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Abstract The emerging use of rechargeable batteries in electric and hybrid electric vehicles and distributed energy systems, and accidental fires involving batteries, has heightened the need for a methodology to determine the root cause of the fire. When a fire involving batteries takes place, investigators and engineers need to ascertain the role of batteries in that fire. Just as with fire in general, investigators need a framework for determining the role that is systematic, reliant on collection and careful analysis of forensic evidence, and based on the scientific method of inquiry. This article presents a systematic scientific process to analyze batteries that have been involved in a fire. It involves examining Li-ion cells of varying construction, using a systematic process that includes visual inspection, x-ray, CT scan, and possibly elemental analysis and testing of exemplars.

Abstract Letter from the Focus Issue Editors

Abstract The global automotive industry’s shift toward electrification hinges on battery electric vehicles (BEV) having a reduced total cost of ownership compared to traditional vehicles. Although BEVs exhibit lower operational costs than internal combustion engine (ICE) vehicles, their initial acquisition expense is higher due to expensive battery packs. This study evaluates total ownership costs for four vehicle types: traditional ICE-based car, BEV, split-power hybrid, and plug-in hybrid. Unlike previous analyses comparing production vehicles, this study employs a hypothetical sedan with different powertrains for a more equitable assessment. The study uses a drive-cycle model grounded in fundamental vehicle dynamics to determine the fuel and electricity consumption for each vehicle in highway and urban conditions. These figures serve a Monte Carlo simulation, projecting a vehicle’s operating cost over a decade based on average daily distance and highway driving percentage.

Abstract The swift progress of electric vehicles (EVs) and hybrid electric vehicles (HEVs) has driven advancements in battery management systems (BMS). However, optimizing the algorithms that drive these systems remains a challenge. Recent breakthroughs in data science, particularly in deep learning networks, have introduced the long–short-term memory (LSTM) network as a solution for sequence problems. While graphics processing units (GPUs) and application-specific integrated circuits (ASICs) have been used to improve performance in AI-based applications, field-programmable gate arrays (FPGAs) have gained popularity due to their low power consumption and high-speed acceleration, making them ideal for artificial intelligence (AI) implementation. One of the critical components of EVs and HEVs is the BMS, which performs operations to optimize the use of energy stored in lithium-ion batteries (LiBs).

Abstract Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity for vehicles to perform autonomous longitudinal control to navigate safely and efficiently through sequences of V2I-enabled intersections, known as connected corridors. Existing research has proposed several control systems to navigate these corridors while minimizing energy consumption and travel time. This article analyzes and compares the simulated performance of three different autonomous navigation systems in connected corridors: a V2I-informed constant acceleration kinematic controller (V2I-K), a V2I-informed model predictive controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent. A rules-based controller that does not use V2I information is implemented to simulate a human driver and is used as a baseline. The performance metrics analyzed are net energy consumption, travel time, and root-mean-square (RMS) acceleration.

Abstract Policy makers generally favor all-electric vehicles over hybrid-electric vehicles because of greater unit effectiveness in reducing carbon emissions. Since both systems use lithium-ion batteries, global demand for batteries is projected to grow 10-fold by 2030. If any step in the global battery supply-chain experiences bottlenecks, shortages can occur. We use a novel cost-effectiveness metric, carbon reduction per unit of battery capacity consumed, to rank emissions reductions accomplished by, alternatively, eight plug-in hybrid-electric vehicles, 75 hybrid-electric vehicles, and 230 mild hybrid-electric vehicles, which have the same total battery capacity as one all-electric vehicle. Our main finding, although counterintuitive, is that, with limited battery supplies, larger reductions in carbon emissions can be accomplished by hybrids than by all-electric vehicles.

