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In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

During driving conditions, when it is needed to transition from Electric Vehicle (EV) to Hybrid Vehicle operation, synchronization of the engine with the shaft and transmission is essential to enable clutch engagement and, subsequently, providing engine power to the wheels. Challenges arise when the engine must generate power to move itself and cannot rely on electric motors for precision. Cost-effective hybrid vehicle propulsion architectures which utilize small 12V belt-starter generators (BSGs) to initiate engine activation are inherently affected. In these situations, a speed profile that balance rapid response and control effort while considering system limitations to mitigate undesirable overshoots and delays, is required. This paper presents a Linear Quadratic Integral (LQI) approach to formulate a speed reference profile that ensures optimal engine behavior.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

This paper introduces a novel approach to modeling Torque Converter (TC) in conventional and hybrid vehicles, aiming to enhance torque delivery accuracy and efficiency. Traditionally, the TC is modelled by estimating impeller and turbine torque using the classical Kotwicki’s set of equations for torque multiplication and coupling regions or a generic lookup table based on dynamometer (dyno) data in an electronic control unit (ECU) which can be calibration intensive, and it is susceptible to inaccurate estimations of impeller and turbine torque due to engine torque accuracy, transmission oil temperature, hardware variation, etc. In our proposed method, we leverage an understanding of the TC inertia – torque dynamics and the knowledge of the polynomial relationship between slip speed and fluid path torque. We establish a mathematical model to represent the polynomial relationship between turbine torque and slip speed.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.

This paper proposes an optimization-based transmission gear shifting strategy for electrified powertrains with a transmission. With the demand for reduced vehicle emissions, electrified propulsion systems have garnered significant attention due to their potential to improve vehicle efficiency and performance. An electrified propulsion system architecture of significance includes multiple electric motors and a transmission where some driveline actuators can transmit torque through changing gear ratios. If there is at least one electric motor arranged before the input of the transmission and at least one after the transmission output, a unique design opportunity arises to shift gears in the most energy efficient manner.

During input tracking, closed-loop performance is strongly influenced by the dynamic of the system under control. Internal and external delays, such as actuation and measurement delays, have a detrimental effect on the bandwidth and stability. Additionally, production controllers are discrete in nature and the sampling time selection is another critical factor to be considered. In this paper we analyze the impact of both transported delay and controller sampling time on tracking performance using an electric machine speed-control problem as an example. A simple linear PI controller is used for this exercise. Furthermore, we show how the PI parameters can be adjusted to maintain a certain level of performance as the delays and sampling times are modified. This is achieved through an optimization algorithm that minimizes a specifically designed cost function.

One of the building blocks of the Stellantis hybrid powertrain embedded control software computes the maximum and minimum values of objective functions, such as output torque, as a function of engine torque, hybrid motor torque and other variables. To test such embedded software, an offline reference function was created. The reference function calculates the ideal minimum and maximum values to be compared with the output of the embedded software. This article presents the offline reference function with an emphasis on mathematical novelties. The reference function computes the minimum and maximum points of a linear objective function as a function of six independent variables, subject to 42 linear and two nonlinear constraints. Concave domains, curved surfaces, disjoint domains and multiple local extremum points challenge the algorithm. As a theorem, the conditions and methods for running trigonometric calculations in 6D Euclidean space are presented.

The Brake judder is a low-level vibration caused due to Disc Thickness Variation (DTV), Temperature, Brake Torque Variation (BTV), thermal degradation, hotspot etc. which is a major concern for the past decades in automobile manufacturers. To predict the judder performance, the modelling methods are proposed in terms of frequency and BTV respectively. In this study, a mathematical model is constructed by considering full brake assembly, tie rod, coupling rod, steering column, and steering wheel as a spring mass system for identifying judder frequency. Simulation is also performed to predict the occurrence of brake judder and those results are validated with theoretical results. Similarly, for calculating BTV a separate methodology is proposed in CAE and validated with experimental and theoretical results.

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.

Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery.

The complexities involved in an optimization problem at a system level require knowledge base that has information on different approaches and customization of these approaches to a specific class of the optimization problems. One approach that is commonly used is the metamodel based design optimization. The metamodel is 1) a conceptual model for capturing, in abstract terms, essential characteristics of a given optimization problem, and 2) a schema of sufficient formality to enable the problem modeled to be serialized to statements in a concrete optimization language [1]. Optimization is performed based on this metamodel. This metamodel approach has been proven effective and accurate in providing the global optimum. Depending upon the computational hardware availability in an organization, the metamodel based optimization could be much faster way of achieving the optimized solution. However, the accuracy of the optimization is highly dependent on the quality of metamodel generated.

This paper will focus on automotive development highlights of FCA US LLC magnesium intensive closures components. Deploying lightweight materials is one of many key strategies that has been implemented to reduce vehicle mass and improve overall fuel economy while maintaining rigorous functional objective performance. This paper will outline some basic design and manufacturing considerations for magnesium closures. The development of the 2017 Chrysler Pacifica liftgate and 2018 Jeep® Wrangler swing gate along with the two generations of magnesium spare tire brackets will be the focus.

