Design and verification of novel powertrain management for multi-geared battery electric vehicles

University of Technology Sydney. Faculty of Engineering and Information Technology.Despite the long-term benefit of battery electric vehicles (BEVs) to customers and the environment, the initial cost and limited driving range present the significant barriers for wide spread commercialization. The integration of multi-speed transmission to BEVs’ powertrain systems, which is in place of fixed ratio reduction transmission, is considered as a feasible method to improve powertrain efficiency and extend limited driving range for a fixed battery size. Additionally, regenerative braking also extends the mileage by recapturing the vehicle’s kinetic energy during braking, rather than dissipating it as heat. Both of these two methods reduce the requirement of battery pack capacity of BEVs without loss of performance. However, the motor-supplied braking torque is applied to the wheels in an entirely different way compared to the hydraulic friction braking systems. Drag torque and response delay may be introduced by transmitting the braking torque from the motor through a multi-speed transmission, axles and differential to the wheels. Furthermore, because the motor is usually only connected to one axle and the available torque is limited, the traditional friction brake is still necessary for supplementary braking, creating a blended braking system. Complicated effects such as wheel slip and locking, vehicle body bounce and braking distance variation, will inevitability impact on the performance and safety of braking. The aim of this thesis is to estimate if the multi-speed transmission and the mechanic-electric blended braking system are worthwhile for the customers, in terms of the price/performance relationship of others’ design solutions; To do so a generic battery electric vehicle is modelled in Matlab/Simulink® to predict motor efficiency, braking performance of different strategies, energy consumption and recovery for single reduction, two-speed Dual Clutch Transmission (DCT) and simplified Continuous Variable Transmission (CVT) equipped BEVs. Braking strategies for different purposes are proposed to achieve a balance between braking performance, driving comfort and energy recovery rate. Special measures are taken to avoid any effects of motor failure. All strategies are analysed in detail for various braking events. Advanced driver assistance systems (ADAS), such as Anti-Lock Brake System (ABS) and Electro Control Brake Distribution (EBD), are properly integrated to work harmoniously with the regenerative braking system (RBS). Different switching plans during braking are discussed. The braking energy recovery rates and brake force distribution details for different driving cycles are simulated. A credible conclusion is gained, through experimental validation of single speed and two-speed DCT scenarios and reasonable assumptions to support the CVT scenario, that both two-speed DCT and simplified CVT improve the overall powertrain efficiency, save battery energy and reduce customer costs, although each of the configurations has unique cost and energy consumption related trade-offs. Results for two of the cycles in an ‘Eco’ mode are measured on a drive train testing rig and found to agree with the simulated results to within approximately 10%. Reliable conclusions can thus be gained on the economic and dynamic braking performance. The strategies proposed in this thesis are shown to not only achieve comfortable and safe driving during all conditions but also to significantly reduce cost in both the short and long terms

Dynamic Modeling, Simulation, and Optimization of Vehicle Electronic Stability Program Algorithm Based on Back Propagation Neural Network and PID Algorithm

The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system

The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle

Considering the limited driving range and inconvenient energy replenishment way of battery electric vehicle, fuel cell electric vehicles (FC EVs) are taken as a promising way to meet the requirements for long-distance low-carbon driving. However, due to the limitation of FC power ability, a battery is usually adopted as the supplement power source to fill the gap between the requirement of driving and the serviceability of FC. In consequence, energy management is essential and crucial to an efficient power flow to the wheel. In this paper, a self-optimizing power matching strategy is proposed, considering the energy efficiency and battery degradation, via implementing a deep deterministic policy gradient. Based on the proposed strategy, less energy consumption and longer FC and battery life can be expected in FC EV powertrain with optimal hybridization degree

Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles

In this paper, we propose an energy management strategy based on deep reinforcement learning for a hybrid battery system in electric vehicles consisting of a high-energy and a high-power battery pack. The energy management strategy of the hybrid battery system was developed based on the electrical and thermal characterization of the battery cells, aiming at minimizing the energy loss and increasing both the electrical and thermal safety level of the whole system. Primarily, we designed a novel reward term to explore the optimal operating range of the high-power pack without imposing a rigid constraint of state of charge. Furthermore, various load profiles were randomly combined to train the deep Q-learning model, which avoided the overfitting problem. The training and validation results showed both the effectiveness and reliability of the proposed strategy in loss reduction and safety enhancement. The proposed energy management strategy has demonstrated its superiority over the reinforcement learning-based methods in both computation time and energy loss reduction of the hybrid battery system, highlighting the use of such an approach in future energy management systems