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The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

Conventional viscous shock absorbers, in parallel with suspension springs, passively dissipate the excitation energy from road irregularity into heat waste, to reduce the transferred vibration which causes the discomfort of passengers. Energy-harvesting shock absorbers, which have the potential of conversion of kinetic energy into electric power, have been proposed as semi-active suspension to achieve better balance between the energy consumption and suspension performance. Because of the high energy density of the rotary shock absorber, a rotational energy-harvesting shock absorber with mechanical motion rectifier (MMR) is used in this paper. This paper presents the assessment of vehicle dynamic performance with the proposed energy-harvesting shock absorber in braking process. Moreover, a PI controller is proposed to attenuate the negative effect due to the pitch motion.

Modelling of disc in brake squeal analysis is complicated because of the rotation of disc and the sliding contact between disc and pads. Many analytical or analytical numerical combined modeling methods have been developed considering the disc brake vibration and squeal as a moving load problem. Yet in the most common used complex eigenvalue analysis method, the moving load nature normally has been ignored. In this paper, a new modelling method for rotating disc from the point of view of modal is presented. First finite element model of stationary disc is built and modal parameters are calculated. Then the dynamic response of rotating disc which is excited and observed at spatial fixed positions is studied. The frequency response function is derived through space and time transformations. The equivalent modal parameter is extracted and expressed as the function of rotation speed and original stationary status modal parameters.

The study and prevention of unstable vibration is a challenging task for vehicle industry. Improving predicting accuracy of braking squeal model is of great concern. Closed-loop coupling disc brake model is widely used in complex eigenvalue analysis and further analysis. The coupling stiffness of disc rotor and pads is one of the most important parameters in the model. But in most studies the stiffness is calculated by simple static force-deformation simulation. In this paper, a closed-loop coupling disc brake model is built. Initial values of coupling stiffness are estimated from static calculation. Experiment modal analysis of stationary disc brake system with brake line pressure and brake torques applied is conducted. Then an optimization process is initiated to minimize the differences between modal frequencies predicted by the stationary model and those from test. Thus model parameters more close to reality are found.

The performance of electric vehicles could be enhanced by more flexible drivetrain configurations combined with advanced control methods. Based on four wheel independent driving and front and rear axle modular steering configuration, which was proposed by our research group last year, the problem of slippery under close-to-limit conditions are further discussed and simulated. A new torque vectoring method based on obtainable parameters and variables in real driving situations is introduced to reduce the sideslip when turning on low friction surfaces or with high speed. This method is developed from a comprehensive index, which reflects the stability and maneuverability, by adding additional torques when stability could not be compensated enough by basic torque vectoring. Besides, an improvement of adding a simu-Torsen differential mechanism is made to the model of the vehicle, which enables another control method with the same purpose as before.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

As mechanical damage induced thermal runaway of lithium-ion batteries has become one of the research hotspots, it is quite crucial to understand the mechanical behavior of component materials of lithium battery. This study focuses on the mechanical performance of separators and electrodes under different loading conditions and the error sources analysis for test results. Uniaxial tensile tests were conducted under both quasi-static and dynamic loading conditions. The strain was acquired through the combination of high speed camera and digital image correlation (DIC) method while the force was obtained with a customized load cell. Noticeable anisotropy and strain rate effect were observed for separators. The fracture mode of separators is highly correlated to the microscopic fiber orientation. To demonstrate the correlation microscopic images of separator material were obtained through SEM to match the facture edges of tensile tests at different loading directions.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

Metal foil is a widely used material in the automobile industry, which not only is the honeycomb barrier material but is also used as current collectors in Li-ion batteries. Plenty of studies proved that the mechanical property of the metal foil is quite different from that of the metal sheet because of the size effect on microscopic scale, as the metal foil shows a larger fracture stress and a lower ductility than the metal sheet. Meanwhile, the fracture behavior and accurate constitutive model of the metal foil with the consideration of the strain rate effect are widely concerned in further studies of battery safety and the honeycomb. This article conducted experiments on 8011H18 aluminum foil, aiming to explore the quasi-static and dynamic tension testing method and the anisotropic mechanical behavior of the very thin foil. Two metal foil dog-bone specimens and three types of notched specimens were tested with a strain rate ranging from 2 × 10−4/s to 40/s and various stress states.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Plug-in electric vehicle (PHEV) has a promising prospect to reduce greenhouse gas (GHG) emission and optimize engine operating in high-efficiency region. According to the maximum electric power and all-electric range, PHEVs are divided into two categories, including “all-electric PHEV” and “blended PHEV” and the latter provides a potential for more rational energy distribution because engine participates in vehicle driving during aggressive acceleration not just by motor. However, the frequent use of engine may result in severe emissions especially in low state of charge (SOC) and ahead of catalyst light-off. This study quantitatively investigates the impact of oil viscosity and driving mode (hybrid/conventional) on oil dilution and emissions including particle number (PN).

