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Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

An investigation was performed to study the effects of convection, diffusion and thermophoresis on diesel exhaust soot deposition inside a plate-type rectangular cooling section for recirculation (EGR) applications since deposited soot can be detrimental to the heat transfer efficiency of EGR cooling devices. A non-destructive neutron radiography technique was used to measure the soot deposition thickness distribution on the plate surface inside the cooling chamber. The chamber cooled with an inlet water coolant of 20 and 40°C, was installed in a modified exhaust system of a 2.4kW diesel engine and subjected to a mass flow rate of 20kg/hr of diesel exhaust ranging 0 to 3 hours with the exhaust gas temperature at 260°C upstream of the cooling chamber. In this work, the effect of cooling temperature and operation time on thermophoretic deposition was investigated.

We have studied the formability of continuous strip cast (CC) AA5754 aluminum alloy for automotive applications. Strip casting technology can considerably reduce material cost compared with conventional direct chill (DC) cast aluminum sheets. However, the CC material tends to exhibit much less post-localization deformation and lower fracture strains compared with DC sheets with similar Fe content, although both alloys show similar strains for the onset of localization. Bendability of the CC alloy is also found to be inferior. The inferior behavior (post-necking and bendability) of the CC alloy can be attributed to the higher incidence of stringer-type particle distributions in the alloy. The formability of the AA5754 alloy has also been studied using two dimensional microstructure-based finite element modeling. The microstructures are represented by grains and experimentally measured particle distributions.

Magnesium AZ80 is a medium strength alloy with good corrosion resistance and very good forging capability which offers an affordable commercial alternative to the Mg ZK60 alloy used for wheels in racing cars. Extending the market of Mg AZ80 alloy to automotive wheels requires a better understanding of macro- and micro-properties of this structural material, especially its forging behaviour. In this study the deformation behaviour of Mg AZ80 alloy is characterized by uniaxial compression tests from ambient to 420°C at a variety of strain rates using a Gleeble 1500 simulator. A constitutive relationship coupling materials work hardening and strain rate and temperature dependences is calibrated based on test results. This flow behaviour is input into a finite element model to simulate the forging operation of an automotive wheel with ABAQUS codes.

An investigation was performed to study the soot deposition and its effect on heat transfer in a cooled cylindrical section. The soot layer thickness was measured using a non-destructive neutron radiography technique. Experiments were performed for a diesel exhaust mass flow rate of 20kg/hr or Reynolds number of approximately 9,000, initial inlet coolant temperatures of approximately 22 and 40°C, and exposure times from 1 to 3 hours. The results show that the nominal soot layer thickness was approximately uniform in the flow direction, hence, the thicker soot layer observed near the entrance by Ismail et al. [8] and de la Cruz et al. [9] was due to entrance effects. The deposited soot layer shows evidence of long wavy thickness variations that appears to be due to a soot re-entrainment and re-deposition moving bed type mechanism. The soot thickness increased and the long wavy variations persisted for larger soot thicknesses when the coolant temperature or wall temperature was lower.

The energy used for cabin cooling and heating can drastically reduce the operating range of electric vehicles. The energy efficiency and performance of the cabin heating, ventilation and air conditioning (HVAC) system depend on the system configuration and ambient conditions. The presented research investigates the energy efficiency and performance of cabin thermal management in electric vehicles. A simulation model of cabin heating and cooling systems was developed in the AMESim software. Simulations were carried out in the standard test cycles and one real-world driving cycle to take into account different driving behaviors and environments. The cabin thermal management performance was analyzed in relation to ambient temperature, system efficiency and cabin thermal balance. The simulation results showed that the driving range can shorten more than 50% in extreme cold conditions.

Advanced engine test methods incorporate several different sensing and signal processing techniques for identifying and locating manufacturing or assembly defects of an engine. A successful engine test method therefore, requires advanced signal processing techniques. This paper introduces a novel signal processing technique to successfully detect a faulty internal combustion engine in a quantitative manner. Accelerometers are mounted on the cylinder head and lug surfaces while vibration signals are recorded during engine operation. Using the engine's cam angular position, the vibration signals are transformed from the time domain to the crank-angle domain. At the heart of the transformation lies interpolation. In this paper, linear, cubic spline and sinc interpolation methods are demonstrated for reconstructing vibration signals in the crank-angle domain.

