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Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

A growing number of electric vehicles (EV) and hybrid electric vehicles (HEV) in the present market depicts the rapid growing demand for energy storage systems. The battery’s main peculiarities must be the power density and reliability over time. The temperature strongly affects battery performance for low and high intensity. In particular, the management of the heat generated by the battery itself is one of the main aspects to handle to preserve the performance over time. The objective of this paper is to compare the surface temperature of the lithium-ion polymer battery at different discharging rates by infrared thermography. Thermal imaging is performed to detect the battery surface temperature distribution, focusing on its variation over time and the local inhomogeneity. Temperature measurements are then used to estimate the contributions of the different heat transfer mechanisms for the dissipation of the heat generated by the battery.

New internal combustion engines (ICE) are characterized by increasing maximum efficiency, thanks to the adoption of strategies like Atkinson cycle, downsizing, cylinder deactivation, waste heat recovery and so on. However, the best performance is confined to a limited portion of the engine map. Moreover, electric driving in urban areas is an increasingly pressing request, but battery electric vehicles use cannot be easily spread, due to limited vehicle autonomy and recharging issues. Therefore, hybrid propulsion systems are under development, in order to reduce vehicle fuel consumption, by decoupling the ICE running from road load, as well as to permit energy recovery and electric driving. This paper analyses a new-patented solution for power split hybrid propulsion system with gearbox. The system comprises an auxiliary power unit, adapted to store and/or release energy, and a planetary gear set, which is interposed between the ICE and the gearbox.

Thanks to the actuation flexibility of their systems, electric vehicles with individual powertrains, including in-wheel and on-board motors, are a very popular research topic amongst various types of electrified powertrain architectures. The introduction of the individual electric powertrain provides great capacity for improvement of the vehicle’s energy efficiency and control performance. However, it also poses tremendous challenges concerning vehicle safety, due to the complex system dynamics and cooperation mechanisms between multiactuators. For an electric vehicle with independently controlled motors, because of design and manufacturing factors, the steady-state error of each motor output torque, and the flexibilities and nonlinear backlash of left and right drivetrains, can be different. This results in asymmetrical output characteristics of electric powertrain systems on the same axle.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.

Increasing regulatory demand to reduce CO2 emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NOx emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.