AB Volvo
  • Göteborg, Sweden
Recent publications
Solar Photovoltaic (PV) power is the most common and efficient renewable energy source. The potential for utilizing solar PV energy on Earth is enormous. PV module characteristics significantly influence extracting maximum output from current solar power installations. Solar insolation and panel temperature substantially influence the voltage-current (V I) and power–voltage (P V) properties. The degree to which the system’s efficiency may be improved is determined by the accuracy with which the maximum power point tracking (MPPT) controller follows the nonlinear PV characteristics. This comprehensive investigation delves into the solar PV system dynamics, highlighting how modules induce power loss because of partial shading-induced peaks. Modified Adaptive Jaya Optimization (MAJO) is a novel meta-heuristic optimization approach designed to monitor maximum global power. MAJO boasts advantages over the conventional Deterministic Jaya Optimization (DM-Jaya) method under partial shading configurations (PSCs), exhibiting quicker convergence times and requiring fewer computing operations. MATLAB/Simulink and experimental scenarios are simulated and assessed under various environmental conditions, including step changes in solar irradiance, partial shading, and load variation, to evaluate the effectiveness of the proposed MAJO algorithm. The acquired findings are compared with the DM-Jaya and the Modified particle swarm optimization (MPSO), Maximum power trapezium (MPT) and Voltage window search (VSW) suggesting and demonstrating that the proposed MAJO algorithm yields better MPPT performance. Rigorous testing with a prototype experimental setup yielded a remarkable 99.9% tracking efficiency for the studied PSCs, accompanied by reduced settling times of 0.6s and convergence time of 0.154s. Additionally, the efficacy of the MAJO in terms of faster convergence characteristics, and attaining the optimal solution has been investigated on several unconstrained benchmark functions.
The increasing adoption of renewable energy sources (RES), such as solar photovoltaics and wind turbines, is transforming electricity generation. However, integrating RES within DC microgrids (DCM) for applications such as fast DC charging in electric vehicles (EVs) presents challenges, including low inertia, power fluctuations, and voltage instability. This study addresses these challenges with novel control strategies and optimization algorithms. A hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach is used to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. In addition, a comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. Crucially, a hardware prototype validates these strategies under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their effectiveness and feasibility for practical DC microgrid applications with integrated EV charging.
Freeway on-ramps are typical bottlenecks in the freeway network due to the frequent disturbances caused by their associated merging, weaving, and lane-changing behaviors. With real-time communication and precise motion control, Connected and Autonomous Vehicles (CAVs) provide an opportunity to substantially enhance the traffic operational performance of on-ramp bottlenecks. In this paper, we propose an upper-level control strategy to coordinate the two traffic streams at on-ramp merging through proactive gap creation and platoon formation. The coordination consists of three components: 1) mainline vehicles proactively decelerate to create large merging gaps; 2) ramp vehicles form platoons before entering the main road; 3) the gaps created on the main road and the platoons formed on the ramp are coordinated with each other in terms of size, speed, and arrival time. The coordination is formulated as a constrained optimization problem, incorporating both macroscopic and microscopic traffic flow models. The model uses traffic state parameters as inputs and determines the optimal coordination plan adaptive to real-time traffic conditions. The benefits of the proposed coordination are demonstrated through an illustrative case study. Results show that the coordination is compatible with real-world implementation and can substantially improve the overall efficiency of on-ramp merging, especially under high traffic volume conditions, where recurrent traffic congestion is prevented, and merging throughput increased.
In the evolving landscape of the fourth industrial revolution, the integration of cyber-physical systems (CPSs) into industrial manufacturing, particularly focusing on autonomous mobile manipulators (MMs), is examined. A comprehensive framework is proposed for embedding MMs into existing production systems, addressing the burgeoning need for flexibility and adaptability in contemporary manufacturing. At the heart of this framework is the development of a modular service-oriented architecture, characterized by adaptive decentralization. This approach prioritizes real-time interoperability and leverages virtual capabilities, which is crucial for the effective integration of MMs as CPSs. The framework is designed to not only accommodate the operational complexities of MMs but also ensure their seamless alignment with existing production control systems. The practical application of this framework is demonstrated at the Platform 4.0 research production line at Arts et Métiers. An MM named MoMa, developed by OMRON Company, was integrated into the system. This application highlighted the framework’s capacity to significantly enhance the production system's flexibility, autonomy, and efficiency. Managed by the manufacturing execution system (MES), the successful integration of MoMa exemplifies the framework's potential to transform manufacturing processes in alignment with the principles of Industry 4.0.
An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.
