Electric buses are central to urban decarbonization, yet public transportation operators face uncertainty regarding battery degradation, climate sensitivity, and the economic value of vehicle-to-grid (V2G) services. This paper proposes a two-stage framework to address these challenges. First, degradation models are calibrated using accelerated aging tests on Lithium Iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC) cells under bus-representative conditions. Second, these models are embedded in a fleet-level dispatch optimization that co-optimizes charging and V2G participation while internalizing battery wear. Monthly throughput-to-end-of-life anchors are derived from weather profiles and incorporated into an annual model. Results show that LFP sustains 2.3–2.4 times the throughput-to-end-of-life of NMC, more than doubling the expected battery lifetime. Degradation-aware dispatch reduces annual operating costs by 12.6% and shifts V2G activity away from wear-prone batteries. Sensitivity analyses confirm robustness to changes in energy prices and replacement costs, providing actionable guidance for climate-resilient fleet electrification. By translating laboratory battery evidence into procurement, scheduling, and market-design guidance for public transport operators, the study bridges battery science, operations-research modeling for decision support, and transport policy, informing the interdisciplinary debate on how cities can electrify public transport at the lowest long-term economic and environmental cost.

The shift to sustainable transportation presents challenges regarding the acquisition or replacement of bus fleets. The need to consider multiple, conflicting and incommensurate factors such as environmental impact, cost-effectiveness, and technological issues makes the decision-making process more complex, time-consuming, and possibly ineffective, thus requiring an adequate multi-criteria evaluation framework. To this end, this study employs the stochastic multi-criteria acceptability analysis method, assessing the feasibility of transitioning to eco-friendly bus fleets by comparing diesel, hybrid, and electric buses while addressing uncertainty. Using the bus transportation system of Sherbrooke (Canada) as a case study, computational simulations generate energy consumption data and define bus system configurations. The case study evaluates five concrete alternatives over twelve criteria. The results show that an electric bus system with an overnight charging strategy outperforms other options (in 32 % of the cases) due to its reliability, cost-effectiveness, and mixed use of the bus fleet. Conversely, the diesel bus alternative consistently ranks lowest due to poor performance on economic and environmental criteria.

The accurate estimation of lithium-ion cell internal temperature is crucial for the safe operation of battery packs, especially during high discharge rates, as operating outside the safe temperature range can lead to accelerated degradation or catastrophic failures. Heat generation in lithium-ion cells arises primarily from ohmic losses and entropy change (ΔS), yet the latter remains frequently overlooked in battery modelling. However, the impact of considering or discarding ΔS from electro-thermal modelling remains subject to debate. This research highlights the critical role of ΔS in improving the accuracy of electro-thermal models for lithium-ion batteries, particularly in high-fidelity thermal simulations. It presents a systematic integration, ΔS, into electro-thermal models, leveraging the energetic macroscopic representation (EMR) approach to enhance predictive accuracy, a methodology not previously structured in this manner. This paper addresses this issue by performing a comparative analysis of an electro-thermal model (ETM) with and without ΔS. The findings provide clear insights into the role of entropy in electro-thermal modelling, demonstrating that while entropy change has a minimal impact on electrical behaviour prediction, it plays a crucial role in accurately capturing temperature dynamics, helping define the conditions under which it must be considered in simulations. While entropy can be neglected for coarse heat generation estimation, its inclusion enhances temperature prediction accuracy by up to 4 °C, making it essential for applications requiring precise thermal management. This study offers a detailed analysis of the conditions under which ΔS becomes critical to model accuracy, providing actionable guidance for battery engineers and researchers.

The performance and reliability of lithium-ion batteries, which are crucial for electric vehicles (EVs) and battery energy storage systems (BESS), are fundamentally dependent on the quality of their cells and components. Despite stringent quality control, intrinsic factors cause cell-to-cell variations (CtCV) in capacity and internal resistance. This paper explores the effects of CtCV in multi-cell battery modules, on current distribution, heat generation and evolution of temperature. This study presents a multi-cell electro-thermal model considering individual cell behavior, and interactions between parallel-connected cells. The Monte Carlo simulation method is used to study the correlations between CtCV and its global impact on overall battery performance. Our findings show that CtCV causes significant variations in cell behavior, particularly at high discharge rates, negatively impacting overall system performance. The effect of the number of cells in parallel is studied. This research provides a comprehensive understanding of the impact of CtCV and offers practical solutions to improve design and manufacturing of large battery modules for EVs and renewable energy applications.