One of the advantages of hybrid vehicles is the ability to operate the engine more optimally at a low brake specific fuel consumption (BSFC) as compared to conventional vehicles. This ability of hybrid vehicles is a major factor contributing to the fuel economy improvement over conventional vehicles. Unlike conventional gasoline powertrains, hybrid powertrains allow engine to be switched off and use battery power to propel vehicles. In order to maintain battery state of charge neutral operation between the start and end of a drive cycle, the net electrical energy consumption from the battery requires to be zero. An optimization algorithm can be developed and calibrated in different ways to achieve net zero battery energy over the cycle. For instance, the engine can be operated at powers higher than the power of the drive cycle to charge the battery. This accumulated energy can be used for all-electric propulsion by turning off the engine.

In response to demands for higher fuel economy and stringent emission regulations, OEMs always strive hard to improve component/system efficiency and minimize losses. In the driveline system, improving the efficiency of an automotive rear-axle is critical because it is one of the major power-loss contributor. Optimum oil-fill inside an axle is one of the feasible solutions to minimize spin losses, while ensuring lubrication performance and heat-dissipation requirements. Thus, prior to conducting vehicle development tests, several dyno-level tests are conducted to study the thermal behavior of axle-oil (optimum level) under severe operating conditions. These test conditions represent the axle operation in hot weather conditions, steep grade, maximum tow capacity, etc. It is important to ensure that oil does not exceed its thermal limits (disintegration of oil leading to degradation).

Rapid Compression Machines (RCM) offer the ability to easily change the compression ratio and the pressure/mixture composition/temperature to gather ignition delay data at various engine relevant conditions. Therefore, RCMs with optical access to the combustion chamber can provide an effective way to analyze macroscopic spray characteristics needed to understand the spray injection process and for spray model development, validation and calibration at conditions that are suitable for engines. Fuel surrogates can help control fuel parameters, develop models for spray and combustion, and perform laser diagnostics with known fluorescence characteristics. This study quantifies and evaluates the macroscopic spray characteristics of multicomponent gasoline surrogates in comparison to their gasoline counterparts, under gasoline direct injection (GDI) engine conditions.

In this work autoignition delay times of two multi-component surrogates (high and low RON) were experimentally compared with their target full blend gasoline fuels. The study was conducted in a rapid compression machine (RCM) test facility and a direct test chamber (DTC) charge preparation approach was used for mixture preparation. Experiments were carried over the temperature range of 650K-900K and at 10 bar and 20 bar compressed pressure conditions for equivalence ratios of (Φ =) 0.6-1.3. Dilution in the reactant mixture was varied from 0% to 30% CO2 (by mass), with the O2:N2 mole ratio fixed at 1:3.76. This dilution strategy emulates exhaust gas recirculation (EGR) substitution in spark ignition (SI) engines. The multicomponent surrogate captured the reactivity trends of the gasoline-air mixtures reasonably well in comparison to the single component (iso-octane) surrogate.

The heat generated by an internal combustion engine must be dissipated to maintain acceptable component temperatures throughout the entire powertrain system under all operating conditions. However, under cold start conditions it is beneficial to retain this available heat to achieve faster warm-up in order to reduce fuel consumption. In modern engines there are several components in the coolant circuit that are used to accelerate the warm-up of sub-system fluids such as the engine oil, transmission oil and axle oil. The magnitude of the fuel consumption reduction will depend on how these rapid warm-up devices are arranged, combined and controlled. This paper describes a methodology that was developed to optimize the distribution of coolant heat in the powertrain system during warm-up. A comparative study can be performed to optimize the arrangement of each heat exchanger in any given powertrain system to minimize cost and time early in development.

Lead-acid batteries have been widely used in automotive applications. Extending battery life and reducing battery warranty requires reducing any deteriorating to battery internals and battery electrolyte. At the end of battery life, it is required to maintain at least 50% of its initial capacity [1,2]. The rate of battery degradation increases at high battery temperatures due to increased rate of electrochemical reactions and potential loss of battery electrolyte. For Lead-Acid batteries, an electrolyte solution consists of diluted sulfuric acid. Battery electrolyte/water loss affects battery performance. Water loss is caused by high internal battery temperature and gassing off due to battery electrochemistry. High temperatures, high charging rates, and over charging can cause a loss of electrolyte in non-sealed batteries. In sealed batteries, the same factors will cause an increase in temperature and pressure which can eventually result in the release of hydrogen and oxygen gases.

Vehicle suspension parts are subjected to variable road loads, manufacturing process variation and high installation loads in assembly process. Seam welding can be considered as such process to connect more components and parts. Typical in a Mc Pherson suspension system stabilizer bar link is connected to the strut assembly through ball stud and clamped to a bracket welded to the outer strut tube. Cracks have been observed in the stabilizer bar link bracket welds of vehicles in the field, effecting the functionality of the suspension system. During preliminary phase of product development CAE assessment of the seam weld is carried out against road load data, if the design does not meet the targets enabler studies are carried out in an iterative approach. Various design variables (control factors) can be considered to carry out the iterations.