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Off-road vehicles are increasingly adopting hybrid and electric powertrains for improved mobility, range, and energy efficiency. However, their cooling systems consume a significant amount of energy, affecting the vehicle’s operating range. This study develops a predictive controller for the battery thermal management system in an autonomous electric tracked off-road vehicle. By analyzing the system dynamics, the controller determines the optimal preview horizon and controller timestep. Sensitivity analysis is conducted to evaluate temperature tracking and energy consumption. Compared to an optimal controller without preview, the predictive controller reduces energy consumption by 55%. Additionally, a relationship between cooling system energy consumption and battery size is established. The impact of the preview horizon on energy consumption is examined, and a tradeoff between computational cost and optimality is identified.

Electrification of off-road vehicle powertrains can increase mobility, improve energy efficiency, and enable new utility by providing high amounts of electrical power for auxiliary devices. These vehicles often operate in extreme temperature conditions at low ground speeds and high power levels while also having significant cooling airpath restrictions. The restrictions are a consequence of having grilles and/or louvers in the airpath to prevent damage from the operating environment. Moreover, the maximum operating temperatures for high voltage electrical components, like batteries, motors, and power-electronics, can be significantly lower than those of the internal combustion engine. Rejecting heat at a lower temperature gradient requires higher flow rates of air for effective heat exchange to the operating environment at extreme temperature conditions.

Substantial fuel economy improvements for light-duty automotive engines demand novel combustion strategies. Low temperature combustion (LTC) demonstrates potential for significant fuel efficiency improvement; however, control complexity is an impediment for real-world transient operation. Spark-assisted compression ignition (SACI) is an LTC strategy that applies a deflagration flame to generate sufficient energy to trigger autoignition in the remaining charge. Operating a practical engine with SACI combustion is a key modeling and control challenge. Current models are not sufficient for control-oriented work such as calibration optimization, transient control strategy development, and real-time control. This work describes the process and results of developing a fast-running control-oriented model for the autoignition phase of SACI combustion. A data-driven model is selected, specifically artificial neural networks (ANNs).

This research proposes to leverage the fast response time of Permanent Magnet Synchronous Motors (PMSMs) to compensate for crank angle resolved engine torque variations caused by cycle-by-cycle combustion variations. This method reduces powertrain vibration and enables engine calibrations with high combustion variation that produces low fuel consumption. This research integrates a Field Oriented Control (FOC) strategy with an Explicit Model Predictive Control (EMPC) to trace previewed current references. The previewed current references are computed from the engine torque difference between predicted nominal operation and the measured torque output. This research reveals that the MPC can track a d-q current reference without overshoot, rendering current magnitude constraints unnecessary in the MPC formulation. A control rate penalty is used to tune the aggressiveness of transient voltage demand and meet with the DC voltage limit.

Spark-assisted compression ignition (SACI) leverages flame propagation to trigger autoignition in a controlled manner. The autoignition event is highly sensitive to several parameters, and thus, achieving SACI in production demands a high tolerance to variations in conditions. Limited research is available to quantify the combustion response of SACI to these variations. A simulation study is performed to establish trends, limits, and control implications for SACI combustion over a wide range of conditions. The operating space was evaluated with a detailed chemical kinetics model. Key findings were synthesized from these results and applied to a 1-D engine model. This model identified performance characteristics and potential actuator positions for a production-viable SACI engine. This study shows charge preparation is critical and can extend the low-load limit by strengthening flame propagation and the high-load limit by reducing ringing intensity.

Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to meet fuel economy, emissions, and performance targets. The response time variations between engine control actuators tend to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) has the potential to significantly reduce control calibration effort as compared to the current methodologies that are based on decentralized feedback control strategies. MPC strategies simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control, the engine model and optimization processes must be computationally efficient without sacrificing effectiveness. Most MPC systems intended for real-time control utilize a linearized model that can be quickly evaluated using a sub-optimal optimization methodology.

Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action.

Knock control systems are fundamentally stochastic, regulating some aspect of the distribution from which observed knock intensities are drawn. Typically a simple threshold is applied, and the controller regulates the resultant knock event rate. Recent work suggests that the choice of threshold can have a significant impact on closed loop performance, but to date such studies have been performed only in simulation. Rigorous assessment of closed loop performance is also a challenging topic in its own right because response trajectories depend on the random arrival of knock events. The results therefore vary from one experiment to the next, even under identical operating conditions. To address this issue, stochastic simulation methods have been developed which aim to predict the expected statistics of the closed loop response, but again these have not been validated experimentally.

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model.

Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

This paper presents the transient power optimization of an organic Rankine cycle waste heat recovery (ORC-WHR) system operating on a heavy-duty diesel (HDD). The optimization process is carried on an experimentally validated, physics-based, high fidelity ORC-WHR model, which consists of parallel tail pipe and EGR evaporators, a high pressure working fluid pump, a turbine expander, etc. Three different ORC-WHR mixed vapor temperature (MVT) operational strategies are evaluated to optimize the ORC system net power: (i) constant MVT; (ii) constant superheat temperature; (iii) fuzzy logic superheat temperature based on waste power level. Transient engine conditions are considered in the optimization. Optimization results reveal that adaptation of the vapor temperature setpoint based on evaporation pressure strategy (ii) provides 1.1% mean net power (MNP) improvement relative to a fixed setpoint strategy (i).

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

This paper presents the development of an electrochemical aging model of LiFePO4-Graphite battery based on single particle (SP) model. Solid electrolyte interphase (SEI) growth is considered as the aging mechanism. It is intended to provide both sufficient fidelity and computational efficiency required for integration within the HEV power management optimization framework. The model enables assessment of the battery aging rate by considering instantaneous lithium ion surface concentration rather than average concentration, thus enhancing the fidelity of predictions. In addition, an approximate analytical method is applied to speed up the calculation while preserving required accuracy. Next, this aging model are illustrated two applications. First is hybrid electric powertrain system model integration and simulation.

Knock-limited engine operation is one of the most important constraints on fuel efficiency and performance that must be considered during the design, control algorithm development and calibration of spark-ignition engines. This research evaluates the accuracy of model-based knock prediction routines and their applicability for control-oriented applications over various engine operating conditions using commercial fuels. Two common methods of knock prediction, a generalized chemical kinetics model and an empirical induction-time correlation, are evaluated and compared against experimental data. The experimental investigation is conducted using a naturally aspirated 3.6L V6 engine, retrofitted with cooled Exhaust Gas Recirculation (EGR). Data are acquired from spark timing sweeps under knocking conditions at different engine speeds and loads in an engine dynamometer cell.

As engines are equipped with an increased number of control actuators to meet fuel economy targets they become more difficult to control and calibrate. The large number of control actuators encourages the investigation of physics-based control strategies to reduce calibration time and complexity. Of particular interest is spark timing control and calibration since it has a significant influence on engine efficiency, emissions, vibration and durability. Spark timing determination to achieve a desired combustion phasing is currently an empirical process that occurs during the calibration phase of engine development. This process utilizes a large number of stored surfaces and corrections to account for the wide range of operating environments and conditions that a given engine will experience. An obstacle to realizing feedforward physics-based combustion phasing control is the requirement for an accurate and fast combustion model.

Accurate in-cylinder air charge estimation is important for engine torque determination, controlling air-to-fuel ratio, and ensuring high after-treatment efficiency. Spark ignition (SI) engine technologies like variable valve timing (VVT) and exhaust gas recirculation (EGR) are applied to improve fuel economy and reduce pollutant emissions, but they increase the complexity of air charge estimation. Increased air-path complexity drives the need for cost effective solutions that produce high air mass prediction accuracy while minimizing sensor cost, computational effort, and calibration time. A large number of air charge estimation techniques have been developed using a range of sensors sets combined with empirical and/or physics-based models. This paper provides a technical review of research in this area, focused on SI engines.

Low pressure (LP) and cooled EGR systems are capable of increasing fuel efficiency of turbocharged gasoline engines, however they introduce control challenges. Accurate exhaust pressure modeling is of particular importance for real-time feedforward control of these EGR systems since they operate under low pressure differentials. To provide a solution that does not depend on physical sensors in the exhaust and also does not require extensive calibration, a coupled temperature and pressure physics-based model is proposed. The exhaust pipe is split into two different lumped sections based on flow conditions in order to calculate turbine-outlet pressure, which is the driving force for LP-EGR. The temperature model uses the turbine-outlet temperature as an input, which is known through existing engine control models, to determine heat transfer losses through the exhaust.

This paper presents an Organic Rankine Cycle (ORC) system model for heavy-duty diesel (HDD) applications. The dynamic, physics-based model includes: heat exchangers for parallel exhaust and EGR circuits, compressible vapor working fluid, distribution and flow control valves, a high pressure pump, and a reservoir. A finite volume method is used to model the evaporator, and a pressure drop model is included to improve the accuracy of predictions. Experimental results obtained on a prototype ORC system are used for model calibration and validation. Comparison of predicted and measured values under steady-state conditions is pursued first, followed by the analysis of selected transient events. Validation reveals the model’s ability to track real-world temperature and pressure dynamics of the ORC system. Therefore, this modeling framework is suitable for future system design studies, optimization of ORC power generation, and as a basis for development of control-oriented ORC models.

This paper develops a methodology to optimize the supervisory controller for a heavy-duty series hybrid electric vehicle, with consideration of battery aging and cooling loss. Electrochemistrybased battery aging model is integrated into vehicle model. The side reaction, reductive electrolyte decomposition, is modeled to determine battery aging rate, and the thermal effect on this reaction rate is considered by Arrhenius Law. The resulting capacity and power fading is included in the system-level study. Sensitivity analysis shows that battery aging could cause fuel economy loss by 5.9%, and increasing temperature could improve fuel economy at any given state-of-health, while accelerating battery aging. Stochastic dynamic programming algorithm is applied to a modeled system to handle the tradeoff between two objectives: maximizing fuel economy and minimizing battery aging.