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SOC (State of charge) plays an important role in vehicle energy management, utilization of battery pack capacity, battery protection. Model based SOC estimation algorithm is widely regarded as an efficient computing method, but battery model accuracy and measuring noise variance will greatly affect the estimation result. This paper proposed an adaptive mixed estimation algorithm. In the algorithm, the recursive least squares algorithm was used to identify the battery parameters online with a second-order equivalent circuit model, and an adaptive unscented Kalman method was applied to estimate battery SOC. In order to verify the effect of the proposed algorithm, the experimental data of a lithium battery pack was applied to build a simulation model. The results show that the proposed joint algorithm has higher estimation accuracy and minimum root mean square error than other three algorithms.

As one of the core components of electric vehicles(EV), the drive motor system has a significant impact on the EV operation performance. The interior permanent magnet synchronous motor (IPMSM) has a wide range of applications in EV, due to its high efficiency, high power density, high torque current and wide speed range. In the field of EV, motor control system is required to have a high operating range. IPMSM operates at constant torque mode below rated speed and constant power mode above rated speed. The back electromotive force(Back-EMF) generated by the rotor in the constant power mode causes the inverter output voltage to saturate. Therefore, it is necessary to ensure that the controller is still operating in the linear region by applying a flux weakening(FW) current to the stator.

A control strategy for searching the optimal working interval based on multi-parameter logic threshold is proposed for the power system of distributed hybrid electric vehicles. The battery state of charge working area and boundary velocity threshold are combined with the optimal engine working curve. Offline simulation of 0-32 km/h acceleration performance is conducted. To further verify the validity of generating C code, a hardware-in-the-loop (HIL) test platform based on MATLAB/RTW/xPC target is built. Real-time simulation and real-time performance comparison test are performed. Test results show that the designed multi-parameter logic threshold control strategy achieves reasonable allocation of energy and improves the dynamic performance of vehicles. The xPC HIL simulation test system is feasible and provides a fast test verification method for vehicle control strategy development.

Battery state estimation is one of the most important decision parameters for lithium battery energy management. It plays an important role in improving battery energy utilization, ensuring battery safety and enhancing system reliability. This paper is proposed to provide a dynamic correction of SOC in the full working condition, including static condition and dynamic condition. Based on the Coulomb-counting method, the current SOC value of the battery is calculated. Under the static conditions, the open circuit voltage of the battery is used to directly collect the initial SOC. Under the dynamic working conditions, the open circuit voltage of the battery is estimated by the sliding mode observer. Based on the deviation between the calculated and estimated values of the open circuit voltage, the current coefficient of the Coulomb-counting method is dynamically corrected by PI strategy.

Battery management system (BMS) is the core component of the new energy vehicle battery system. With the increase of energy density of new energy vehicle battery, its control algorithm becomes more and more complex, and the work of the battery management system will be heavier. In order to solve the limits, the hardware, software and control strategy model of battery management system are developed based on AURIX multi-core microcontroller. The microprocessor control unit is developed by using dual-core chip, which meets the functional safety requirements. Dual-core processing of control strategy and individual information acquisition are realized, and the processing efficiency is improved. A four-tier software architecture of battery management system is developed to handle the Dual-core processing. The graphical development of battery management system strategy model is realized by using MATLAB / Simulink.

It is important to verify the requirements of battery management strategy (BMS), which is directly related to the safety of the electric vehicles. A model-in-loop test framework was established to realize the automated test based on the application scenario of BMS, including battery simulation module, battery management strategy module, test sequence module and function assessment module. The inputs of the automated test were designed in the test sequence module. The function requirements of state estimation, balance control, security protection, thermal management and fault diagnosis were designed in the function assessment module. The test results show that the proposed model-in-loop test can reach 100% of function requirements, and 87% of execution coverage. Additional automated test cases can be added to the proposed model-in-loop test, and it will be an effective method to the requirements verification of battery management strategy.

The fuel economy of plug-in hybrid electric city bus (PHEV) is deeply affected by driving cycle and travel distance. To improve the adaption of energy management strategy, the equivalent coefficient of fuel is the key parameter that needs to be pre-optimized based on the predicted driving cycle. An iterative learning method was proposed and implemented in order to get the best equivalent coefficient based on the predicted driving cycle and battery capacity. In the iterative learning method, the energy model and kinematics model of the bus were built. The ECMS (Equivalent Consumption Minimization Strategy) method was applied to obtain the best fuel economy with the given equivalent coefficient. The driving paths and running time of city buses were relatively fixed comparing with other vehicles, and their driving cycle can be predicted by route content. The proposed optimized strategy was applied on the factory sets of plug-in hybrid electric city bus.

Approximately 50% energy is consumed during the acceleration of a city bus. Fuel consumption during acceleration is significantly affected by driving behavior. In this study, 13 characteristic parameters were selected to describe driving style based on analysis of how driving influences fuel consumption during acceleration. The 100,000 km real-world vehicle running data of six drivers on three city buses in a particular bus line in Tianjin, China were sampled using a vehicle-on-line data logger. Based on the selected characteristic parameters and collected driving data, an evaluation model of the fuel consumption level of a driver was established by adopting the method of decision tree C4.5. For two-level classification, the model has over 85% prediction accuracy. The model also has the advantages of having a few training samples and strong generalization. As an example of the model application, the fuel-saving potential of a driver under optimal operations was analyzed.