Lane-changing systems have been developed and applied to improve environmental adaptability of advanced driver assistant system (ADAS) and driver comfort. Lane-changing control consists of three steps: decision making, trajectory planning and trajectory tracking. Current methods are not perfect due to weaknesses such as high computation cost, low robustness to uncertainties, etc. In this paper, a novel lane changing control method is proposed, where lane-changing behavior is analogized to parallel parking behavior. In the perspective of host vehicle with lane-changing intention, the space between vehicles in the target adjacent lane can be regarded as dynamic parking space. A decision making and path planning algorithm of parallel parking is adapted to deal with lane change condition. The adopted algorithm based on rules checks lane-changing feasibility and generates desired path in the moving reference system at the same speed of vehicles in target lane.

Studying drifting dynamics and control could extend the usable state-space beyond handling limits and maximize the potential safety benefits of autonomous vehicles. Distributed electric vehicles provide more possibilities for drifting control with better grip and larger maximum drift angle. Under the state of drifting, the distributed electric vehicle is a typical nonlinear over-actuated system with actuator redundancy, and the coupling of input vectors impedes the direct use of control algorithm of upper. This paper proposes a novel automated drifting controller for the distributed electric vehicle. First, the nonlinear over-actuated system, comprised of driving system, braking system and steering system, is formulated and transformed to a square system through proposed integrative recombination method of control channel, making general nonlinear control algorithms suitable for this system.

A novel fault tolerant brake strategy for In-wheel motor driven electric vehicles based on integral sliding mode control and optimal online allocation is proposed in this paper. The braking force distribution and redistribution, which is achieved in online control allocation segment, aim at maximizing energy efficiency of the vehicle and isolating faulty actuators simultaneously. The In-wheel motor can generate both driving torque and braking torque according to different vehicle dynamic demands. In braking procedure, In-wheel motors generate electric braking torque to achieve energy regeneration. The strategy is designed to make sure that the stability of vehicle can be guaranteed which means vehicle can follow desired trajectory even if one of the driven motor has functional failure.

In low-temperature environment, heat supply requires considerable energy, which significantly increases energy consumption and shortens the mileage of electric vehicle. In the fuel cell vehicles, waste heat generated by the fuel cell system can supply heat for vehicle. In this paper, a thermal management system is designed for a the fuel cell interurban bus. Thermal management strategy aiming at temperature regulation for the fuel cell stack and the passenger compartment and minimal energy consumption is proposed. System model is developed and simulated based on AMESim and Matlab/Simulink co-simulation. Simulation results show that the fuel cell system can provide about 78 % energy of maximum heat requirement in -20 °C ambient temperature environment.

The vision for the future automotive chassis is to interconnect the lateral, longitudinal, and vertical dynamics by separately controlling driving, braking, steering, and damping of each individual wheel. A major advantage of all wheel drive electric vehicles with four in-wheel motors is the possibility to control the torque and speed at each wheel independently. This paper proposes a traction controller for such a vehicle. It estimates the road's adhesion potential at each wheel and adjusts each motor voltage, such that the longitudinal slip is kept in an optimal range. For development and validation, a full vehicle model is designed in ADAMS/View software, in co-simulation with motor and control elements, modeled in MATLAB/Simulink.

A hydraulic chamber is embedded in serial with the accumulator of a normal mono-tube magnetorheological fluid damper (MRFD). The damper stiffness can be adjusted by changing the accumulator volume with the hydraulic chamber. The hydraulic chamber is connected to an electric pump and controlled by the braking-by-wire (BBW) system. A modified bi-viscosity magnetorheological fluid (MRF) model that explicitly includes the parameter of control current is proposed. A dynamic model of this hydraulic MRFD is subsequently set up based on the MRF model. Experiments are conducted to validate the model and simulations are carried out to study the influences of accumulator volume on the external performances. Results show that the hydraulic chamber is able to provide rapid variations of the external force through accumulator volume changes.

The first several cycles determine the quality of an engine start. Low temperatures and air/fuel ratio cause incomplete combustion of the fuel. This can lead to dramatic increases in HC and PM emissions. In order to meet Euro V legislation requirements which have stricter cold start emission levels, it is critical to study the characteristics of cold and warm starting of engines in order to develop an optimized operation. The NO and THC emissions were measured by fast CLD and Fast FID gas analyzers respectively and PM in both nucleation and accumulation modes were measured by DMS500. The coolant temperature was controlled in order to guarantee the experiment repeatability. The results show that at cold start using RME60 produced higher NO and lower THC than the other tested fuels while combustion of HVO60 produced a similar level of NO but lower THC compared with mineral diesel. Meanwhile, the nucleation mode of mineral diesel was similar to RME60 but higher than HVO60.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.