This paper explores a novel hybrid configuration integrating a Reverberation Chamber (RC) with a Compact Antenna Test Range (CATR) to achieve a controllable Rician K-factor. The focus is testing directive antennas in the lower FR2 frequency bands (24.25-29.5 GHz) for 5G and beyond wireless applications. The study meticulously evaluates 39 unique configurations, using a stationary horn antenna for consistent reference K-factor characterization, and considers variables like absorbers and CATR polarization. Results demonstrate that the K-factor can be effectively adjusted within the hybrid setup, maintaining substantial margins above the noise level across all configurations. Sample independence is confirmed for at least 600 samples in all cases. The Bootstrap Anderson-Darling goodness-of-fit test verifies that the data align with Rician or Rayleigh distributions. Analysis of total received power, stirred and unstirred power and frequency-dependent modeling reveals that power variables are inversely related to frequency, while the K-factor remains frequency-independent. The hybrid RC-CATR system achieves a wide range of frequency-averaged K-factors from -9.2 dB to 40.8 dB, with an average granularity of 1.3 dB. Notably, configurations using co-polarized CATR signals yield large K-factors, reduced system losses, and improved frequency stability, underscoring the system’s efficacy for millimeter-wave over-the-air testing. This research offers a cost-efficient and repeatable method for generating complex Rician fading channels at mmWave frequencies, crucial for the effective OTA testing of advanced wireless devices.
The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other’s actions and intentions. To achieve this, strategies for human- robot interaction are needed that are adapted to operators’ needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.
This paper presents an analysis of a multi-pump solution for a hydraulic cylinder for application in mobile machinery with electric prime movers. The flexibility provided by using electric motors instead of an internal combustion engine allows for the design of alternative hydraulic architectures that remove the need for a centralized pumping system and support more direct control of individual components. This allows them to operate at a higher efficiency region to improve overall vehicle efficiency, leading to smaller batteries and shorter or less frequent recharge periods. To evaluate the capabilities of this proposal, this paper focuses on a backward calculation analysis of a single actuator operating with multiple pump/motors connected to each chamber. A series of hydraulic machines with fixed displacement and identical sizes are connected to the actuator chambers through on/off directional valves. The system controls the flow by using the required pumps and selecting their optimal speeds to minimize energy consumption or maximize energy recovery. The results show how the number of pumps affects the system’s performance and provide insights regarding the selection of operating machines according to the actuator speed and force.
In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT, and advanced sensors converge to shape smarter future factories. Mobile manipulators (MMs) are pivotal, fostering flexibility, adaptability, and collaboration in industrial processes. On one hand, MMs offer a remarkable level of flexibility, adaptability, and collaboration in industrial processes, facilitating swift production line changes and efficiency enhancements. On the other hand, their integration into real manufacturing environments requires meticulous considerations, such as safety, human–robot interaction, and cybersecurity. This article delves into MMs’ essential role in achieving Industry 4.0’s automation and adaptability by integrating mobility with manipulation capabilities. The study reviews MMs’ industrial applications and integration into manufacturing systems. The most observed applications are logistics (49%) and manufacturing (33%). As Industry 4.0 advances, the paper emphasizes updating and aligning MMs with the smart factory concept by networks of sensors and the real-time analysis of them, especially for an enhanced human–robot interaction. Another objective is categorizing considerations for MMs’ utilization in Industry 4.0-aligned manufacturing. This review methodically covers a wide range of considerations and evaluates existing solutions. It shows a more comprehensive approach to understanding MMs in Industry 4.0 than previous works. Key focus areas encompass perception, data analysis, connectivity, human–robot interaction, safety, virtualization, and cybersecurity. By bringing together different aspects, this research emphasizes a more integrated view of the role and challenges of MMs in the Industry 4.0 paradigm and provides insights into aspects often overlooked. A detailed and synthetic analysis of existing knowledge was performed, and insights into their future path in Industry 4.0 environments were provided as part of the contributions of this paper. The article also appraises initiatives in these domains, along with a succinct technology readiness analysis. To sum up, this study highlights MMs’ pivotal role in Industry 4.0, encompassing their influence on adaptability, automation, and efficiency.
