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Electric vehicles (EV) present distinctive challenges compared to ICE (Internal Combustion Engine) powered counterparts. Cabin heating and air-conditioning stand out among them, especially cabin heating in cold weather, owing to its outsized effect on drivable range of the vehicle. Efficient management of the cabin thermal system has the potential to improve vehicle range without compromising passenger comfort. A method to improve cabin thermal system regulation by effectively leveraging the solar load on the vehicle is proposed in this work. The methodology utilizes connectivity and mapping data to predict the solar load over a future time horizon. Typically, the solar load is treated as an unmeasured external disturbance which is compensated with control. It can however be treated as an estimated quantity with potential to enable predictive control. The solar load prediction, coupled with a passenger thermal comfort model, enables preemptive thermal system control over a route.

DC fast charging (DCFC) also referred to as L3 charging, is the fastest charging technology to replenish the drivable range of an electric vehicle. DCFC provides the convenience of faster charging time compared to L1 and L2 at the expense of potentially increased battery health degradation. It is known to accelerate battery capacity fade leading to reduced range and lifetime of the EV battery. While there are active efforts and several means to reduce the downsides of DCFC at cell chemistry level, this trade-off is still an important consideration for most battery cells in automotive propulsion applications. Since DCFC is a customer driven technology, informing drivers of the trade-off of each DCFC event can potentially result in better outcomes for the EV battery life. Traditionally, the driver is advised to limit DCFC events without providing quantifiable metrics to inform their decisions during EV charging.

Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. It is especially important for systems with multiple energy storage units where optimally arbitrating power demand among the energy storage units is challenging. While many optimal control methods exist for designing a good energy management system, in this work a Reinforcement-Learning (RL) methodology is explored to design an energy management system for an electric vehicle with a Hybrid Energy Storage System (HESS) that included a battery and a supercapacitor. The energy management system is designed to optimally divide the traction power request among a battery and a super-capacitor in real-time; while trying to minimize the overall energy consumption and battery degradation.

The need to reduce fuel consumption and harmful pollutants from engines is an important task for automotive industry. It has led to technological advances in new engine design, such as engine downsizing. Due to the reduction of displacement, engine power output is reduced and thus its overall performance is limited. In order to increase torque and power, engines are typically boosted by turbochargers or superchargers. Meanwhile, the improvement on turbo design makes it possible to operate VGT (variable geometry turbocharger) at harsher exhaust environment for gasoline engines as well (e.g., with much higher exhaust temperature than that of diesel engines). This makes VGT related control problems more challenging and requires attention to protecting corresponding engine hardware during an entire engine life.

This paper presents a combustion stability index derived from an in-cylinder ionization signal to control the engine maximum EGR limit. Different from the existing approaches that use the ionization signal values to gauge how much EGR was added during the combustion, the proposed method concentrates on using the ionization signal duration and its stochastic properties to evaluate the end result of EGR on combustion stability. When the duration index or indexes are higher than pre-determined values, the EGR limit is set. The dynamometer engine test results have shown promise for closed loop EGR control of spark ignition engines.

Spark timing of an Internal Combustion (IC) engine is often limited by engine knock in the advanced direction. The ability to operate the engine at its advanced (borderline knock) spark limit is the key for improving output power and fuel economy. Due to combustion cycle-to-cycle variations, IC engine combustion behaves similar to a random process and so does the engine performance criteria, such as IMEP (Indicated Mean Effective Pressure), and knock intensity. The combustion stability measure COVariance of IMEP assumes the IMEP is a random process. Presently, the spark limit control of IC engines is deterministic in nature. The controller does not utilize any stochastic information associated with control parameters such as knock intensity for borderline spark limit control. This paper proposes a stochastic limit control strategy for borderline knock control. It also develops a simple stochastic model for evaluating the proposed stochastic controller.

Internal combustion engines are designed to maximize power subject to meeting exhaust emission requirements and minimizing fuel consumption. However, the usable range of ignition timing is often limited by knock in the advance direction and by combustion instability (partial burn and misfire) in the retard direction. This paper details a retard limit management system utilizing ionization signals in order to maintain the desired combustion quality and prevent the occurrence of misfire without using fixed limits. In-cylinder ionization signals are processed to derive a metric for combustion quality and closeness of combustion to partial burn/misfire limit, which is used to provide a limiting value for the baseline ignition timing in the retard direction. For normal operations, this assures that the combustion variability is kept within an acceptable range.