A novel adaptive energy management strategy is proposed for real time power split between fuel cells and supercapacitors in a hybrid electric vehicle in view of the fact that driving patterns greatly affect fuel economy. The driving pattern recognition (DPR) is achieved based on the features extracted from the historical velocity window with a multi-layer perceptron neural network. After the DPR has been obtained, an adaptive fuzzy energy management controller is utilized for power split according to the required power for vehicle running. In order to prolong the fuel cell lifetime whilst decreasing the hydrogen consumption, a genetic algorithm is applied to optimize critical factors such as adaptive gains and fuzzy membership function parameters for several standard driving cycles. In the proposed method, the future driving cycles are not required and the current driving pattern can be successfully recognized, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions. Compared with conventional energy management systems, the proposed framework can ensure the state of charge of supercapacitors within the desired limit.

Fuzzy neural networks (FNNs) are quite useful for nonlinear system identification when only the input/output information is available. A new FNN framework is first proposed by combining an AutoRegressive with exogenous input (ARX) with the nonlinear Tanh function in the Takagi-Sugeno (T-S) type fuzzy consequent part. An improved genetic algorithm is then designed to optimize the structure and parameters of the FNN simultaneously under unknown plant dynamics. The hybrid encoding/decoding, neighborhood search operator, and maintain operator are presented to optimize the input structure of the ARX plus the nonlinear function submodel, the number of the fuzzy rules, and the parameters of the membership function. Three benchmarks and a liquid level modeling problem in an industrial coke furnace are utilized to compare the performance of several typical methods. Simulation results show that the proposed method is superior in structure simplification, modeling precision, and generalization capability.

The combination of the fuel cell (FC) and Super-capacitor (SC) for Hybrid Electric Vehicle (HEV) has the benefit of compensating for the slow dynamic response and avoiding reactant starvation of FC. Energy management system (EMS) is critical to the HEV and a fuzzy controller plus low pass filter is proposed to prolong the FC lifetime and decrease the hydrogen consumption. The constrained bi-objective optimization problem for fuzzy EMS is then solved by an improved genetic algorithm (GA), where the decimal and rule base encoding, constraint handling, the pruning and maintain operator are designed to optimize both the fuzzy rule base and the parameters of the membership functions. Simulation results of Highway fuel economy certification test (HWFET), Urban Dynamometer Driving Schedule (UDDS) and New European Drive Cycle (NEDC) illustrate that the proposed approach can smooth the output of FC with robustness and be implemented in real time, which decreases 19% current variation with about 10% increase of H2 consumption.

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Given that transportation contributes to 23% of global energy-related greenhouse gas emissions, the electrification of the transport sector is an inevitable trend. This paper presents a hybrid energy management method that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm. To enhance its exploration capability and balance exploration and exploitation, the Locality Sensitive Hashing (LSH) algorithm and Prioritized Experience Replay (PER) algorithm are introduced. DDPG, being a deterministic strategy, typically explores the environment by adding noise to the action space, which lacks effective exploration capability. Therefore, the LSH algorithm is employed for approximate counting of state variables in high-dimensional continuous state spaces. This encourages agents to explore less frequently occurring state variables, thereby improving the algorithm’s exploration ability. Additionally, the integration of the PER algorithm enables more efficient utilization of limited sample data during experience replay, leading to improved learning efficiency and stability. In addition, periodic updates are used to compensate for the additional computational consumption caused by counting. Simulation results on driving cycles from the typical road conditions demonstrate that the proposed algorithm has stronger exploration ability and better management performance comparing with EMSs based on DDPG, DQN and Q-Learning. The LSH-PER-DDPG based EMS achieves a significant 8.5% reduction in lithium battery power consumption, a 6.25% increase in supercapacitor recovery, and a substantial 15.7% reduction in instantaneous fluctuation of lithium battery output current.