div class="section abstract"> Next-generation vehicle electrical architectures will be based on highly sophisticated domain controllers called HPCs (high-performance computers). These HPCs are more alike gaming PCs than as the traditional ECUs (electronic control units). Today’s diagnostic communication protocol, e.g., UDS (Unified Diagnostic Services, ISO 14229-1) was developed for ECUs and is not fit to be used for HPCs. There is a new protocol being developed within ASAM, SOVD (service-oriented vehicle diagnostics), which is based on a RESTful API (REpresentational State Transfer Application Programming Interface) sent over http (hypertext transfer protocol). But OBD (OnBoard Diagnostic) under the emissions regulation is not yet updated for this shift of protocols and therefore vehicle manufacturers must support older OBD protocols (e.g., SAE J1979-2) during the transition phase. Another problem is that some of the software packages may fall under the DEC-ECU (diagnostic or emission critical electronic control unit) definition and need to communicate with an OBD scan tool. This document will give an insight into SOVD and provide examples of how to support different regulatory diagnostic communication use cases for vehicles equipped with HPCs. </div
Urban stakeholders have divergent interests in the use of public space in cities and should be considered in city-planning of urban freight. This paper explores Swedish urban stakeholder's interests in the use of public space. A literature review on Urban Freight Stakeholders (UFSs) with direct impact on city-planning, and their interest’ in the use of public space was conducted and used as a theoretical foundation in a cross-case analysis of two Swedish cities. Forty-five semi-structured interviews, and forty-one answers from a multiple-choice question were used as empirical data in the evaluation of UFSs' interests. The paper shows that interests' of UFSs which contribute to attractive urban environment should be considered in city-planning of urban freight. In addition, policies on road safety, decoration of the city environment and pricing the use of public space in cities need to be developed at local authorities. The paper confirms property owners as UFSs with similar accessibility and service interests as local authorities in the city-planning of urban freight. The literature review of published research and a cross-case analysis of Swedish UFSs' interests in public space in two cities provides insights for further development of research to enrich theory and city-planning of urban freight.
Advances in methodologies for real-time analysis of batteries have come a long way, especially with the development of Operando Electrochemical Mass Spectrometry (OEMS). These approaches allow for the determination of side reactions during battery cycling with unprecedented selectivity and sensitivity, providing vital information necessary for determination of lifetime-limiting processes. However, the work thus far has primarily been carried out on model battery systems, where cell atmospheres are largely altered (through open flow, closed cell, and intermittent sampling approaches) and operation conditions are therefore not comparable with real-life situations. Herein, the development and validation of an intermittently closed OEMS system adapted for readily available commercial batteries is showcased. We provide a detailed description of a unique analysis design for large-format PHEV2 cells, with subsequent pressure and gassing data. A qualitative analysis of the results shows that side reactions brought on by structural transitions within both electrodes can be clearly observed. Transitions causing large volume changes in graphite induce H2 and C2H4 as SEI reformation products while the c lattice collapse in NMC induces CO2 evolution (through O2 release). OEMS can therefore be used for the quick and effective study of commercially available rechargeable batteries without influencing the internal battery chemistry.
In this paper, a complete operating cycle (OC) description is developed for heavy-duty vehicles traveling long distances in the region of Västra Götaland, Sweden. Variation amongst road transport missions is accounted for using a collection of stochastic models. These are parametrized from log data for all the influential road parameters that may affect the energy performance of heavy trucks, including topography, curvature, speed limits, and stop signs. The statistical properties of the developed OC description are investigated numerically by considering some composite variables, condensing the salient information about the road characteristics, and inspired by two existing classification systems. Two examples are adduced to illustrate the potential of the OC format, which enables ease of classification and detailed simulation of energy efficiency for individual vehicles, with application in vehicle design optimization and selection, production planning, and predictive maintenance. In particular, for the track used in the first example, a Volvo FH13 equipped with a diesel engine, simulation results indicate mean CO2 emissions of around 1700 g/km, with a standard deviation of 360 g/km; in the second example, dealing with electrical fleet sizing, the optimal proportion shows a predominance of tractor-semitrailer vehicles (70%) equipping 4 motors and 11 battery packs.
A control strategy is proposed to improve the high-speed lateral performance of an A-double (tractor-semitrailer-dolly-semitrailer) combination vehicle using active steering of the front axle of the dolly. The strategy is realized as a H∞-type static output feedback combined with dynamic feed-forward. The static output feedback synthesis is based on a previous work from the literature, while novel linear matrix inequality (LMI) conditions have been derived for dynamic feed-forward synthesis that can also be used for uncertain systems. The proposed controller has a simple structure and is easy to implement from a practical point of view since it requires only the driver steering angle (for feed-forward) and just one articulation angle measurement (for feedback). Obtained using a high-fidelity vehicle model, the synthesis results confirm a significant reduction in the rearward amplifications of the yaw rate and the lateral acceleration as well as the high-speed transient off-tracking during sudden lane change maneuvers.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
989 members
Andrew Lowery
  • Volvo Trucks
Mahesh Suresh Patil
  • Electromobility
Per Hanarp
  • Department of Emerging Technologies
Information
Address
Göteborg, Sweden