant battery impedance cannot be measured in real time by a low-sampling-rate battery management system, so that the regular core temperature estimation methods are not applicable during the self-heating. To solve the issues, an online core temperature estimation algorithm based on the lumped thermal-electrical model is developed for the onboard ac self-heater. By implementing an extended state observer to compensate for the effect of the parameter uncertainties, the core temperature can be accurately detected even with the unknown internal resistance and root mean square (RMS) heating current. The experimental validation of 18 650 lithium-ion batteries shows that the core temperature estimation error is within only 1.2 °C. As a result, the self-heating time and energy consumption can be reduced by 50%.

The aim of this paper is to present the design and implementation of a hybrid energy storage system (HESS) with wireless power transfer (WPT). This study combines a battery bank and a supercapacitor bank to achieve high performance energy sources for electric scooters. The proposed system can be employed on commercial 48-V electric scooters for evaluation. The presented approach follows the WPT frequency standard of SAE J2954 to build the energy transmission. Based on the control of the state of charge on the battery bank, this paper proposes a three-mode strategy for hybrid energy management. The proposed HESS makes three compact and integrated contributions. First, it acts as the regulator to manage the input energy from WPT. Second, it manipulates the energy distributions to the battery and supercapacitor. Third, it provides a mechanism to initiate a short period of “negative current” to avoid excessive exploitation of the battery and facilitates palliation upon polarization. This function has the ability to extend battery life. The system performance of the electric scooter, as a result, can be improved. A double-sided LCC coil with a 15-cm air gap for wireless charging was built on a 0.8-kW electric scooter. In the wireless stage, the efficiency from the dc source to the dc output after the rectifier is 90.362%. The overall system efficiency from the dc source to the battery and supercapacitor is 86.4%.

To improve a vehicle's fuel efficiency when operating on roadways, this study develops an ecological driving system under the connected and automated vehicle (CAV) environment. The system includes three critical functions, including traffic state prediction, eco-driving speed control, and powertrain control implementation. According to the real-time traffic information obtained from vehicle-to-infrastructure and vehicle-to-vehicle communications, the embedded traffic state prediction model will estimate and predict the average speeds and densities of freeway subsections. With an objective of minimizing the fuel consumption, the eco-driving speed control function follows a two-stage hierarchical framework. The first stage, which is executed at the global level, aims to optimize the travel speed profile of the CAV over a certain time period. The second stage, local speed adaption, is designed to dynamically adjust the CAV's speed and make lane-changing decisions based on the local driving condition. The resulting control parameters will then be forwarded to the powertrain control system for implementations. To evaluate the proposed system, this study performs comprehensive numerical tests by using simulation models. This results confirm the effectiveness of the proposed system in reducing fuel consumption. Further comparisons with different models highlights the need to consider traffic state information in the first-stage optimization and lane-changing decision module in the local adaption function.

Battery is a commonly used energy storage component of the ESS (energy storage system) widely applied in the development of the PHEVs (plug-in hybrid electric vehicles). With the different system configurations and parameters selection, battery will undertake the different electrical stress, e.g., loss and current ripple which are believed to pose the discount of its longevity. This paper addresses the electrical impact and power capability of the battery pack under different system configurations. Influence of the internal impedance of the battery and capacitor will be emphasized. Simulation and experiments validated that, the topology of boost converter followed by a three-phase inverter has the advantages compared to the traditional stand-alone inverter-fed motor drive system in suppressing the high-order current harmonics and elevating the power capability.

This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed.