Keywords: Gas exchange processes, turbocharging, supercharging, variable valve technology
Abstract: This paper illustrates a multiple-input multiple-output (MIMO) controller design framework and a controller switching algorithm for MIMO controllers to achieve robust coordinated control of turbocharged lean-burn engines. The control problem tracks engine speed, differential pressure across the throttle valve and the air-to-fuel ratio simultaneously to achieve satisfactory engine performance while avoiding compressor surge. The controller design approach is applied to a high-fidelity GT-Power engine model for a lean-burn natural gas engine to assess the closed-loop controller performance. The engine performance with the robust MIMO controller is compared with that using a benchmark production controller to evaluate the additional benefits of the MIMO controller. In a large step increase in desired engine speed and corresponding engine torque, it is observed that the MIMO controller leads to a slightly faster engine speed response. Furthermore, during transience, the minimum air-to-fuel ratio is 20% higher and the peak in differential pressure across the throttle is reduced by 59% when using the MIMO controller.

Keywords: Combustion modeling and control: spark ignition, compression ignition, low temperature combustion, Dual fuel control, bio-fuels or bio-gas alternatives
Abstract: Abstract For advanced pre-mixed combustion concepts, Cylinder Pressure-Based Control is a key concept for robust operation. It also opens the possibility for on-line heat release shaping. For cost and time efficient development of these controllers, fast control-oriented combustion models that predict average in-cylinder pressure traces have been proposed. However, they are not able to capture cyclic variations. In this study, a data-based modelling procedure is proposed to predict the in-cylinder pressure trace and cyclic variation during the combustion cycle. The inputs to the model are the in-cylinder conditions at intake valve closing and the fuelling settings. The proposed model is based on experimental data, Principal Component Analysis and Gaussian Process Regression. This new data-driven approach is applied to model the combustion behaviour of a Reactivity Controlled Compression Ignition engine running on Diesel and E85. The resulting model has a root-square-mean-error of average behaviour and cyclic variance of 0.8◦ and 0.2◦^2 in CA50, 0.1 bar and 0.03 bar^2 in Gross Indicated Mean Effective Pressure, and 0.1 % and 0.001 %^2 in the Gross Indicated Efficiency, respectively.

Keywords: Powertrain modeling and control, Combustion modeling and control: spark ignition, compression ignition, low temperature combustion, AI/ML application to automotive and transportation systems
Abstract: The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant emissions is needed. One solution is the use of Machine Learning (ML) and Model Predictive Control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller. ML is developed in this paper for both modeling engine performance and emissions and for imitating the behaviour of a Linear Parameter Varying (LPV) MPC. Using a support vector machine-based linear parameter varying model of the engine performance and emissions, a model predictive controller is implemented for a 4.5~L Cummins diesel engine. This online optimized MPC solution offers advantages in minimizing the NOx emissions and fuel consumption compared to the baseline feedforward production controller. To reduce the computational cost of this MPC, a deep learning scheme is designed to mimic the behavior of the developed controller. The performance in reducing NOx emissions at a constant load by the imitative controller is similar to that of the online optimized MPC, however, the imitative controller requires 50 times less computation time when compared to that of the online MPC optimization.

Keywords: Powertrain modeling and control, Gas exchange processes, turbocharging, supercharging, variable valve technology
Abstract: This paper presents a purge estimation method for a robust air fuel ratio control of internal combustion engines. The air fuel ratio control is a primary emission control mechanism of gasoline engines, and purge is one of the most critical disturbances that can lead to significant air fuel ratio excursions. In this work, Luenberger-like unknown input observers are proposed to estimate purge flow rate and purge fuel fraction using existing sensors available in production engine management systems. The convergence properties of the proposed observers are investigated analytically and numerically. The estimation method allows to improve accuracy of the air fuel ratio control by compensating for the purged fuel, thereby it allows aggressive purge in a wide range of operational conditions to meet stringent evaporative emission standards.

Keywords: Vehicle dynamics, control and state estimation, Transmissions, brakes, steering, suspension systems, Powertrain modeling and control
Abstract: During engine slipping start (ESS) of parallel hybrid electric vehicles (HEV) with a dual-clutch transmission, the motor needs to propel the vehicle and crank the engine via the slipping clutch simultaneously. However, a lack of capability to respond to changes in driver demand can disturb normal driving habits. Meanwhile, parametric uncertainty of the clutch friction coefficient makes it hard to compensate for the clutch slipping torque. To guarantee response speed to changes in driver demand torque, a two-layer model reference adaptive controller (MRAC) is proposed for ESS utilizing state varialbes as the feedback. Under this framework, appropriate reference profiles can be generated according to the real-time driver demand torque rather than assuming that the accelerator pedal is unchanged. An auxiliary reference model is introduced in addition to the major reference model to modify the convergence rate of the tracking error without changing the well-designed reference profiles, and adaptive control laws are accordingly developed to enhance the transient performance against matched parameter uncertainty of clutch and unmatched measurement noise.

Keywords: Powertrain modeling and control, Gas exchange processes, turbocharging, supercharging, variable valve technology
Abstract: This paper describes a comprehensive framework for the development of a model-based robust coordinated control system, which is used to regulate the gas exchange processes of an advanced turbocharged natural gas engine with multifaceted control objectives. The natural gas engine involved in this study features a multi-input, multi-output structure and is highly-nonlinear. A robust coordinated control system is synthesized to realize desired performances of the engine over its entire operating region and is compared to a benchmark production control system in simulation. The comparison results explore the merits of coordinated control over decoupled control in the aspect of complex engine dynamics.

Keywords: Model-based diagnostics, Testing and validation, Vehicle dynamics, control and state estimation
Abstract: Connected vehicle paradigm allows the systematic recording of data, which may be made available for both on-board and cloud diagnostics functions. However, real-driving conditions may be highly dynamic, making the application of diagnostic methods cumbersome. This article analyzes the variability of real-world data coming from a mild hybrid vehicle at various levels (i.e., vehicle, powertrain and engine cycle). The results show that although non-steady, real-driving conditions can exhibit situations that could be leveraged to characterize the nominal operation of the vehicle over time and therefore ease the detection of faulty operation.

Keywords: Vehicle dynamics, control and state estimation, Single and multi-vehicle planning and coordination, Intelligent transportation systems
Abstract: A typical urban signalized intersection poses significant modeling and control challenges in a mixed traffic environment consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs). In this paper, we address the problem of deriving safe trajectories for CAVs in a mixed traffic environment that prioritizes rear-end collision avoidance when the preceding HDVs approach the yellow and red signal phases of the intersection. We present a predictive control framework that employs a recursive least squares algorithm to approximate in real time the driving behavior of the preceding HDVs and then uses this approximation to derive safety-aware trajectory in a finite horizon. We validate the effectiveness of our proposed framework through numerical simulation and analyze the robustness of the control framework.

Keywords: Testing and validation, Vehicle dynamics, control and state estimation
Abstract: The evaluation of the energy savings potential of Connected and Automated Vehicles (CAVs) technologies necessitates a representative baseline that accounts for the inherent variability due to route, terrain, traffic, traffic lights, etc., in real-world driving conditions. While considerable work has been done in the field of optimal energy management, eco-driving and eco-routing of CAVs, few contributions have addressed the creation of a representative baseline to realistically evaluate the energy savings potential of these technologies. This work proposes a route generation methodology based on leveraging a high-dimension driving dataset to construct diverse subset of synthetic driving trips and synthetic routes for large scale evaluation of energy consumption of CAVs. The generated synthetic routes can then be used to extract real-world routes from open-source mapping platforms, which have similar characteristics as the generated synthetic routes.

Keywords: Control, guidance and navigation of autonomous vehicles, Intelligent transportation systems
Abstract: This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solutions are presented for various conflicts that occur at an intersection. Cooperation is introduced amongst CAVs as the ability to share intentions. Two levels of cooperation, namely the Cooperative ED (C-ED) and Non-Cooperative (NC-ED) algorithms are evaluated, in a simulation environment, for energy efficiency with Intelligent Driver Model (IDM) as the baseline.

Keywords: Energy management for XEV, Vehicle dynamics, control and state estimation, Optimal design and control of XEV
Abstract: Electric vehicles (EVs) have demonstrated significant advantages of high fuel economy and low maintenance cost over gasoline-powered vehicles and hybrid electric vehicles in moving people and goods. However, range anxiety remains as one of the main barriers in market penetration for EVs. Platooning has proven to be an effective approach to reduce aerodynamic drag resistance and thus extend EV ranges. However, taking full advantage of platooning to reduce energy consumption during a trip while satisfying the time constraint is a challenge. This paper is focused on the design and validation of the high-level speed planner of a two-level real-time platooning framework for EVs. The speed optimization problem in the high-level speed planner for the entire trip is reformulated into two speed profile optimization problems in two processes: 1) catch-up and then platooning, and 2) platooning and then break-away. Analytical solutions are derived for the optimal speed profiles in both processes. The analytical solutions capture the impacts of critical parameters such as initial and final inter-vehicle distances, and the leading vehicle speed.

Keywords: V2X communications, Intelligent transportation systems, Control, guidance and navigation of autonomous vehicles
Abstract: Most studies evaluating the energy efficiency of connected and automated vehicles (CAVs) in car-following scenarios have considered a few preceding vehicles communicating with the controlled CAVs. However, considering rapidly evolving technologies in CAVs, extended vehicle-to-vehicle (V2V) connectivity over large-scale traffic needs to be considered in estimating CAVs' energy benefits. This paper investigates the potential energy saving of V2V-connected vehicles in large-scale downstream traffic by adopting a human driver model generating stable car-following trajectories for many consecutive vehicles. The energy-efficient driving of a CAV is demonstrated based on an optimal controller minimizing the longitudinal acceleration by forecasting an immediately preceding vehicle's trajectory over a fixed prediction horizon. Various traffic scenarios are considered by applying different simulation parameters, including the distribution of vehicle time gaps, the number of connected vehicles, and prediction horizon lengths. Furthermore, a comprehensive analysis is conducted to discover the relationships between the parameters of interest and system performance, including prediction and control. Our findings from the parameter study are validated by evaluating the realistic energy consumption of a vehicle in a simulation platform operating high-fidelity powertrain models.

Keywords: Model-based diagnostics, AI/ML application to automotive and transportation systems, Combustion modeling and control: spark ignition, compression ignition, low temperature combustion
Abstract: Data-driven fault diagnosis of dynamic systems is complicated by incomplete training data, unknown faults, and overlapping classes. Many existing machine learning models and data-driven classifiers are not expected to perform well if training data is not representative of all relevant fault realizations. In this work, a data-driven model, called a flexi-pipe model, is proposed to capture the variability of data in residual space from a few realizations of each fault class. A diagnosis system is developed as an open set classification algorithm that can handle both incomplete training data and overlapping fault classes. Data from different fault scenarios in an engine test bench is used to evaluate the performance of the proposed methods. Results show that the proposed fault class models generalize to new fault realizations when training data only contains a few realizations of each fault class.

Keywords: Combustion modeling and control: spark ignition, compression ignition, low temperature combustion, Dual fuel control, bio-fuels or bio-gas alternatives, Powertrain modeling and control
Abstract: Internal combustion engines may use ignition assisting heating elements such as glow plugs to facilitate combustion control in automotive or aircraft powertrains that operate with synthetic fuels of varying ignition behavior or at extreme inlet conditions. This work presents a model predictive controller (MPC) that regulates combustion phasing in compression ignition engines on a cycle-to-cycle basis by coordinating fuel start of injection (SOI) with power supplied to a glow plug acting as an ignition assist (IA) device, while enforcing IA actuator range and rate constraints. Simulations were conducted using a nonlinear virtual engine informed by data from a commercial engine operating at a condition that induced high combustion variability. A rate-based MPC formulation leveraging state estimate feedback and integral setpoint tracking was developed. Simulation results show the MPC scheme ensures steady-state tracking of combustion phasing within 70 engine cycles, conserves IA usage whenever possible to reduce thermo-mechanical stress on the actuator, and maintains closed-loop combustion variability at only 4% higher than the open-loop system variability. Furthermore, the controller maintains reference tracking even if combustion sensitivity to the actuators deviates by more than 20% from the controller's internal model, without the need for retuning control parameters.

Keywords: Model-based diagnostics, Exhaust gas after-treatment: catalyst and DPF models, thermal management, SCR control, regeneration control , Health monitoring of ADAS systems, powertrain and its components
Abstract: Fault diagnosis is important for automotive systems, e.g., to reduce emissions and improve system reliability. Developing diagnosis systems is complicated by model inaccuracies and limited training data from relevant operating conditions, especially for new products and models. One solution is the use of hybrid fault diagnosis techniques combining model-based and data-driven methods. In this work, data-driven residual generation for fault detection and isolation is investigated for a system injecting urea into the aftertreatment system of a heavy-duty truck. A set of recurrent neural network-based residual generators is designed using a structural model of the system. The performance of this approach is compared to a baseline model-based approach using data collected from a heavy-duty truck during different fault scenarions with promising results.

Keywords: Modeling and control for electric and electro-magnetic components
Abstract: The paper presents the development of LQG (Linear Quadratic Gaussian) Control to minimize the jerk , during the actuation of a novel engine crank mechanism ,for a hybrid electric vehicle. The operation of the novel mechanism is explained in detailed. Open loop simulation results show an undesirable jerk , which is perceptible to the driver, during the actuation of this mechanism. A closed loop LQG controller is then formulated to minimize the jerk and provide a smooth engine start. The formulation of the plant model, observer design is explained. Incremental control is used to approximate jerk from the output equation. Finally, closed loop simulation results show a smooth engine crank while minimizing the undesirable jerk.

Keywords: Advanced Driver Assist Systems, AI/ML application to automotive and transportation systems, Intelligent transportation systems
Abstract: Eco-driving strategies have been shown to provide significant reductions in fuel consumption. This paper outlines an active driver assistance approach that uses a residual policy learning (RPL) agent trained to provide residual actions to default power train controllers while balancing fuel consumption against other driver-accommodation objectives. Using previous experiences, our RPL agent learns improved traction torque and gear shifting residual policies to adapt the operation of the powertrain to variations and uncertainties in the environment. For comparison, we consider a traditional reinforcement learning (RL) agent trained from scratch. Both agents employ the off-policy Maximum A Posteriori Policy Optimization algorithm with an actor-critic architecture. By implementing on a simulated commercial vehicle in various car-following scenarios, we find that the RPL agent quickly learns significantly improved policies compared to a baseline source policy but in some measures not as good as those eventually possible with the RL agent trained from scratch.

Keywords: Control, guidance and navigation of autonomous vehicles, ML/AI for vehicle autonomy, Vehicle dynamics, control and state estimation
Abstract: Eco-driving is a highly nonlinear control problem. The nonlinearities include the complex energy conversion/dissipation in the powertrain, environmental influences such as road grade and aerodynamic drag, constraints due to traffic signs, safety issues, and physical limits of the vehicle system. In recent years, researchers have increasingly revisited the Koopman operator to linearize nonlinear dynamics. This paper adopts such an approximation technique to construct the lifted state space in a data-driven procedure that allows us to incorporate nonlinearities and system perturbations in the cost function. In addition, the nonlinear constraints in states can also be handled linearly. The resultant formulation of a linearly constrained quadratic program can be readily applied to design a model predictive control that enjoys a low computation load as with a linear dynamic system. Simulation results demonstrate additional energy saving potential compared to a linear approach.

Keywords: Vehicle dynamics, control and state estimation, Optimal design and control of XEV
Abstract: Battery electric vehicles (BEVs) are more commonly deployed in short-distance and in-city operations than in long-distance on-highway operations mainly due to range anxiety. One of the solutions to alleviate the range anxiety is platooning. Vehicle platooning with short distances has demonstrated significant improvements in vehicle efficiency by reducing the aerodynamic drag force. To maximize the energy saving benefit brought by vehicle platooning, it is critical to identify the inter-vehicle space that results in the most reduction of aerodynamic drag coefficient in real time and appropriately maintain the inter-vehicle distance, which is a rather challenging task due to the unknown correlation between the inter-vehicle space and aerodynamic drag coefficient. This paper proposes a unified extremum seeking control (ESC)-based control framework to find and maintain the inter-vehicle distance that corresponds to the minimum air drag coefficient in presence of the environment uncertainty. The controller is implemented on a BEV model with a one-pedal driving (OPD) feature and validated in simulation. Simulation results demonstrated that the proposed ESC-based control framework can effectively identify the inter-vehicle distance with respect to the minimum aerodynamic drag coefficient in real time and regulate the inter-vehicle distance at the desired value without steady-state oscillations. The proposed framework can potentially be applied to both passenger BEVs and commercial BEVs to improve vehicle efficiency.

Keywords: ML/AI for vehicle autonomy, AI/ML and model based approaches for safety and security in automotive systems, AI/ML application to automotive and transportation systems
Abstract: Understanding the causal relationship between an autonomous vehicle's input state and its output action is important for safety mitigation and explainable automated driving. However, reinforcement learning approaches have the drawback of being black box models. This work proposes an interpretable state representation that can capture state-action causalities for an automated driving agent, while also allowing for the underlying formulation to be general enough to be adapted to different driving scenarios. It also proposes encoding temporally-extended information in the state representation for better driving performance. We test this approach on a reinforcement learning agent in a highway simulation environment and demonstrate that the proposed state representation can capture state-action causalities in an interpretable manner. Experimental results show that the formulation and interpretation can be used to adapt the behavior of the driving agent to achieve desired, even unseen, driving behaviors after training.

Keywords: Vehicle cybersecurity for safety and privacy, Control, guidance and navigation of autonomous vehicles, Intelligent transportation systems
Abstract: Ubiquity of connected devices ranging from cellphones to in-vehicle navigation systems has enabled information rich routing and navigation services. Eco-routing utilizes these infrastructure and data about the routes to ascertain and inform the driver the energy cost of traversing a route to their destination. An accurate energy consumption model of the vehicle traversing the route is essential to perform eco-routing effectively. Unfortunately, very accurate energy consumption models also contain operating strategies that their owners are disinclined to disclose publicly. We propose the use of partially homomorphic cryptosystem for private function evaluations to enable secure eco-routing. A novel way to encrypt the energy consumption model to enable secure eco-routing, methods for private evaluation of the encrypted energy consumption model, and the associated protocol are described. Practical considerations for implementing such a system are explored through software implementation.

Keywords: Vehicle dynamics, control and state estimation, Testing and validation, Control, guidance and navigation of autonomous vehicles
Abstract: Expert human drivers can execute emergency steering actions to avoid sudden events like a deer crossing the road. However, justifying beyond-the-limit emergency maneuvering for automated driving systems is exceptionally challenging. Emergency maneuvering often requires non-linear control policies without stability guarantees. Liability concerns, ethics, lack of safety guarantees, and non-linear system dynamics convolute an already complicated problem. Against this backdrop, we propose a principled approach to justify a particular type of emergency steering in safety-critical situations. A limit-handling controller is justified and deployed to execute the emergency maneuver upon a conventional controller's formally verified incapability to handle. We claim this check justifies the execution of the emergency maneuver as we show failure is mathematically inevitable otherwise. The simulation-based experimental validation shows that using backward reachability analysis, the proposed approach can determine emergencies. The validation justifies using limit-handling controllers for collision avoidance in a scenario where the baseline controllers fail catastrophically.

Keywords: AI/ML and model based approaches for safety and security in automotive systems, ML/AI for vehicle autonomy
Abstract: Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy efficiency and traffic flow. However, the application of RL in safety critical systems such as ACC requires strong safety guarantees which are difficult to achieve with learning agents that have a fundamental need to explore. In this paper, we derive control barrier functions as safety filters that allow an RL-based ACC controller to explore freely within a collision safe set. Specifically, we derive control barrier functions for high relative degree nonlinear systems to take into account inertia effects relevant for commercial vehicles. We also outline an algorithm for accommodating actuation saturation with these barrier functions. While any RL algorithm can be used as the performance ACC controller together with these filters, we implement the Maximum A Posteriori Policy Optimization (MPO) algorithm with a hybrid action space that learns fuel optimal gear selection and torque control policies. The safety filtering RL approach is contrasted with a reward shaping RL approach that only learns to avoid collisions after sufficient training. Evaluations on different drive cycles demonstrate significant improvements in fuel economy with the proposed approach compared to baseline ACC algorithms.

Keywords: Combustion modeling and control: spark ignition, compression ignition, low temperature combustion
Abstract: Knock behaves a cyclically random process but also excites deterministic knock resonant behavior within any given cycle. While individual instances of the resonant response are readily acquired, the stochastic / cyclic variations of such signals (which also reflect the underlying knock process) are harder to quantify. In this work, a more complete model of this process is developed, capturing both the cyclic variability in the knock response as well as its functional resonant behavior. A recently developed alignment process is first used to evaluate the characteristic ensemble mean knock ‘signature’ of the data. A functional linearization about this ensemble mean is then used to decompose and model cyclic variations in terms of small variations in amplitude, frequency and phasing of the signal. The model is fitted to the data, encapsulating the stochastic variation of the knock signal within the stochastic variation of the estimated parameters. A residual analysis used to assess the goodness of fit as a function of crank angle, and the distribution and covariance of the estimated parameters is discussed.

Keywords: Control, guidance and navigation of autonomous vehicles, Intelligent transportation systems, Energy management for XEV
Abstract: Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents an Eco-driving trajectory planning approach for a platoon of heterogeneous electric vehicles (EVs) in urban environments. The proposed control strategy for the platoon considers energy consumption, mobility and passenger comfort, with which vehicles may pass signalized intersections with no stops. For a given urban route, first, the platoon's leader vehicle employs dynamic programming (DP) to plan a trajectory for the anticipated path with the aim of balancing energy consumption, mobility and passenger comfort. Then, other following CAVs in the platoon either follow the preceding vehicles, using a PID-based cooperative adaptive cruise control, or plan their own trajectory by checking whether they can pass the next intersection without stopping. Furthermore, a heavy vehicle that cannot efficiently follow a light-weight vehicle would instead employ the DP-based trajectory planner. The results of simulation studies demonstrate the efficacy of the proposed control strategy with which the platoon's energy consumption is shown to reduce while the mobility is not compromised.

Keywords: Energy management for XEV
Abstract: A proper Energy Management Strategy (EMS) is a cornerstone for Hybrid Electric Vehicles consumption minimization. In general, to find the global optimal strategy, the knowledge of the entire mission profile of the vehicle is needed, making the real-time implementation impossible. A well-known solution to this problem is the Equivalent Consumption Minimization Strategy (ECMS) that optimizes at each time instant an equivalent fuel consumption, which combines the real fuel rate and a virtual fuel associated to the use of the battery energy. This virtual fuel is the actual battery power weighted by an equivalence factor, that implicitly accounts for the battery recharge efficiency during the vehicle mission. In this work, we propose an efficiency based EMS, rather than a fuel consumption based one. Despite the two approaches are proven to be identical under some assumptions, in the efficiency based solution the definition of the equivalence factor results easier. An offline estimation of this quantity is firstly proposed, eventually extended with a real-time adaptation. Simulation results show the effectiveness of the proposed approach, in particular when the adaptive strategy is used.

Keywords: Energy management for XEV, Optimal design and control of XEV, Vehicle dynamics, control and state estimation
Abstract: An Energy Management System for Hybrid Electric Vehicles is described based on a Model Predictive Control solution. This is implemented using a Processor-In-the-Loop simulation running on a GreenBox II development board from NXP Semiconductors. The hybrid vehicle considered contains an internal combustion engine and an electric motor in a parallel configuration. The MPC design involves a Linear Parameter-Varying model to approximate the nonlinear vehicle model and provide a simpler algorithm for implementation. The control policy was integrated into the GBII control board and assessed in simulation with the processor in the loop. The very promising performance of the proposed predictive controller in terms of mileage and battery degradation is compared with the well-established Equivalent Consumption Minimization Strategies.

Keywords: Powertrain modeling and control
Abstract: This paper presents the real-time optimization of the crankshaft motion in a hybridized opposed piston (OP) engine using an iterative learning-based trajectory optimization scheme. The powertrain is oriented in a series hybrid design with each crankshaft directly coupled to electric motors, eliminating the conventional geartrain linking the two crankshafts along with the associated friction and weight. In this way, the electric motors can directly extract the work generated by the engine and regulate the crankshaft dynamics, introducing the capability to dynamically vary compression ratio, combustion volume, and scavenging dynamics on an inter-cycle basis. This control freedom increases the system’s maximum potential efficiency, yet requires highly optimized intra-cycle crankshaft motion profiles to realize the improved work extraction efficiency of the dual motor-controlled OP engine. Leveraging the repetitive nature of the internal combustion engine, an iterative trajectory optimization (ITO) algorithm is used to define the optimal crankshaft motion profile in real-time for steady state operation. We demonstrate experimentally the rapid convergence and near optimal crankshaft motion profiles for the ITO strategy as well as its proficiency under both motored and fired cycle operation.