Improving Computational Efficiency for Energy Management Systems in Plug-in Hybrid Electric Vehicles Using Dynamic Programming Based Controllers

Reducing computational time has become a critical issue in recent years, particularly in the transportation field, where the complexity of scenarios demands lightweight controllers to run large simulations and gather results to study different behaviors. This study proposes two novel formulations of the Optimal Control Problem (OCP) for the Energy Management System of a Plug-in Hybrid Electric Vehicle (PHEV) and compares their performance with a benchmark found in the literature. Dynamic Programming was chosen as the optimization algorithm to solve the OCP in a Matlab environment, using the DynaProg toolbox. The objective is to address the optimality of the fuel economy solution and computational time. In order to improve the computational efficiency of the algorithm, an existing formulation from the literature was modified, which originally utilized three control inputs. The approach involves leveraging the unique equations that describe the Input-Split Hybrid powertrain, resulting in a reduction of control inputs firstly to two and finally to one in the proposed solutions. The aforementioned formulations are referred to as 2-Controls and a 1-Control. Virtual tests were conducted to evaluate the performance of the two formulations. The simulations were carried out in various scenarios, including urban and highway driving, to ensure the versatility of the controllers. The results demonstrate that both proposed formulations achieve a reduction in computational time compared to the benchmark. The 2-Controls formulation achieved a reduction in computational time of approximately 40 times, while the 1-Control formulation achieved a remarkable reduction of approximately 850 times. These reductions in computational time were achieved while obtaining a maximum difference in fuel economy of approximately 1.5% for the 1-Control formulation with respect to the benchmark solution. Overall, this study provides valuable insights into the development of efficient and optimal controllers for PHEVs, which can be applied to various transportation scenarios. The proposed formulations reduce computational time without sacrificing the optimality of the fuel economy solution, making them a promising approach for future research in this area

Adaptive convex loss mappings for enhanced loss assessment in asynchronous drives

Control topologies in electric drive applications commonly aim at minimizing the dissipated power in the system to guarantee energy-efficient operation. Especially in vehicle electrification, loss minimization is the main objective in the supervisory control loops as this is directly related to the range of the vehicle. Advanced drive systems are characterized by an elevated complexity but require nevertheless a real-time control strategy to be implemented. Appropriate model abstraction, enabling real-time viability with a reliable system representation, is found in convex mapping procedures of the dissipated power in the drive components. These reduced-order models are generally obtained based on model information solely. This paper proposes a methodology to recursively enhance the reliability of the convex loss approximations. An instantaneous power flow estimation is assessed based on a unification of model expectations and sensor data. Using this information, a proper adaptation to the underlying convex loss coefficients is then determined. The methodology is validated in simulation for an electric drive on three different case studies. The algorithm is furthermore applied on actual experimental data of an asynchronous drive for validation purposes. Preliminary results demonstrate that the error on the loss assessment is reduced by 55.7%-89.0%. Adaptive convex loss mappings can, therefore, be consulted in practical control structures to ameliorate the reliability of loss minimization control schemes, while still maintaining a computationally efficient format

Electric Motor and Dry Clutch Control in Launch Manoeuvres of Mild-Hybrid Vehicles Based on AMT/DCT Transmissions

Mild-Hybrid Electric Vehicles (mild-HEVs) earned market share over the last years an as effective roadmap to limit air pollution in big cities. In addition to this role, hybrid propulsion can be used to avoid dry clutch overheating in mild-HEVs equipped with automated manual transmissions. Indeed, high thermal level could result in serious damaging of dry clutch linings with very fast decay of expected lifespan affecting vehicle reliability. This paper shows results of vehicle launch simulations to highlight how the propulsion due to electric motor can effectively reduce clutch thermal stress during the slipping phase

Modelling and Co-simulation of hybrid vehicles: A thermal management perspective

Thermal management plays a vital role in the modern vehicle design and delivery. It enables the thermal analysis and optimisation of energy distribution to improve performance, increase efficiency and reduce emissions. Due to the complexity of the overall vehicle system, it is necessary to use a combination of simulation tools. Therefore, the co-simulation is at the centre of the design and analysis of electric, hybrid vehicles. For a holistic vehicle simulation to be realized, the simulation environment must support many physical domains. In this paper, a wide variety of system designs for modelling vehicle thermal performance are reviewed, providing an overview of necessary considerations for developing a cost-effective tool to evaluate fuel consumption and emissions across dynamic drive-cycles and under a range of weather conditions. The virtual models reviewed in this paper provide tools for component-level, system-level and control design, analysis, and optimisation. This paper concerns the latest techniques for an overall vehicle model development and software integration of multi-domain subsystems from a thermal management view and discusses the challenges presented for future studies

Design and energy management of aircraft hybrid electric propulsion system.

This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.PhD in Aerospac