This paper presents an advanced methodology focusing on Energetic Macroscopic Representation (EMR)-based modelling for accurate characterization, validation, and simulation of lithium cells in electric urban buses. Specifically tailored to the Karsan eJest minibus, this approach includes detailed cell characterization, rigorous validation processes, and comprehensive vehicle simulations. By leveraging real-world data and EMR techniques, the objective is to establish a robust framework that integrates detailed battery modelling with system-level performance analysis for precise and future aging studies. Through meticulous EMR-based modelling, validation, and vehicle simulation, this work lays a solid foundation for understanding battery dynamics and optimizing operational strategies for long-term performance and sustainability in electric urban transportation.

In the world of electric vehicle (EV) advancement, thorough testing of battery components is crucial. Our study introduces a focused power-hardware-in-the-loop (pHIL) method designed to validate EV battery module performance and improve vehicle range estimations. Addressing the challenge of integrating components with varying timelines, our approach ensures seamless testing throughout the product lifecycle. Recognizing the critical role of battery performance in overall vehicle behavior, particularly with lithium-ion batteries and their management systems, our method emphasizes thorough testing for EV safety and reliability. By exploring various testing techniques, including HIL methods, our study demonstrates the versatility and effectiveness of the pHIL approach. Our paper provides practical insights for industry professionals and researchers, showcasing how pHIL techniques can transform EV battery validation. With continuous testing and refined estimations, our method marks a significant step forward in electric vehicle design reliability.

The development of a battery management system (BMS) necessitates the collaboration of multiple engineering disciplines to create a customized solution. To optimize power and energy density at the pack level, the BMS must be seamlessly integrated, occupying minimal space in the overall assembly. This becomes particularly crucial for light electric vehicles (EVs) with limited space compared to passenger cars. Electronic hardware design is influenced by mechanical assembly, requiring careful component and sensor selection for optimal firmware performance. However, the literature often introduces algorithm solutions without proper validation on embedded processors, compromising accuracy for real applications. While selecting a lower-cost microcontroller may reduce retail expenses, it can impact firmware performance. This article explores the key aspects of BMS design and validation, emphasizing that comprehensive system awareness is essential for certain design decisions. It underscores the significance of validating algorithms for the battery state, crucial for effective lithium-ion battery (LiB) utilization, cautioning against compromising these algorithms for cost reduction. It includes a validation cycle case study to highlight the benefits of early validation in the process.

Reduced reliance on fossil fuels is a critical issue, and electrification of agricultural machinery is a solution for lowering greenhouse gas emissions in non-road transportation. By separating the load and drive from the engine, electrification the implement allows the engine to operate at higher efficiency. This study suggests a series hybrid design that combines a snow blower with battery support and a tractor with a reduced engine size to meet the fuel consumption reduction target while maintaining its performance. Accordingly, the Particle Swarm Optimization (PSO) algorithm is employed to size the battery that meets the constraints. Additionally, the motors of actuators are designed based on efficiency maps. Moreover, the rule-based energy management strategy is also proposed to assess the solution. This suggestion is analyzed using simulation and compared to a traditional tractor as a benchmark. The simulation results demonstrate the effectiveness of the strategy in lowering emissions from non-road machinery. Furthermore, this approach can be applied to various different types of implements attached to the tractor.

The Energetic Macroscopic Representation (EMR) is a formalism that focuses on the energetic exchanges of various systems that are connected together.It allows to represent the macroscopic interactions between them through an intuitive graphical representations.The EMR formalism is based on the concept of a macroscopic energy balance, which is used to describe the overall energy conversion process.It is useful for representing the physical behavior of complex energy systems, such as power plants, solar pannels, or vehicle powertrains.The graphical representation allows easier understanding and direct control of the system behavior, as well as the ability to quickly identify and troubleshoot potential model issues.Additionally, the EMR formalism can be used to develop control systems for energy systems, such as for optimal operation and energy efficiency.This paper will present the principles of EMR and introduce several vehicle powertrain engineering studies using EMR.

When modelling lithium-ion batteries thorough identification of parameters across the entire operation domain is necessary to capture non-linear variations of properties caused by temperature or state of charge. This work presents a parameter identification method using galvanostatic intermittent titration technique (GITT) to create high resolution look-up tables and response surfaces for equivalent circuit models (ECM). Significant improvements are proposed over other parameter estimation method, such as HPPC. These improvements are open-circuit voltage change compensation, and ohmic resistance correction, which yield to better overall accuracy of the model. An iterative parameter identification algorithm (IPIA) is introduced to increase the robustness of the non-linear least square curve fitting for higher orders ECM. The method is applied to identify the parameters of 1RC and a 2RC ECM. The use of GITT in conjunction with IPIA has allowed greater fidelity of model response surfaces than previously published pulse identification methods. Experiments were conducted on Nickel-Manganese-Cobalt cathode lithium-ion cells. The methodology presented in this paper is intended to be applicable to any lithium-ion battery format or chemistry with minor adjustments.

For each fuel cell (FC) system design, a constant operating temperature is frequently chosen to maximize its efficiency. Nevertheless, the temperature variation leads to changing the FC output at a given power. This adjustment in the FC current and voltage impacts the power electronics performance, thus influencing the performance of the fuel cell hybrid electric vehicle (FC-HEV). Therefore, this paper investigates the performance of coupling the FC and power electronics to choose this operating temperature. Firstly, an operating temperature adjustment model of the Proton Exchange Membrane Fuel Cell (PEMFC) system is established. The efficiency of the powertrain based on an embedded highperformance active switched quasi-Z-Source inverter (HP-ASqZSI) at various FC operating voltages is secondly realized by theoretical analysis. Opal-RT-based real-time simulation is then performed to validate the performance of the FC-HEV system against various temperatures in terms of efficiency and hydrogen consumption. Simulation results indicate that increasing the FC operating temperature from 25°C to 60°C and 70°C results in an improved FC-HEV efficiency by 1.09% and 1.14%, respectively. Moreover, the average total hydrogen consumption of the FC system is also decreased by 21.23% and 29.34%, respectively over the lowest operating temperature under the studied Artemis driving cycle.

In this paper, a novel high-performance active switched quasi-Z-Source inverter (HP-AS-qZSI) dual-source for fuel cell hybrid electric vehicle (FC-HEV) is proposed. In order to eliminate extra dc-dc converters, dual-energy sources based on FC and lithium-ion capacitors (LiCs) are integrated into the Z-source network (ZSN). By adding an anti-parallel power switch, the proposed topology enables to deal with the uncontrollable and distorted dc-link voltages in FCEV applications-based broad-range of loads over the traditional AS-qZSI. The modeling and the operation modes analysis are firstly presented. Real-time simulation based on Opal-RT is then implemented to validate the operation and performance of the proposed topology. As a result, it provides a higher average efficiency (3.06%) and lower component size and volume of passive elements for the EV system. Furthermore, this topology also indicates improved aging performance indexes of high specific-energy sources under the studied Artemis-long driving cycle, compared to the hybrid energy storage system conventional two-stage inverter.

This paper presents the new experimental electric vehicle (EV), which is adapted on the basic of the electric Formula SAE Hertz at the University id Sherbrooke. It is 4-wheel motored EV configuration, in which each wheel is driven by an independent motor. This structure enables us to exploit full advantages of electric motors, mainly in fast torque generation. Therefore, various advanced methods can be applied to obtain good motion control performance. In the paper, the vehicle is first introduced, the laboratory-made electric motors are then described. The modelling of the system is made by using EMR. The research topics on motion control are presented using our new platform. Case studies, including disturbance observer based (DOB) anti-slip control and optimization force distribution control have been provided. In the future, different advanced traction and motion control techniques will performed on this new platform.

The use of hybrid energy sources in electric vehicles is an interesting prospect towards extending batteries lifetime and vehicles autonomy. Supercapacitors are used in this study to absorb the high current variations during the real use of a light electric vehicle. This paper aims to determine the number or battery and supercapacitors modules to associate in parallel, following a passive topology, to respond to the requirements of a driving cycle for a three-wheel recreational vehicle. The vehicle has been modelled following the energetic macroscopical representation formalism. Module-scaled models of batteries and supercapacitors have been made describing their behavior in relation to their state-of-charge using the galvanostatic intermittent titration technique. This approach allows significant gains in calculation time compared to a cell-scaled model for full-scale vehicle simulations. Standard FTP and WLTC driving cycles are used for the comparisons. They firstly highlight the need of a sufficient number of battery modules to limit the current through them and preserve battery cells. Also, they show the need for supercapacitors to reduce both the amplitude and the magnitude of current peaks in the batteries. Results are of very different nature considering the driving cycle. Several supercapacitors modules are needed to best preserve batteries in urban context. However, for a driving cycle involving higher speeds, they show to be barely useful.

