Browse Topic: Electronic control systems
This paper proposes a nonlinear and robust State-Dependent Riccati Equation (SDRE) combined with H∞ control architecture for brake- by-wire systems, specifically designed to handle severe tire-road friction variations and μ-split scenarios. The primary objective is to maximize deceleration capabilities while rigorously maintaining yaw stability, trajectory tracking, and passenger comfort through jerk limitation. Situated within the domain of active safety, this research addresses robustness against real-world uncertainties by utilizing a high-fidelity 14-degree-of-freedom vehicle model that accounts for longitudinal, lateral, and yaw dynamics, suspension-induced pitch and roll effects, and nonlinear tire behavior with explicit load transfer. To ensure near-optimal slip tracking under variable surface conditions, the system employs online friction estimation via Extended and Unscented Kalman Filters (EKF/UKF) fusing wheel and IMU data to adaptively adjust slip targets. The control strategy is bifurcated: the SDRE component manages dominant nonlinearities through state-dependent gains to prevent wheel lock-up, while the H∞ component provides robust disturbance rejection against parametric uncertainties such as mass variations and sensor noise. Control efforts are distributed via a Quadratic Programming (QP) torque allocator featuring anti-windup mechanisms and explicit saturation handling to compensate for lateral drift during μ-split braking. Validation is conducted through a Model- in-the-Loop (MIL) to Software-in-the-Loop (SIL) pipeline using scenarios including wet surfaces and panic braking. Simulation results demonstrate enhanced yaw stability and controlled deceleration profiles compared to conventional baselines, ensuring computational feasibility for automotive Electronic Control Units (ECUs).
Designing, manufacturing, and controlling a high-precision gantry positioning system is a complex, multidisciplinary task demanding expertise in structural mechanics, thermal modeling, mechatronics, and advanced motion control.
Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines, maintaining precise trajectory tracking under severe uncertainties including mass variations, asymmetric payloads, and dynamic slung loads.
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents a systematic evaluation of control strategies (PID, LQR, MPC) for the classical control problem involving inverted pendulum on a cart under strict embedded constraints representative of software-defined vehicle ECUs. The objective is to evaluate and compare the performance of advanced control algorithms under varying control objectives when deployed on microcontrollers with constrained computational and memory resources, representative of the limitations encountered in embedded platforms used for SDVs. Furthermore, the study illustrates systematic optimization strategies that enable these algorithms to achieve real-time execution within such resource-constrained environments. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. By demonstrating how advanced control logic can be effectively deployed on constrained hardware, this work supports the broader goal of enabling intelligent, responsive vehicle behavior through software-centric design. The findings are particularly relevant for automotive and embedded engineers developing control systems for SDVs, where balancing performance and resource constraints is critical to achieving scalable, safe, and adaptive vehicle functionality.
Accurate control of the engine park angle during Autostop in hybrid vehicles is critical for enabling rapid and smooth Autostarts, reducing start-up vibrations, and enhancing overall driving comfort. However, in real-world scenarios, the available torque for engine positioning is often limited by competing driver torque demands, battery discharge constraints, and the state of charge (SoC). Under these conditions, conventional position-speed control strategies frequently fail to achieve the desired precision. This paper introduces an adaptive control strategy for the electric machine (EM) that drives the internal combustion engine, ensuring precise alignment of the crankshaft at a predefined angle to optimize restart conditions. Upon receiving an engine shutdown request, the proposed controller computes an adaptive deceleration profile that respects the EM’s torque and deceleration limits while guiding the crankshaft toward the target park position. The core of the approach lies in generating a theoretical speed trajectory and tracking it through an adaptive nonlinear control law that dynamically adjusts in real time to compensate for disturbances and eliminate residual angular error at the end of the maneuver. Unlike conventional methods, the proposed solution maintains robustness under stringent deceleration constraints and varying operating conditions. Simulation and experimental results on a hybrid powertrain test bench demonstrate that the proposed method significantly improves park angle accuracy and consistency even under limited deceleration scenarios.