The constitutive behavior of aluminum alloy sheet and their resistance spot welds at large strains is critical for light weight vehicle design analysis and life prediction. However, data from uniaxial tensile tests are usually limited to small strains or by material instability. A novel technique was developed using digital image correlation coupled with a modified shear test to directly measure the stress - strain curves of aluminum alloy sheet at large strains. The modified shear sample prevents end rotation of the shear zone as compared to the ASTM B831 test. The results show that the effective stress - effective strain curves from shear tests match those obtained by uniaxial tension, but only by incorporating material anisotropy using the Barlat-Lian yield function. For the first time, the technique was applied to aluminum resistance spot welds to determine both the shear strength and stress-strain curves of spot welds at large strains.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

The application of high strength steels in tube hydroforming is being considered as one of the most effective ways to achieve the overall weight reduction without compromising the vehicle safety (crashworthiness). In this paper, the tensile behaviour of high strength dual-phase steel DP600 was investigated. The microstructure, mechanical performance and damage evolution was evaluated. A new finite element (FE) model based on crystal plasticity theory was developed to investigate large strain phenomena in multi-phase materials.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.

The purpose of this work was to initiate a comparative evaluation of the aqueous corrosion resistance of ferritic stainless steels currently used to fabricate automotive exhaust systems. Both acid condensate and double loop electrochemical potentiokinetic reactivation (DL-EPR) testing using both as-received and heat treated test coupons prepared from Types 409, 409Al, 436 and 439 stainless steel was conducted for this purpose. A truncated version of an in-house acid condensate testing protocol revealed that Type 409Al stainless steel was the most resistant to corrosion of the four ferritic stainless steels examined, whereas Type 409 stainless steel was the least resistance to corrosion.

The purpose of this study was to conduct a comparative corrosion assessment of alloys and coating schemes of interest for the fabrication of multi-material lightweight vehicle architectures. Alloys considered for this application included galvanized high strength low alloy steel, aluminum alloy AA6111 and magnesium alloy ZEK100. The coating scheme considered for corrosion protection included a layered paint top-coat scheme that was applied to a pre-treated surface. The pre-treatments included an alloy-specific commercial conversion coating (CC) and a plasma electrolytic deposition (PED) process that was applied only to the ZEK100 material. The corrosion assessment of the scribed coated alloy panels was conducted after 1000 h exposure in the ASTM B117 salt fog environment. Characterization of the mode and extent of corrosion damage observed and the role played by the exposed alloy microstructure utilized both light optical microscopy and electron microscopy.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

This paper presents a compact thermal management solution for a high-power traction inverter. The proposed design utilizes a stacked cooling system that enables heat extraction from two of the largest heat sources in a power inverter: the power module and the DC-link capacitor. The base plate of the power module has circular pin fins while the capacitor comes with a flat surface which must be placed on a cold plate to provide the adequate heat dissipation. Incorporating individual cooling mechanisms for the DC-link capacitor and the power module would increase the weight, complexity and overall volume of the inverter housing. The proposed cooling system mitigates these problems by integrating the cooling mechanisms of the power module and the DC-link capacitor within a single cooling system. The cooling mechanism is designed to provide a uniform coolant flow with minimal pressure drop across the heat sink of the power module and DC-link capacitor.

Using standard tensile testing methods, the material properties of AKDQ and DP600 steels tubes along the axial direction were determined. A novel in-situ optical strain mapping system ARAMIS® was utilized to evaluate the strain distribution during tensile testing along the axial direction. Microstructural and damage characterization was carried out using microscopy and image analysis techniques to compare the damage evolution and formability of both materials. Failure in both steels was observed to occur via a ductile failure mode. AKDQ was found to be the more formable material as it can achieve higher strains, total elongations and thinning prior to failure than the higher strength DP600.