RNN Battery Monitoring for Nonlinear State Estimation
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Solution Overview
Problem
Current battery management systems in electric vehicles face challenges in accurately modeling and monitoring lithium-ion battery behavior due to nonlinear characteristics, particularly polarization and internal resistance, which affect state-of-charge and state-of-health estimation.
Innovation Solution
The implementation of Recurrent Neural Networks (RNNs) in battery management systems to model battery behavior, incorporating input from current, temperature, and previous time states, which captures nonlinear dynamics and temperature dependency without requiring physical parameters, and can be trained using stochastic gradient descent and backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional equivalent circuit battery models are used, then the system complexity is low and ease of manufacture is good, but the measurement precision of battery state estimation deteriorates due to inability to capture nonlinear behaviors
Solution Approach 1:
The patent replaces traditional physics-based mechanical/electrical circuit models with a data-driven neural network model. The neural network learns battery behavior patterns from training data without requiring explicit physical equations, thereby achieving high measurement precision for battery state estimation while avoiding the complexity of detailed physical modeling.
Solution Approach 2:
The patent transforms the battery modeling approach from fixed physical parameters to adaptive learned parameters. The neural network dynamically adjusts its internal parameters (weights and biases) during training to capture nonlinear battery behaviors, enabling accurate state estimation without manual parameter specification.
2Measurement precision
If physics based models are used, then the measurement precision improves by capturing nonlinear behaviors, but the device complexity increases and difficulty of detecting and measuring physical parameters worsens
Solution Approach 1:
The patent substitutes physics-based models requiring physical parameter measurement with a neural network model that learns from operational data. This eliminates the need to detect and measure difficult physical parameters like diffusion coefficients, while still capturing nonlinear behaviors through data-driven pattern recognition.
Solution Approach 2:
The neural network model performs self-training using historical battery operation data, automatically learning the nonlinear relationships without requiring external expert input for parameter specification. The system serves itself by learning from its own operational experience.
3Measurement precision
If recurrent neural networks are implemented, then the measurement precision and ability to capture temporal dynamics improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent implements a recurrent neural network with dynamic hidden states that adapt to temporal variations in battery behavior. The RNN structure allows the model to capture time-dependent dynamics such as polarization effects and temperature changes, improving measurement precision for state estimation while managing complexity through efficient recurrence.
Data Source
AI summary
Systems and methods for monitoring and controlling a battery are disclosed. Systems can include a battery having an output voltage and an output current when delivering power, a load driven by power delivered from the battery, battery output voltage and current sensing circuits, and processing circuitry coupled to the battery output voltage and current sensing circuits. The processing circuitry may implement a recurrent neural network for battery state estimation.