Abstract Environmental concerns and technological progress push the development and market penetration of electric vehicles (EVs) and hybrid electric vehicles (HEVs). On the other hand, transportation systems are becoming more efficient by improved communication systems within vehicles and between vehicles and infrastructure. In this study, a driving cycle-based energy management strategy is developed for range-extended electric vehicles (REEVs) to increase system efficiency and equivalent vehicle range. A validated vehicle model is developed by critical subsystem testing and a comparative study is conducted to assess the developed strategy. The results showed that the optimized strategy can save CO2 emission by 6.21%, 1.77%, and 0.58% for heavy, moderate, and light traffic, respectively. Furthermore, the efficient use of a range extender (REx), guided by traffic data, extends the vehicle range, especially in heavy traffic conditions.

Abstract This article presented a real-time torque distribution strategy for a single-shaft, plug-in hybrid electric vehicle (PHEV) and investigated the effect of multiple operating parameters by using three working conditions, namely, steady operating conditions, acceleration operating conditions, and brake operating conditions. The corresponding experiments were performed according to the earlier discussed three working conditions.

Abstract Deep reinforcement learning has been utilized in different areas with significant progress, such as robotics, games, and autonomous vehicles. However, the optimal result from deep reinforcement learning is based on multiple sufficient training processes, which are time-consuming and hard to be applied in real-time vehicle energy management. This study aims to use expert knowledge to warm start the deep reinforcement learning for the energy management of a hybrid electric vehicle, thus reducing the learning time. In this study, expert domain knowledge is directly encoded to a set of rules, which can be represented by a decision tree. The agent can quickly start learning effective policies after initialization by directly transferring the logical rules from the decision tree into neural network weights and biases. The results show that the expert knowledge-based warm start agent has a higher initial learning reward in the training process than the cold start.

Abstract The Wankel engine is an eccentric rotary internal combustion engine known for its simplicity, compactness, reliability, and efficiency. However, issues related to sealing, efficiency, and emissions have hindered its widespread use. Recent advancements in sealing technology, novel designs, material coatings, and alternative fuels have addressed some of these problems, leading to improvements in Wankel engine performance. This study examines these advancements in Wankel engine technology and proposes three potential applications for future automotive use. The first application involves utilizing a Wankel engine with a continuously variable transmission to replace the powertrain in conventional vehicles. The second application suggests replacing the engine in a series-parallel electric-hybrid architecture with a Wankel engine. Lastly, the third application explores using a Wankel engine as a range extender for electric vehicles.

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Abstract The tailpipe zero-emission legislation has pushed the automotive industry toward more electrification. Regenerative braking is the capability of electric machines to provide brake torque. So far, the regenerative braking feature is primarily considered due to its effect on energy efficiency. However, using individual e-machines for each wheel makes it possible to apply the antilock braking function due to the fast torque-tracking characteristics of permanent magnet synchronous motors (PMSM). Due to its considerable cost reduction, in this article, a feasibility study is carried out to investigate if the ABS function can be done purely through regenerative braking using a mid-fidelity model-based approach. An uni-tire model of the vehicle with a surface-mount PMSM (SPMSM) model is used to verify the idea. The proposed ABS control system has a hierarchical structure containing a high-level longitudinal slip controller and a low-level SPMSM torque controller.

Abstract The current development of electric and hybrid electric vehicles has drawn more attention toward the development of electrical machines with high power densities. Though highly efficient, these machines heat up significantly during operation. By design, state-of-the-art water jacket cooling concepts remove the heat mainly through high internal thermal resistances of the electrical machine. The resulting maximum temperatures in the end winding region limit the achievable machine power output. In this study, alternative cooling concepts are presented, which efficiently use the existing heat conduction paths of an electric machine. For this purpose, two modeling methods for the stator windings were developed: a high-resolution approach that considers each individual wire and an abstract approach that uses zones of constant anisotropic thermal conductivity to specify the heat flow in the windings.

