Individual information

This paper studies how the user charging practice affects battery degradation over time. To achieve this objective, a system oriented simplified aging model based on the literature is proposed. The differential calculation of the capacity loss is used for infinitesimal variations. The model inputs are the battery state of charge, the battery temperature and the cumulative number of full equivalent cycles. The output is the battery state of health. This model is identified and validated with experimental aging tests from the Renault Zoe 41kWh battery manufacturer. The battery model (electro-thermal and aging) interconnects with the vehicle traction model complete the system model. The battery electro-thermal and traction models are also validated with measurements on the studied vehicle. The Energetic Macroscopic Representation (EMR) formalism organizes in a unified way the interconnections of all the sub-system models. The impact of the charging interval and SoC on the battery aging is then studied. Five charging scenarios are studied by simulation while keeping the driving phases and the charging current the same. In these conditions, the average SoC is the main contributor for the battery aging. Compared to daily charge of the EV, a charge every 4 days extends the time to reach 80% of state of health by 36 % due to lower average SoC. The daily driving distance is fixed for every studied scenario.

DOI: 10.3390/en13215538

The combination of batteries and supercapacitors is promising in electric vehicles context to minimize battery aging. Such a system needs an energy management strategy (EMS) that distributes energy in real-time for real driving cycles. Pontryagin’s minimum principle (PMP) is widely used in adaptive forms to develop real-time optimization-based EMSs thanks to its analytical approach. This methodology leads to an off-line optimal solution which requires an extra adaptive mechanism for real-time applications. In this paper, a simplification of the PMP method is proposed to avoid the adaptation mechanism in real-time. This new EMS is compared to well-known conventional strategies by simulation. Furthermore, experimental results are provided to assess the real-time operation of the proposed EMS. Simulation and experimental results prove the advantages of the proposed approach by a reduction up to 50% of the batteries rms current on a real-world driving cycle compared to a battery-only EV.

Despite their great improvements, reliability and availability of power electronic devices always remain a focus. In safety-critical equipment, where the occurrence of faults can generate catastrophic losses, health monitoring of most critical components is absolutely needed to avoid and prevent breakdowns. In this paper, a noninvasive health monitoring method is proposed. It is based on fuzzy logic and the neural network to estimate and predict the equivalent series resistance (ESR) and the capacitance (C) of capacitors and supercapacitors (SCs). This method, based on the neo-fuzzy neuron model, performs a real-time processing (time series prediction) of the measured device impedance and the degradation data provided by accelerated ageing tests. To prove the efficiency of the proposed method, two experiments are performed. The first one is dedicated to the estimation of the ESR and C for a set of 8 polymer film capacitors, while the second one is dedicated to the prediction of the ESR and C for a set of 18 SCs. The obtained results show that combining fuzzy logic and the neural network is an accurate approach for the health monitoring of capacitors and SCs.

This paper aims to study the recharge time for a battery as a function of the temperature. An electro-thermal model is used for the battery to simulate the evolution of the battery internal temperature. The electrical parameters of the battery are dependent on the temperature and the state of charge. They have been characterized from an electric vehicle Lithium Iron Phosphate (LFP) cell. Instead of a classical Constant Current, Constant Voltage (CC-CV) technique, a current trajectory is used for charging. The goal is to recharge at maximal admissible current to be as fast as possible. The maximal admissible charge current is a function of the battery internal temperature. Simulation are performed for a 0 °C to 40 °C temperature range. The simulation results show that the fast charging is slower when the ambient temperature is increasing.

