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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.

Efficiency testing of hybrid-electric vehicles is challenging, because small run-to-run differences in pedal application can change when the engine fires or the when the friction brakes supplement regenerative braking, dramatically affecting fuel use or energy regeneration. Electronic accelerator control has existed for years, thanks to the popularity of throttle-by-wire (TBW). Electronic braking control is less mature, since most vehicles don’t use brake-by-wire (BBW). Computer braking control on a chassis dynamometer typically uses a mechanical actuator (which may suffer backlash or misalignment) or braking the dynamometer rather than the vehicle (which doesn’t yield regeneration). The growth of electrification and autonomy provides the means to implement electronic brake control. Electrified vehicles use BBW to control the split between friction and regenerative braking. Automated features, e.g. adaptive cruise control, require BBW to actuate the brakes without pedal input.

Lithium-ion batteries (LIBs) have been widely used as the energy storage system in plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) due to their high power and energy density and long cycle life compared to other chemistries. However, LIBs are sensitive to operating conditions, including temperature, current demand and surface pressure of the cell. One very well understood phenomenon of lithium-ion battery is the reduction in charge capacity over time due to cycling and storage commonly known as capacity fade. Considering the need for predicting the behavior of an aged cell and the need for estimating battery useful life for warranty purpose, it is crucial to predict the capacity fade with reasonable accuracy. To accommodate this need, a novel cell level empirical aging model is built based on storage tests and cycle tests. The storage test captures the calendar aging of the lithium-ion cell while the cycle test estimates the cycle aging of the cell.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

A Dodge Omni electric car was prepared for competition in an electric “stock car” 2-hour endurance event: the inaugural Solar and Electric 500 Race, April 7, 1991. This entry utilized a series-wound, direct-current 21-hp electric motor controlled by an SCR frequency and pulse width modulator. Two types of lead-acid batteries were evaluated and the final configuration was a set of 16 (6-volt each) deep-cycle units. Preparation involved weight and friction reduction; suspension modification; load, charge and temperature instrumentaltion; and electrical interlock and collision safety systems. Vehicle testing totalled 15 hours of operation. Ranges observed in testing with the final configuration were from 30 to 52 miles for loads of 175 to 90 amperes. These were nearly constant, continuous discharge cycles. The track qualifying speed (64mph) was near the 68 mph record set by the DEMI Honda at the event on the one-mile track.

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.

A DC-to-AC main Power Inverter Module (PIM) is one of the key components in electrified powertrain systems. Accurate thermal modeling and temperature prediction of a PIM is critical to the design, analysis, and control of a cooling system within an electrified vehicle. PIM heat generation is a function of the electric loading applied to the chips and the limited heat dissipation within what is typically compact packaging of the Insulated Gate Bipolar Transistor (IGBT) module inside the PIM. This work presents a thermal modeling approach for a 3-phase DC/AC PIM that is part of an automotive electrified powertrain system. Heat generation of the IGBT/diode pairs under electric load is modeled by a set of formulae capturing both the static and dynamic losses of the chips in the IGBT module. A thermal model of the IGBT module with a simplified liquid cooling system generates temperature estimates for the PIM.

The automotive industry is polarized between external pressures for ‘zero’ emission battery electric vehicles (BEV) and the ability to manufacture them economically and with minimal environmental impact. Most predictions of future BEV market share suggest that the internal combustion engine (ICE) has an important role to play in personal transportation for the next several decades. That engine will very likely be part of a hybrid architecture. Accepting that the engine will be part of a hybrid powertrain permits new design rules and strategies for the ICE. A major change of the engine could be to reduce BMEP, power density and/or engine speed requirements as performance demand will be supplemented by electric machines. This study focuses on simple changes to the ICE to increase thermal efficiency assuming supplemental electric energy.

Plug-in hybrid electric vehicles (PHEVs) have the ability to significantly reduce petroleum consumption. Argonne National Laboratory (Argonne), working with the FreedomCAR and Fuels Partnership, helped define the battery requirements for PHEVs. Previous studies demonstrated the impact of the vehicle's characteristics, such as its class, mass, or electrical accessories, on the requirements. However, questions on the impact of drive cycles remain outstanding. In this paper, we evaluate the consequences of sizing the electrical machine and the battery to follow standard drive cycles, such as the urban dynamometer driving schedule (UDDS), as well as real-world drive cycles in electric vehicle (EV) mode. The requirements are defined for several driving conditions (e.g., urban, highway) and types of driving behavior (e.g., smooth, aggressive).

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.

The short-term future direction of the automotive transportation sector is uncertain. Many governments and environmental localities around the world are proposing internal combustion engine (ICE) bans and enacting large subsidy programs for zero-tailpipe emissions vehicles powered by batteries or fuel-cells. Such policies can be effective in driving the consumer towards specific powertrains. The reason for such aggressive change is to reduce the sector’s carbon footprint. However, it is not clear if these proposals will reduce greenhouse gas (GHG) emissions. Emissions from raw material extraction, manufacturing, and power generation are shadowed by the focus on reducing the reliance on fossil fuel use. Emissions from non-tailpipe sources should also be considered before pushing for a rapid change to powertrains. Life-cycle analysis (LCA) can assess the GHG emissions produced before, during and after the life of a vehicle in a cradle-to-grave analysis.

Due to increasing standards in fuel consumption, battery electric vehicles (BEV) and plug in electric hybrid vehicles (PHEV), are becoming more commonplace in the automotive industry. Batteries used in such applications require methods of thermal management to promote longer life, higher efficiency and performance. A common method of keeping the battery cool, in high heat conditions, is to use a water to refrigerant chiller. The already existing automotive air conditioning system is leveraged to enable the use of such a chiller. The added thermal transient load of the battery adds complexity to the refrigeration system. Balancing the thermal comfort of the occupants with temperature requirements of battery drives challenges to the overall system capacity. The sudden change in battery cooling loads can noticeably degrade the evaporator heat rejection. In extreme cases the battery cooling load can cause complete loss of refrigerant flow to the evaporator.

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

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.

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).

More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry toward more efficient vehicle thermal management systems to best utilize the heat produced from burning fuel and improve driveline efficiency. The greatest part of the effort is directed toward the hybridization of automotive transmission systems. The efficiency and durability of hybrid powertrain depends on the heat generation in electric motors and their interactions among each other, ambient condition, the cooling system and the transmission component configuration. These increase the complexity of motor temperature prediction as well as the computational cost of running a conjugate heat-transfer based CFD analysis. In this paper, 1D physics based thermal model is developed which allows rapid and accurate component-wise temperature estimation of the electric motor during both steady-state and transient driving cycles.

The key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life. These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load. Studies have shown that thermal sub-system loads can reduce the drive range by as much as 45% under ambient temperatures below −10 °C. Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss. Reducing this range loss may improve consumer acceptance of BEVs. The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs.

With the demand of growing cooling requirements of fast charging and new thermal architecture design in battery electric vehicles, the automotive industry is exploring electric compressors of large displacement. Compared with small and mid-size (displacement less than 33 cc) compressor, large (34-44 cc) and extra-large (45 cc and above) compressor products are used. This paper investigates the compressor sizing effect for heat pump (HP) system of A-Segment and D-Segment battery electric vehicles. The system performance is evaluated with large (34 cc) and extra-large (57 cc) compressors by considering energy efficiency, cabin thermal management and battery fast charging use cases.