Abstract Exhaust emission standards for road vehicles require on-board diagnostics (OBD) of all comprehensive powertrain components (CCMs) impacting pollutant emissions. The legislation defines the generic malfunction criteria and pollutant threshold limits to trigger the component functional degradation. The electric drivetrain in xEV (more than one propulsion energy converter) applications substitutes or supports the internal combustion engine (ICE) operation with electric machine (EM) power. Malfunctions in the electric drivetrain will lead to an increase in ICE power demand. Hence, the electric drive system is classified as a comprehensive component in the OBD legislation. The regulation defines monitoring of the EM performance. The malfunctions that could prevent the EM(s) from properly operating emission control strategies, including any ICE control activation or electric drivetrain performance degradation, should be monitored by the OBD system.

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Abstract Lithium-ion batteries are the most crucial component of hybrid electric vehicles (HEVs) with respect to cost and performance. In this article, a new energy management strategy (EMS) is developed that improves fuel efficiency (FE) and suppresses the degradation of the battery. A hybridized two-layer algorithm that combines multi-objective nonlinear model predictive control (NMPC) with a rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The RB-NMPC is designed to optimize the torque split between the engine and electric motors while maintaining the maximum and minimum constraints of each component. The proposed EMS is incorporated into control-oriented vehicle models, and their performances are analyzed for different driving cycles by comparing with RB, dynamic programming (DP), and NMPC.

Abstract Hybrid electric vehicles (xHEV) are a critical enabler to fulfil the most recent CO2 and fuel economy requirements in key markets like North America, China, and Europe [1, 2]. Different levels of hybridization exist; the main differentiator is the power of the electric system and battery capacity. Increased electrical power enables the vehicle to run more often in electric mode and recuperate energy from braking, which enhances the saving potential [3]. Mild (MHEV) and plug-in hybrid vehicles (PHEV) impose different duty cycles on the engine compared to a conventional powertrain, potentially altering the degradation mechanisms of the lubricant, and challenging the basis on which the lubricant should be condemned [4]. The biggest concerns are water and fuel dilution [5], which promote corrosion and can form emulsions [6].

Abstract In Asian countries, small two-wheelers form a major share of the automobile segment and contribute significantly to carbon dioxide (CO2) emissions. Hybrid drives, though not widely applied in two-wheelers, can reduce fuel consumption and CO2 emissions. In this work three hybrid topologies, viz., P2 (electric motor placed between engine and transmission), P3 (electric motor placed between transmission and final drive), and power-split concepts (with planetary gear-train) have been modeled in Simulink, and their fuel consumption and emissions under the World Motorcycle Test Cycle (WMTC) have been evaluated. A physics-based model for the Continuously Variable Transmission (CVT) was used which is capable of predicting its transient characteristics. A map-based fuel consumption model and a Neural Network (NN)-based transient emission model were used for the engine.

Abstract Among the myriad of potential hybrid powertrain architectures, selecting the optimal for an application is a daunting task. Whenever available, computer models greatly assist in it. However, some aspects, such as pollutant emissions, are difficult to model, leaving no other option than to test. Validating plausible options before building the powertrain prototype has the potential of accelerating the vehicle development even more, doing so without shipping components around the world. This work concerns the design of a system to virtually couple—that is, avoiding physical contact—geographically distant test rigs in order to evaluate the components of a powertrain. In the past, methods have been attempted, either with or without assistance of mathematical models of the coupled components (observers). Existing methods are accurate only when the dynamics of the systems to couple are slow in relation to the communication delay.

Abstract Engine thermal management (ETM) is a promising technology that allows the reduction of harmful emissions and fuel consumption when the internal combustion engine (ICE) is started from a cold state. The key technology for ETM is the decoupling of the cooling pump from the crankshaft and the actuation of the pump independently. In this article, an electric engine cooling pump has been designed through a novel experimentally based procedure and operated on a vehicle equipped with an advanced turbocharged gasoline engine, particularly interesting for its hybridization potential. In the first phase, a dedicated experimental campaign was conducted off board on an engine identical to the one equipped in the vehicle to assess the characteristics of the cooling circuit and the reference pump performances.