Multi-parameter predictive shift schedule of automatic mechanical transmission for electric bus

<div class="section abstract"><div class="htmlview paragraph">By installing an automated mechanical transmission (AMT) on heavy-duty vehicles and developing a reasonable shift strategy, it can reduce driver fatigue and eliminate technical differences among drivers, improving vehicle performance. However, after detaching from the experience of good drivers, the current shifting strategy is limited to the vehicle state at the current moment, and cannot make predictive judgment of the road environment ahead, and problems such as cyclic shifting will occur due to insufficient power when driving on the ramp. To improve the adaptability of heavy-duty truck shift strategy to dynamic driving environments, this paper first analyzes the shortcomings of existing traditional heavy-duty truck shift strategies on slopes, and develops a comprehensive performance shift strategy incorporating slope factors. Based on this, forward-looking information is introduced to propose a predictive intelligent shift strategy that balances power and economy. The vehicle power demand is calculated based on the possible future working conditions and road information, and the rotation speed, torque and impact degree are constrained to ensure the rationality of the shift range under the premise of meeting the vehicle dynamic performance. Finally, the optimal gear sequence planning is carried out with the cumulative fuel consumption in the next 5 seconds as the economic objective and output at the corresponding time. Based on GT-Suite simulation platform, the vehicle object model and gearshift control strategy are established. Under different driving conditions and road scenes, the gearshift strategy of the real vehicle is compared with the gearshift strategy built in this paper. The simulation results show that compared with the real vehicle gearshift strategy, the predictive intelligent gearshift strategy improves the fuel consumption by about 0.2% ~ 0.78%, and has better adaptability in the dynamic driving environment; Compared with the comprehensive performance shift strategy, this strategy reduces the frequent gear shift caused by the gradient change of the comprehensive shift strategy, and the fuel consumption is reduced by about 1.22% under the actual road test condition.</div></div>

This paper proposes an on-line shift schedule optimization strategy using model predictive control for an electric vehicle with 4-speed automated manual transmission. Gauss pseudo-spectral method is used to find optimal gear shift schedule in prediction horizon and the digital implementation is conducted under the frame of model predictive control. Simulation is used to show the feasibility and effectiveness of the proposed shift schedule strategy. The overall efficiency of the powertrain system utilizing the proposed shift strategy is improved.

For a two-speed AMT pure electric commercial vehicle, the pros and cons of the shift control strategy directly determine the overall vehicle performance of the electric vehicle, such as power and economy. This article takes a certain type of commercial pure electric vehicle equipped with a two-speed automatic transmission as the research object, formulates a dynamic shift rule based on the torque map of the motor, structures an economic shift rule based on the efficiency map, and identifies road gradient and Car load quality, comprehensive consideration of the car’s loading quality, road slope, accelerator pedal opening, accelerator pedal opening change rate, vehicle speed and other parameters for optimal shift control strategy formulation. Comparing the dynamic shift strategy with the economic shift strategy, simulation analysis results show that the optimized shift control strategy can meet the requirements of dynamic performance and has the advantages of economy and energy saving.

The shift strategy of automatic transmission plays a vital role in the smoothness and economy of heavy-duty mining trucks. In this paper, an engine fuel consumption model, a 6 + 2 speed automatic transmission model, and a vehicle resistance model are built in MATLAB. Combined with the development of smart mining technology, the traditional two-parameter economic shift strategy is corrected based on vehicle load and road slope. The dynamic programming optimization algorithm is used to extract the best economical shift strategy under known working conditions to reduce the fuel consumption and the number of shifts. Finally, simulation experiments of the optimized shift strategy by dynamic programming in a typical mine work cycle are carried out. The simulation results show that the engine speed and output torque are maintained in a relatively stable and efficient working range by using the proposed shift strategy. Compared with the traditional two-parameter shift strategy, dynamic programming has advantages in reducing fuel consumption and shift numbers.

As pure electric vehicles are considered to be a major growth trend in the automotive industry, research into and development of efficient electric powertrain systems and related control technologies have become popular research topics. The growing importance and use of multi-speed transmissions in these vehicles make shift schedule design and research a crucial aspect of the powertrain systems design of pure electric vehicles. This paper provides a gear shift schedule calculation method for pure electric vehicles, which includes a dynamic shift schedule and an economic shift schedule calculation method, demonstrating how to optimize the shift points and to produce the upshift and downshift lines based on the motor efficiency map. Through the establishment of a pure electric vehicle model, simulation results show that a properly designed shift schedule can improve the working region of the motor and can refine the dynamic performance and the economic performance of the vehicle. Finally, rig testing results are demonstrated to be comparable with simulations and indicate the correctness of the method.

The shift strategy plays a pivotal role in influencing the power and economy performance of vehicles. In order to improve the adaptability of shift to dynamic driving conditions, this paper proposes a comprehensive shift strategy. This study employs the FMRLS algorithm to establish a model for identifying vehicle mass and road slope and presents a model for altitude identification based on SVM. The comprehensive shift strategy is formulated by using the NSGA-II algorithm to calculate the Pareto front of the shift speeds and determine the power and economy weights for different driving conditions based on the linear weighted sum method in an adaptive manner. The simulation results show that the energy consumption of the comprehensive shift strategy is close to the economy shift strategy under the CHTC-HT condition. Significantly, the maximum difference is 2.97% in acceleration time between the comprehensive and the power shift strategy under plateau condition. Additionally, the comprehensive shift strategy can eliminate cyclic shift on upslope and provide braking force in response to the driver intention to decelerate on downslope, improving vehicle safety, and dynamic adaptability.

