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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.

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

Dual three-phase (DTP) permanent magnet synchronous machines (PMSMs) are becoming attractive for electric vehicle (EV) propulsion systems in the automotive industry. Flux-weakening (FW) control technique is important to ensure DTP-PMSMs operating in high-speed range. This paper proposes an adaptive FW control algorithm to ensure better performance and stability in variant speeds. A small-signal model is developed to obtain the adaptive gain for a constant controller bandwidth regardless of the speeds. The proposed FW controller is implemented, tuned, and validated on a DTP-PMSM experiment setup. The proposed method improves the FW performances in terms of torque and system stability, compared with the non-adaptive FW controller. Moreover, the harmonics analysis shows an inevitable xy-components affecting the overall performances. The effect of xy controller gain is further investigated for the FW operation.

The modern automotive industry field is in the middle of a huge transformation of the Electric & Electronics (E/E) system design in order to meet the future mobility trends: driven by autonomy, electrification and connectivity. Autonomy (as defined by SAE J3016) implies five levels of driving automation and will include an explosion of sensors and computing power. As well, functional safety and cybersecurity constraints will increase. Electrification implies replacing energy from thermal sources with electricity from the wall and will include enhanced integration between sub-systems and components, along with higher speed in real time controls. Connectivity will provide huge data mining capability, along with enhanced off-board communication (so-called “Vehicle-to-Everything” or V2X) and remote software updates (FOTA).

As compared with other batteries, lithium-ion batteries are featured by high power density, long service life, high energy density, environmental friendliness and thus have found wide application in the area of consumer electronics. However, lithium-ion batteries for electric and hybrid electric vehicles (EVs and HEVs) have high capacity and large serial-parallel numbers, which, coupled with such problems as safety, durability, cost and uniformity, imposes limitations on the wide application of lithium-ion batteries in the EVs and HEVs. The narrow area in which lithium-ion batteries operate with safety and reliability necessitates the effective control and through the use of management of battery management system. Battery state of health (SOH) monitoring has become a crucial challenge in EVs and HEVs research, as SOH significantly affects the overall vehicle performance and life cycle. This paper presents both cycling and calendar aging at high and low temperatures.

Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.

The integrity of isolation resistance of the xEV high-voltage subsystems is critically important to ensure the safety of vehicle occupants. The term xEV collectively refers to the hybrid electrical vehicle (HEV), plug-in HEV (PHEV), battery electric vehicle (BEV), fuel cell electric vehicle (FCEV), and range extended electric vehicles (REEV) etc. As described in the Federal Motor Vehicle Safety Standards (FMVSS) 305, the isolation resistance is indirectly determined based on the measured voltages. The existing formula used for calculating the isolation resistance does not take into account the finite input-resistance of the voltage measurement equipment. The assumption of infinite input resistance of the voltage measurement equipment leads to significant error, which will be quantified in this paper. The proposed method in this paper includes the input-resistance and results in accurate calculation of isolation resistance.

A comparative analysis of overmodulation methods is performed in the generalized form in this paper. The generalized form is based on four segmented formulae, which streamlines the execution of the PWM module. The comparative analysis considers five aspects: actual modulation index, harmonic content, transition to six-step operation, modulation index linearization, and execution complexity. The main contributions of this paper are twofold. Firstly, a thorough assessment of conventional overmodulation strategies for dual three-phase PMSM drives is undertaken. Secondly, a modified Minimum Phase Error (MPE) overmodulation method is proposed to extend the overmodulation to six-step operation. The modified MPE is introduced with advantages of wider modulation index range, low harmonic components in voltages and currents, smooth transition to six-step operation, and simple implementation.

Battery packs of electric vehicles are typically composed of lithium-ion batteries with aluminum and copper acting as cell terminals. These terminals are joined together in series by means of connector tabs to produce sufficient power and energy output. Such critical electrical and structural cell terminal connections involve several challenges when joining thin, highly reflective and dissimilar materials with widely differing thermo-mechanical properties. This may involve potential deformation during the joining process and the formation of brittle intermetallic compounds that reduce conductivity and deteriorate mechanical properties. Among various joining techniques, laser welding has demonstrated significant advantages, including the capability to produce joints with low electrical contact resistance and high mechanical strength, along with high precision required for delicate materials like aluminum and copper.

With electromobility, vehicles are becoming quieter due to the presence of electric motors that replace internal combustion engines. The interior cabin noise of electric vehicles is characterized by high-frequency components that can be annoying and unpleasant. Therefore, it is essential to analyse the NVH behaviour of e-powertrains early in the design-phase. However, this induces inherent uncertainties during the design process related to the operating conditions, geometrical parameters, measurement techniques, etc. that need to be quantified with fast and comprehensive stochastic models. In this work, we first present a deterministic framework to provide first-order estimations of the e-powertrain’s interior whining noises, combining both the airborne & structure-borne contribution with data-driven NVH transfers meta-models.

A new method is presented for developing proving ground durability tests for electrified vehicle (xEV) drivetrains. For xEVs, the durability tests must be able to capture the added driveline torques due to regenerative braking, customer battery charge/recharge usage, and battery power degradation over time. In terms of xEV duty cycle test schedule development, using actual vehicle data from specific proving ground test events might prove to be costly and time consuming. Furthermore, newer electrified prototype vehicles might not be available for trackside testing while still undergoing the design stage. The new method presented here aims to reduce the complexities of xEV proving ground duty cycle development with the use of a high-fidelity vehicle dynamics simulation model for electrified powertrains.

Battery terminal voltage modelling is crucial for various applications, including electric vehicles, renewable energy systems, and portable electronics. Terminal voltage models are used to determine how a battery will respond under load and can be used to calculate run-time, power capability, and heat generation and as a component of state estimation approaches, such as for state of charge. Previous studies have shown better voltage modelling accuracy for long short-term memory (LSTM) recurrent neural networks than other traditional methods (e.g., equivalent circuit and electrochemical models). This study presents two new approaches – sequence training and data shuffling – to improve LSTM battery voltage models further, making them an even better candidate for the high-accuracy modelling of lithium-ion batteries. Because the LSTM memory captures information from past time steps, it must typically be trained using one series of continuous data.

Recently, the automotive industry has experienced rapid growth in powertrain electrification, with more and more battery electric vehicles (BEV) and hybrid electric vehicles being launched. Lithium-ion batteries play an important role due to their high energy capacity and power density, however they experience high heat generation in their operation, and if not properly cooled it can lead to serious safety issues as well as lower performance and durability. In that way, good prediction of a battery behavior is crucial for successful design and management. This paper presents a 1D electrochemical model development of a 144 Ah prismatic rolled cell using the GT-Autolion software with a pseudo 2D approach. The model correlation is done at cell level comparing model results and test data of cell open circuit voltage at different temperatures and voltage and temperature profile under different C-rates and ambient temperatures.

Modern automotive industry field is recently moving to more electrification level, so the presence of Battery Electric Vehicles (BEVs) is constantly increasing, along with charging technology evolution. Typically, BEVs do not use a significant portion of their battery’s capacity in day-to-day travel, which means their most valuable asset, the battery, sits idle during most of its life. Vehicle to Load (V2L) feature enables the transfer of energy from vehicle to the external loads (like utility tools, dryer, camping equipment or any other electrical appliance) which is connected to the power socket present in the Power Panel to perform AC Discharging. V2L technology lets consumers get more energy from a vehicle, even when it is turned off, improving consumer appeal. Bottomline, consumers can use this on-board Power Panel like a normal portable generator.