Configuration design and screening of multi-mode double-planetary-gears hybrid powertrains

Hybrid powertrains with planetary gearset(PG) have been widely used. However, there are few types of powertrains in use, more powertrains have not been found. Based on the principle of organic chemistry, a design and screening method of multi-mode 2-PGs hybrid powertrain is proposed, which is divided into five stages. Firstly, powertrains are expressed in the form of molecules. Secondly, powertrains split into the libraries of PGs and power sources. The power sources can be mutually identified to construct new library. Thirdly, the mode switching rules are defined to screen power source group. Fourthly, two libraries interact with each other to promote the generation of new molecules, namely, new powertrains. And the more modes, the greater the vehicle performance potential. Powertrains are screened with mode richness theory firstly. Finally, taking the comprehensive evaluation of power performance and fuel economy as the optimal standard, powertrains are screened and evaluated twice. Through the method, hybrid powertrains with smooth mode switching, simpler structure, and optimal power and economy can be obtained.

The Integrated Kinetic Energy Recoup Drive (i-KERD): An Optimized Powertrain for EVs, HEVs and FCEVs

In this paper, a particular form of flywheel hybrid powertrain, namely, the Integrated Kinetic Energy Recoup Drive (i-KERD) is fully explored and its applications for EVs, HEVs and FCEVs in recent years to show the energy-savings and performance enhancement potential of this innovative powertrain technology. It is shown that the i-KERD is a small highspeed flywheel integrated into an e-CVT, or power-split hybrid drive. Under NEDC or WLTC, typically it can achieve some 40% energy savings and >50% gain in 0–100 kph acceleration due to effective regenerative braking mechanism of the integrated flywheel power system. In addition to its “peak-shaving” capability, the highly-efficient, long-life flywheel power on-board, is able to keep the kinetic energy of the vehicle fully recycled, rather than dissipated during braking. The i-KERD technology has also been applied to urban railway transportation (i.e., underground railway) and off-road heavy construction equipment, where regenerative braking plays a great role on energy efficiency.

Experimental Validation of Hydrogen Fuel−Cell and Battery−Based Hybrid Drive without DC−−DC for Light Scooter under Two Typical Driving Cycles

Faced with key obstacles, such as the short driving range, long charging time, and limited volume allowance of battery−−powered electric light scooters in Asian cities, the aim of this study is to present a passive fuel cell/battery hybrid system without DC−−DC to ensure a compact volume and low cost. A novel topology structure of the passive fuel cell/battery power system for the electric light scooter is proposed, and the passive power system runs only on hydrogen. The power performance and efficiency of the passive power system are evaluated by a self−developed test bench before installation into the scooters. The results of this study reveal that the characteristics of stable power output, quick response, and the average efficiency are as high as 88% during the Shanghainese urban driving cycle and 89.5% during the Chinese standard driving cycle. The results present the possibility that this passive fuel cell/battery hybrid powertrain system without DC−DC is practical for commercial scooters.

Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programming

Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Development of a Method for Evaluating the Technical Condition of a Car’s Hybrid Powertrain

The article presents the results of a study performed and substantiated based on the principles of a new method of diagnostics of technical conditions of a hybrid powertrain regardless of the structural diagram and design features of a hybrid vehicle. The presented new technology of the diagnostics of hybrid powertrains allows an objective complex assessment of their technical condition by diagnostic parameters in contrast to existing diagnostic methods. In the proposed method, a mechanism for the general standardization of diagnostic parameters has been developed as well as for determining the numerical values of the parameters of the powertrain. The control subset was used to control the learning error. As a result of debugging the system, the scatter of experimental and calculated points has decreased, which confirms the quality of debugging the tested fuzzy model. As a result of training the artificial neural network, the standard deviation of the error in the control sample was 0.012·Pk. A symmetry method of diagnostics of the technical state of a hybrid propulsion system was developed based on the concept of a neural network together with a neuro-fuzzy control with an adaptive criteria based on the method of training a neural network with reinforcement. The components of the vector functional include the criteria for control accuracy, the use of traction battery energy, and the degree of toxicity of exhaust gases. It is proposed to use the principle of symmetry of the guaranteed result and the linear inversion of the vector criterion into a supercriterion to determine the technical state of a hybrid powertrain on a set of Pareto-optimal controls under unequal conditions of optimality.

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Real Drive Truth Test of the Toyota Yaris Hybrid 2020 and Energy Analysis Comparison with the 2017 Model

This paper presents the performance analysis of a latest-generation hybrid vehicle (Toyota Yaris 2020) with a testing campaign in real road conditions and a comparison with the previous model (Toyota Yaris 2017). The study was conducted by applying the Real Drive Truth Test protocol, developed by the research group, validated and spread to other full hybrid vehicles: Toyota Prius IV (2016) and Toyota Yaris 2017 (2017). In the case of the 2020 tests, the co-presence on board—deemed unsafe in the usual ways given the ongoing pandemic—was achieved through precise and sophisticated remote control. An on-board diagnostic computer, video transmission and recording equipment guarantee the virtual co-presence of a technical control room and a driver. Thus, several engineers can follow and monitor each vehicle via a 4G modem (installed in each vehicle), analysing data, route and driver behaviour in real-time, and therefore even in the presence of a single occupant in the car under test. The utmost attention has also been paid to adopting anti-COVID behaviours and safety standards: limited personal interactions, reduced co-presence in shared rooms (especially in the control room), vehicle sanitising between different drivers, computers and technicians and video technicians working once at a time. The comparison between the two subsequent vehicle models shows a significant improvement in the performance of the new generation Yaris, both in terms of operation in ZEV (zero-emission vehicle) mode (+15.3%) and in terms of consumption (−35.1%) and overall efficiency of the hybrid powertrain (+8.2%).

Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.