The Energetic Macroscopic Representation formalism is more and more used for the modelling and control organization of energy conversion systems. Many applications for electric vehicles have been developed using this formalism. If different simulation packages have included EMR libraries, there is still no drawing software dedicated to this formalism. To address this limitation, an open-source EMR drawing software is proposed in this paper. Our proposed software aims to provide a comprehensive solution for visualizing and organizing EMR models. By offering a user-friendly interface and compatibility with different word processing software, this tool contributes to the advancement and wider adoption of EMR in energy conversion system design and control.

he aim of this paper is to validate the compliance between an EV and a battery with a reference driving cycle. The Hardware in the Loop methodology is used. It is based on the interactions between the different EV subsystems. In this paper, a 35 kWh battery pack is studied. A 5 kWh module from this pack is tested with the same power constraints when used in the real EV. The developed power Hardware-in-the-Loop (HiL) test bench is composed of a model of the traction chain and a controllable current source. A driving velocity cycle is used as an input. The HiL test is achieved at full power scale for a module. It is shown that the tested battery could be used in the studied EV as the limits of the battery operation are not crossed for a WLTC velocity cycle.

This paper shows that the way of charging electric vehicle influences the battery aging. A simplified lithium-ion battery calendar aging model is used. This simple battery lifetime model, estimates the capacity fade including the effects due to the temperature and the state of charge. The reference vehicle is a 2018 Nissan Leaf. The entire vehicle, including the battery, the traction system and the road, is simulated with a reference velocity cycle. The energetic macroscopic representation of the vehicle is used for model and control organization. Two charging scenarios are defined. The first scenario corresponds to a daily vehicle charge, while the second one corresponds to one charge every 5 days. The model including the aging of the battery shows a significant difference between the two scenarios in term of capacity degradation through years.

The Toyota Mirai is a fuel cell vehicle with a hybrid energy storage subsystem (battery and fuel cell). Its small-size battery has a limited state-of charge (SoC) variation of only 10%. In order to reduce the fuel cell current peak, a new control scheme is proposed by merging two control laws. This merging offers a supplementary degree of freedom for the energy management strategy. In simulation, an increase the battery SoC variation by only 10% leads to 20% reduction of the fuel cell current for a WLTC driving cycle.

This paper studies the effect of the granularity of a cell model on the voltage accuracy. A multi-coupled cell model with varying parameters is compared with a simpler one (varying voltage source and equivalent series resistance). The first model implies a very long and complex characterization process. The second one is very simple. Experiments are performed at different temperatures by applying a current profile corresponding to a driving cycle of an electric vehicle. Experimental results show both models can be used for 25°C and 10 °C ambient temperatures with reasonable accuracy. Nevertheless, when the temperature is cold the multi-coupled model is more accurate.

The automotive industry is shifting to mass production of electrified vehicles. Simulation is a key step for developing these new vehicles. System simulation tools as Simcenter Amesim includes ready-to-use multi-physics libraries in that perspective. A new library is being created based on the Energetic Macroscopic Representation formalism to speed-up the battery electric vehicles (BEV) development process. The paper presents the BEV multi-level simulation where the simulations were done using 3 different levels for the battery models. By using “plug & play” method different BEV model configurations can be built in a fast, precise and systematic way.

Equivalent circuit models are widely used to describe the electrical behavior of batteries. They are also prioritized for energetic studies and driving range estimations of electric vehicles. This is mainly due to their simplicity and acceptable accuracy. This paper presents a comparison of the accuracy of two equivalent circuit models (Rint and Thevenin) under a driving scenario. For this, a Hardware-in-the-Loop simulation of a traction subsystem is used to impose a current profile into a real Li-ion battery module. This current profile is deduced from a reference velocity profile. Then, the measured battery voltage and current are compared to those of the simulated models, by imposing the same reference velocity. Results show a small error difference between the two models.

This study deals with the driving range evolution of an EV as a function of the cumulated hours of operations. The studied vehicle is a three-wheel electric vehicle (e-TESC 3W platform) developed and instrumented at the University of Sherbrooke. The system model is built and organized using the Energetic Macroscopic Representation. A current cycle is applied with a programmable source on one cell to emulate the driving current constraints. The cell model parameters are characterized periodically. Then, the EV simulation is performed with the battery parameters corresponding to different cumulated driving hours. It is shown that the driving ranges is reduced by about 12% after 200 h of operations.