State-of-charge (SoC) of an electric vehicle (EV) battery pack is a crucial information for the driver. The accuracy of the SoC estimation algorithms is often limited by the computational resources of the electronic hardware. Ensuring that required performances are maintained between the design stage and the implementation in a low-cost microcontroller is critical. The growing demand for light EVs increases the need for precise and computationally-light algorithms for low-cost Battery Management System (BMS). This paper proposes a novel SoC estimation method that deals with accuracy and simplicity. A disturbance observer-based (DOB) algorithm is developed to offer a simple solution that reduces the computational time while achieving similar performances of other well-known SoC estimators. The new estimator has been implemented into an on-board system to be more representative of the real computational resources. The validation of the algorithm has been done with a customized hardware-in-the-loop system that emulates the battery electrical signals of the BMS sensors. The validation uses a real vehicle speed profile recorded on a three-wheel light EV. The performance of the proposed method has been compared to the other estimators. The results indicate that the DOB-algorithm SoC estimator can achieve a faster and more accurate estimation than the conventional approaches.

Lithium-ion battery packs are often made of multiple groups of parallel cells connected in series. This article addresses how the inherent variability in lithium-ion cell properties due to manufacturing inconsistencies may cause un-even current sharing between them when used in modules. Non uniform current sharing may cause some cells to overheat, that could lead to a thermal runaway. Results show that the intrinsic variability of cells creates large differences in currents and temperature, suggesting that designs could wrongfully be considered safe if the variability of the properties of the cells happens to be neglected. These differences in temperature and current are also shown to increase as the power demand increases, confirming that the parallel interaction of cells must be considered for the performance calculation for high discharge rate applications.

The validation of a Battery Management System (BMS) is a complex task that involves hardware and software components such as an analog front-end (AFE), a balancing circuit and a microcontroller with estimation algorithms and fault management. Test conducted with real batteries can become time-consuming and a Hardware-in-the-loop (HIL) simulator can be more efficient and safer when it comes to testing situations outside the normal range of operation. In this paper, a cell emulator circuit has been designed to create a custom HIL system for BMS validation that can be used with any standard real-time computer or signal generation hardware. This HIL emulates the behavior of battery cells dynamic in order to validate the voltage monitoring and state-of-charge (SOC) estimation function of the BMS. The system can supply current directly to the BMS to test the balancing function of the BMS. Initial tests show good performance of the cell emulator system compare to a real cell discharge. This system can standardize the validation of BMSs while being an affordable solution for simulation test systems.

Despite several advances in regenerative braking (RB) strategies for electric vehicle applications, the developments associated to the case of an electric motorcycle remain very limited. Indeed, since the rear wheel load of an electric motorcycle is light during braking, the RB torque contribution is usually limited accordingly to avoid a rear wheel lockup. The fine-tune modulation of the RB torque by the driver is thereby constrained as well. As a contribution, this paper proposes an ergonomic novel strategy to pilot the RB torque. Hence, through the combined use of the twist grip, the "regen shift pedal" and the left-hand "regenerative brake lever," it emulates the engine braking effect of a gasoline motorcycle. Indeed, while braking, a predefined amount of RB torque, proportional to the vehicle speed, can be increased by " downshifting" the regen shift pedal and the resulting RB torque can be modulated through both the twist grip and the RB lever. More specifically, the purpose of the RB lever is to emulate the quick release effect of the clutch on the engine braking effect. Through simulations with identified parameters, the behavior of the strategy is verified while a more comprehensive verification will be done through road test in future work.

This paper presents a method for battery pack sizing for electric vehicles, applied to the case of an electric motorcycle. A novel way of analyzing battery pack performances in a single graphical tool is proposed. It is intended to help engineers get a broader understanding of the influence of design decisions from the early stages of the engineering process. In multi-cell battery packs, specifications such as energy, power, volume and mass are proportional to the total number of cells, while voltage, current are dependent of the series and parallel arrangement. Thus, presenting results as functions of the number of cells in series and parallel allows to compare quickly the various performance metrics of a pack. By applying the design constraints of the technical requirements as limiting functions, one can easily select a suitable solution that meets design goals, or assess the effect of design constraints. This graphical design tool could be used to size other electric energy storage devices such as lithium-capacitors, super-capacitors or battery pack of other chemistries than lithium- ion.