Performance, fuel economy, driving experience, and safety are the most critical attributes automotive original equipment manufacturers (OEMs) focus on meeting customer requirements. OEMs are coming up with many features content to meet the customer requirements while meeting the stringent regulations regarding emissions and safety. Advanced sensors and embedded controllers are employed to meet the above needs. Performance of these algorithms can be significantly improved if we accurately predict a few important vehicle operational parameters. Vehicle mass and road slope are two crucial parameters essential for developing and optimizing control algorithms of powertrain, brake, and advanced driver assistance systems (ADAS). This study focuses on model-based road slope and vehicle mass estimation by integrating a state estimation algorithm and a longitudinal dynamics model. Drive-train torque, vehicle velocity, and vehicle design parameters are inputs for developing a longitudinal dynamics model. Initial approximate estimates of road slope and vehicle mass are given as initialization values for the state estimation algorithm, and future estimates are obtained using recursive adaptive filtering. A comparative study was performed between Extended Kalman filter (EKF) and Recursive Least Square (RLS) estimation algorithms for the convergence rate of state variables and error with respect to the actual state.

The control strategy of AMT (Automated Mechanical Transmission) is one of the most popular and heated-discussed problems in the research of automobile transmission. To realize the reasonable control on a vehicle, it is significant to meet the need of dynamics and economy, but it is more important to work out intelligence gearshift and control strategy accord with driver's operating intentions and realistic driving conditions. This paper builds up a tracing model of driver's intentions in the guidance of fuzzy control theory. The model has self-adaptation fuzzy features of tracing throttle and other parameters according to a vehicle's driving parameter and specific road status, and improves the function of fuzzy controller greatly. Furthermore, establish the shift decision-making based on ANFIS (Adaptive Network based Fuzzy Inference System) self-adaptation neural-fuzzy inference system. Design intelligent control shift strategies system of AMT according to summary driver experience, which can coordinate driver's behavior and wish. Thus, the vehicle's performances are improved greatly.

This work undertakes research problem on prediction of state of battery modules used in electric vehicle. Past studies focussed extensively on the estimation of state of charge and state of health of a single cell/battery. However, the focus on the estimation of state of entire battery module is hardly studied. During the actual operation of electric vehicle, the environmental conditions (road slope and climate) and factors such as abnormal voltage and temperature conditions causes the deviations among the battery modules from its equilibrium state. As a result, its power efficiency and the life‐cycle decreases. One of the reasons of deviation is the defects in manufacturing of a battery module. For this purpose, the evaluation and estimation of state of battery modules is a priority to have information on which module among the given ones should be discharged first. Therefore, this study proposes the methodology for the evaluation of state of battery modules based on its current and temperature. Firstly, the rules are defined based on experiments to determine the priority for the discharge of each of the six modules. Dataset of 6000 samples obtained comprises of the 12 inputs (temperature and current) and 6 outputs (on‐off state of switch). Each output represents the priority of module to be discharged. To analyze this multi‐input multi‐output dataset, the popular artificial intelligence algorithm namely, artificial neural network with three training algorithms (Levenberg, Scaled conjugate and Bayesian regularization), using a number of neurons from 2 to 20 in the hidden layer is used. It was found that the model obtained using Levenberg algorithm performs the best.

Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles

Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning

This work explores converting a conventional 1996 Honda Civic Coupe into a battery-electric vehicle (BEV). A simulation model is developed to analyze the vehicle’s dynamic behavior under various conditions, including changes in vehicle mass and road slope. The system specifications are detailed based on a 50 kW AC induction motor, controller, battery, and battery management system. The battery comprises 2,376 NCR18650B cells with a capacity of 23 kWh based on inverter requirements. Energy consumption is simulated using the New European Driving Cycle (NEDC), resulting in 1.69 kWh to travel 10.8 km. Autonomy is estimated at 147 km. The environmental impact of 19.2 g CO2/km is calculated. The results illustrate the change in maximum speed, acceleration, and distance traveled based on changes in mass and slope. It is concluded that mass variation has a lesser impact on performance than road slope. The work demonstrates the feasibility of converting a conventional vehicle to a BEV with comparable dynamic capabilities using currently available technology. Future development plans include constructing a battery prototype, electrifying the vehicle, and incorporating instrumentation for validation.

Road gradient and vehicle mass are estimated using a method with variable forgetting factor, which has strong adaptability and following capability. A control strategy with hierarchical correction is proposed for a vehicle that shifts on a slope. The shifting process is composed of the upper identification decision and lower shift correction executive layers. The upper layer focuses on collecting vehicle parameters, estimating road gradient and vehicle mass, and presenting a modified control decision. The lower layer performs the slope shift schedule from the upper layer. Results of the simulation test are shown. Furthermore, the designed strategy can duly downshift the gear uphill and avoid frequent shifting, thereby reducing the wear of the transmission parts and downshifting the gear timely downhill to utilise the torque characteristics of engine braking and reduce the wear of the braking system.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

Utility companies seek to increase energy efficiency and productivity and try to reduce peak loads. This often involves consumer-side demand management in residential areas using dynamic time-of-use (ToU) tariff. Such strategies work if the consumer-side response is at least partly automated using some real-time optimization strategy. Our paper proposes a consumer-side optimization and control framework for scheduling the electric appliances in a smart household and preserving a thermal comfort level through an electric heating system. Our framework consists of two optimization components interacting with each other. The first optimization component schedules the home appliances based on a mixed integer programming approach. An electric vehicle (EV) is considered as a special home appliance with an energy storage capability. The second optimization component is the model predictive control (MPC) strategy for the electric heating system, such that the input constraints are defined by the scheduling results of the first component. Due to outside temperature variations, the input constraints may impede the MPC to maintain the required thermal comfort, which triggers a rescheduling event for the first component. The efficiency of the framework is presented in multiple simulations for scenarios with different consumer behaviors.