Parallel hybrid electric vehicles (HEVs) are suitable for heavy-duty applications like trucks. Yet there is an issue of this power configuration that the electrical drives have to work with many fluctuations which can degrade the batteries. Supercapacitors (SCs) can be added to save the batteries life-time. However, it also adds more losses to the system which can increase the engine fuel consumption. This paper aims to study the impact of SCs subsystem on fuel consumption and batteries life-time of a hybrid truck. Energetic Macroscopic Representation (EMR) is used to deal with the complexity of the studied system. A fair comparison is achieved by using dynamic programming due to its global optimal solutions. Simulation results figure out that the SCs subsystem causes 14% decrease of the maximum optimal saved fuel but can reduce up to 49% of batteries rms current for a specific hybrid truck. A positive effect of using hybrid energy storage subsystems for HEVs can be therefore concluded.

Hybrid electric vehicles (HEVs) can be supplied by hybrid energy storage systems (H-ESSs). Such kind of system needs to be handled by complex energy management strategies (EMSs) considering several objectives. Besides, to evaluate such EMSs, an optimal benchmark could be developed. Dynamic programming (DP) is suitable for that purpose because of its ability to obtain the optimal solution. However, it is non-trivial to address a multi-objective energy management problem by using DP because of the complexity of the system and computational issues. This paper develops a DP-based optimal EMS for a hybrid truck supplied by battery/supercapacitor H-ESS using a bi-level optimization approach. This approach decomposes the EMS into optimal sub-strategies regarding the structure of the studied system. The validation of the optimal strategy is demonstrated by simulation results.

Battery operation is influenced by the temperature. Thus, thermal modelling is an important aspect for battery electric vehicles (EVs). Generally the electro-thermal parameters are identified for one single cell. In EVs the battery is divided into several modules (groups of cells side by side). The objective of this paper is to quantify the accuracy by using the electro-thermal parameters of one single cell to model the behavior of a module. The temperature of a cell inside a module and the voltage of this module are recorded during a real driving cycle in an instrumented commercial EV. Considering the module as a group of identical single cells leads to an error of 7% for module voltage and 4.8% on the self-heating.

Hybrid energy storage systems (H-ESSs), as hybridizations of batteries and supercapacitors (SCs), need to be handled by appropriate energy management systems (EMSs). Smoothness of battery current to reduce their degradation are normally the aim of the EMSs. The evolutions of SC voltage however also need to be addressed. Various approaches for handling SC voltage while managing battery current can be found in the literature. Most of the time, their limitations are achieved directly or indirectly in the EMS. This paper aims to develop a merging control scheme to include the SC control outside the EMS. This control scheme based on the inversion principle of the Energetic Macroscopic Representation is proposed for a battery/SC subsystem. Two control schemes are thus systematically deduced and then merged. The battery current reference is generated regarding their aging behavior. Meanwhile, the SC voltage reference is deduced concerning the required SCs stored energy as a function of the vehicle velocity. The proposed approach is validated by simulation in Matlab/Simulink®.

Charging a battery packs is a critical point for electric vehicles (EVs). The customer is seeking for fast charging. However, the current level has a negative impact on the battery pack lifetime. This paper aims to study through simulation results the self-heating of a battery pack used in an EV as a function of the charging current. Energetic Macroscopic Representation (EMR) is used to organize the control of current and voltage of battery pack while coupling electrical and thermal domains. The study shows that the self-heating of an EV pack can be sizeable even under the EV manufacturer charging current limits. This is why EV manufacturers are advising customers to reduce the occurrence of fast charges.

Fuel consumption is a critical issue of hybrid vehicles, especially the heavy-duty ones. Studies on energy saving ability considering different power and energy capabilities of the electrical components are therefore of interest. This paper aims to compare the maximal fuel saving of the hybrid truck with different hybridizations. Modeling, control, and energy management of the system are carried out using Energetic Macroscopic Representation formalism. Dynamic programming is employed as a global optimization-based strategy for energy management of the vehicle. An 8.5-ton parallel hybrid truck primarily driven by a 147-kW internal combustion engine is under study. Simulation results point out a preferred hybridization option by using a 120-kW electrical machine supplied by a 19.4-kWh Li-ion battery pack. A respective 21.6% reduction of fuel consumption is reported for USA FTP Highway driving cycle.