Improving the energy efficiency of electrified vehicles remains a central objective in modern electric powertrains. Multi-level converters (MLCs) are widely recognised for lowering conversion losses relative to two-level inverters and improving total harmonic distortion (THD) in the sinusoidal supply to motors with a consequent reduction in motor losses. Despite this, sustained production-oriented validation at the integrated system level remains limited. This work introduces a multi-level converter architecture of the Battery Integrated Modular Multi-Level Converter (BIMMC) topology using Cascaded H-Bridge (CHB) architecture. It offers improvements in all key metrics of performance, cost, package size, mass and robustness compared to the current state-of-the-art two-level inverter system with distributed functions for charging available in the market today. The overall solution is highly functionally integrated. It supports four major functions required in electric vehicles without the need for additional hardware. Firstly, supply to and control of a three-phase electric motor without the need for a separate, standalone inverter. Secondly, all usual battery management system (BMS) functionality including energy and State of Charge (SOC) management to module level enabling usable energy and robustness improvements. Thirdly, the ability to charge from both alternating current (AC) (single phase and three-phase) and direct current (DC) sources without the need for separate on-board charger (OBC) hardware whilst also enabling an innovative pulse charging approach which benefits both charging time and battery ageing compared to conventional DC charging. Finally, the ability to deliver a controlled DC supply to non-traction loads on the vehicle with high efficiency and redundancy. The BIMMC topology proposed has been designed, built at prototype level and tested in order to collect performance data to empirically validate empirical study of the performance and functional benefits of the approach for traction motor drive, battery stored energy management and charging. Measured results demonstrate that improved inverter waveform quality correlates with lower motor harmonic losses and measurable drive-cycle efficiency gains, consistent with prior MLC assessments. Battery SOC depletion can be managed actively within the complete battery yielding increased usable energy and further driving range gains. Pulse charging shortens charge time whilst maintaining battery health metrics within acceptable limits, aligning with experimental evidence on pulse-based fast charging. The topology has also demonstrated the ability to pulse charge cells in a complete battery pack whilst consuming incoming DC supply current from a standard commercially available DC charger (Electric Vehicle Supply Equipment - EVSE). This potential to offer the benefits associated with pulse charging without requiring change to existing deployed charging infrastructure. Overall, proposed CHB-BIMMC architecture offers a practical blueprint for next-generation electric vehicles (EVs), and is compatible with ongoing integration trends that converge traction, charging and battery management functions within a unified power electronics and control platform.
In the stringent market of BEV, the development of integrated Drive Modules (iDM) fitting environmental and customer needs is mandatory. It is important to extract the best from the less. To achieve those goals, a deep insight into complex multiphysics phenomena occurring in an iDM has been achieved by accurate and validated models. This engineering methodology is applied through the development of BorgWarner products, comprising non-exhaustively iDM 180-HF, Externally Excited Synchronous Machine and Multi-Level Inverter. The paper will review the methodology development for deeper understanding involving in-house technical excellence and complemented by strategic partnerships with academic institutions and start-ups. It will present the approach of integrating advanced multiphysics models with high-quality experimental validations, specifically on loss evaluation on electrical machines and inverters. Complex models involving multiphysics such as thermal/fluid coupling or electric-magnetic-mechanical behaviors are usually difficult to optimize separately since their objectives are often contradictory. Thus, BorgWarner PDS Engineering uses tools involving close coupling to optimize iDM products. The lecture will focus on innovation and optimization which are supported by several key pillars in the scope of a Next Generation iDM development. These are based on the following strategic levers such as process and design development, material development and control strategy among others. This ensures tailoring all components at the best of their capabilities to reduce their weight and maximize their use. Finally, the results achieved by the high-fidelity model-based optimization on the selected example will be presented (e.g., impact of the cooling improvement on overall iDM performances), demonstrating the benefit of capturing the system from granular view to a helicopter view in the design phase of next generation eDrives.
Inverters are typically integrated into electric drive units for electric vehicles (EVs) to reduce packaging size and cost. However, coupled vibrations from the electric motor and gears are transmitted to the inverter, which can become a dominant noise source due to its large radiative panel. Metal panels are required for electromagnetic interference (EMI) compliance, yet these covers usually lack sufficient stiffness or damping for noise control. Adding ribs and applying damping treatments result in excessive mass, cost, and packaging challenges. A new bubble sheet panel design has been developed to enhance the structural strength and damping performance of the inverter cover while significantly reducing its mass. A thin sheet of aluminum is welded onto the cover in an optimized pattern that enhances stiffness and damping performance while accommodating packaging requirements. The welding pattern can include logos or artistic designs to improve the panel’s appearance. The metal sheets are blown apart between the welds to form a 3D structure that is three times stiffer and twice as strong as a flat monolithic sheet of the same thickness. The composite dual bubble sheets can effectively reduce structural vibration. Damping materials, such as liquid-applied sound deadener (LASD), can be injected into the bubble sheet cavities to significantly improve its damping performance. Prototype bubble sheets have been designed, analyzed, and tested for an integrated inverter used in front-wheel-drive (FWD) electric drive unit for EV applications. Analysis and modal tests show a reduction of 10 to 15 dB at panel resonances with close to 30% reduction in mass. The bubble sheet panel is installed on an integrated inverter and tested in the electric drive unit. Sound power measurements confirm a reduction of up to 10 dB in inverter panel vibration.
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