Energy management strategies are mandatory for hybrid energy storage systems in applications for electric and hybrid vehicles. Optimization–based real–time strategies are of interest since they are straightforward to optimize performance criteria. The available methods are often the combinations of adaptive mechanisms with solutions deduced by optimal control techniques such as Pontryagin’s minimum principle (PMP). This paper proposes an approach to develop a real–time strategy for battery/supercapacitor systems based on PMP and Energetic Macroscopic Representation without any need of adaptive mechanism for the co-state variable. Performances of the proposed strategy are validated by simulation.

The paper deals with the design and the control of a battery/supercapacitors energy storage system for a CVT-based hybrid electric truck. The battery/supercapacitors system is used as a secondary source in the vehicle instead of using a battery like conventional hybrid vehicles. The proposed vehicle represents then a complex system to control. Simulation results on a real driving cycle validate the energy management of the hybrid truck. The supercapacitors bank enables to avoid some battery current peaks, what is interesting for the battery lifetime. The battery size is furthermore reduced in comparison to a battery-only hybrid truck.

Energy management strategies are mandatory for hybrid energy storage systems in applications for electric and hybrid vehicles. Optimization-based real-time strategies are of interest since they are straightforward to optimize performance criteria. The available methods are often the combinations of adaptive mechanisms with solutions deduced by optimal control techniques such as Pontryagin's minimum principle (PMP). This paper proposes an approach to develop a real-time strategy for battery/supercapacitor systems based on PMP and Energetic Macroscopic Representation without any need of adaptive mechanism for the co-state variable. Its performances are validated by simulation.

Using supercapacitors (SCs) to configure the hybrid energy storage system (H-ESS) to extend battery life-time is promising for electric vehicles (EVs). Energy management strategy (EMS) is mandatory for such systems. Most of previous works develop improved EMS with the only one objective, normally on battery life-time. However, at least the costs on SCs operation also need to be taken into account. Generally, a methodology to develop multi-objective EMS is demanded. This paper presents a procedure to develop multi-objective EMS of H-ESS for an EV. SCs system losses are considered as the second cost function. Dynamic programming is used to give the benchmark in order to evaluate the performance of the other strategies. Simulation results validate the effectiveness of the developed off-line EMS. The dynamic programming generates a Pareto front to be used in order to compare with other on-line EMS.

Nowadays energy storage is the keypoint of the development of electrical vehicles (EV) for charging time, autonomy and cost reasons. The main energy storage system for electrical vehicles is the battery and can cost up to one third of the vehicle price. Nevertheless the battery has limited power features and can be damaged by EV peak power requirements. Thus higher power density energy storage systems (such as supercapacitors) are used in combination with batteries (multisource EV). A reduced-scale EV demonstrator has been developed to present the concept of multi-source EV to high school and undergraduate students. The aim of this demonstrator is to highlight the interest of research on electric vehicle to attract students in this research topic.

In electric vehicles, supercapacitors (SC) are commonly used to perform a hybrid energy storage system (H-ESS). This approach, correctly coordinated and designed, can extend battery lifetime by limiting the battery current stress. SC are often coupled to DC/DC converters. To use the maximum energy stored in SC, high variability of the SC voltage is mandatory. Several time, the associated converter voltage ratio becomes too high and can produce instability of the system. Thus, at each time there is a minimal value for the SC voltage. This article proposes a dynamical limitation method for the SC voltage. The dynamic method is compared with a traditional constant limitation technique. Both are tested under the urban part of New European Driving Cycle with slope. The results point out that the dynamical limitation can ensure a better stability of the system regardless environment disturbance.